CN109031291B - Method for evaluating SAR signal detection subsurface target capability - Google Patents

Abstract

The invention discloses a method for evaluating the ability of a SAR signal to detect a subsurface target, comprising: acquiring environmental parameters of a target to be detected and observation parameters of a radar; constructing a data model; dividing a simulated detection area into a plurality of surface elements; The position of the corresponding virtual surface element; the position of the oblique height surface element is obtained; the initial incident wave is input into the corresponding model to obtain the echo signal; the echo signals of each surface element are spliced to obtain the SAR image corresponding to the simulated detection area sequence; invert the SAR image sequence to obtain the actual position of the target to be detected. The present invention can perform simulated detection of the SAR signal according to the environment where the target to be detected is located, so as to evaluate the ability of the SAR signal to detect the target to be detected.

Description

A method for evaluating the ability of SAR signals to detect subsurface targets

technical field

The invention relates to the technical field of synthetic aperture radar tomography, and more particularly, to a method for evaluating the ability of SAR signals to detect subsurface targets.

Background technique

In the field of cultural heritage archaeology, searching and detecting subsurface archaeological objects is a difficult task. The relics left by the ancients are affected by geological changes and human activities, and are generally not directly exposed on the surface, so many of them become unknown relics. To search and detect unknown remains on the subsurface has brought some difficulties to modern cultural heritage archaeology. First, the target is not exposed on the surface and cannot be directly searched by human vision alone. Second, the range of uncertainty about the target is huge, often making the search impossible. Again, traditional fieldwork search methods are costly and risky to those involved. Finally, traditional search and detection methods can also cause damage to the ruins themselves.

Therefore, the use of synthetic aperture radar tomography technology is an urgent technology to be developed for target detection in the archaeological field. TomoSAR technology can obtain the height information of the target and the distribution of the backscattering coefficient in the height direction, and the radar wave is not suitable for shallow dry and loose. The surface has a certain penetrating ability, so it can be used in the field of cultural heritage archaeology to perform tomography on shallow surface relics to obtain the shape, electromagnetic scattering characteristics and burial depth of the target relics, and then plan for the next archaeology. Provide evidence. But so far, TomoSAR technology is mostly used in forest remote sensing, glacier remote sensing and other fields. There is no accurate conclusion about the detection effect of TomoSAR technology applied to subsurface targets. Therefore, there is an urgent need to invent a method for evaluating the ability of SAR signals to detect subsurface targets.

SUMMARY OF THE INVENTION

In view of this, the present invention provides a method for evaluating the ability of a SAR signal to detect subsurface targets, which solves the problems of high cost and difficulty in detecting subsurface targets in the prior art.

In order to solve the above problems, the present invention proposes a method for evaluating the ability of SAR signals to detect subsurface targets, including the following steps:

Obtain environmental parameters of the environment where the subsurface target to be detected is located, wherein the film layer where the subsurface target to be detected is located is a relic layer, and there is a sand layer between the relic layer and the air layer, and the environmental parameters include the Physical parameters of the sand layer;

Determine the observation parameters of the radar corresponding to the simulated SAR signal, wherein the propagation speed of the electromagnetic wave of the radar in the air layer, the propagation speed of the electromagnetic wave in the sand layer, the incident angle of the electromagnetic wave emitted by the radar, the wavelength of the initial incident wave and said electromagnetic wave;

Build a data model of the simulated detection target, wherein the data model of the simulated detection target includes a simulated detection area and the depth of the simulated detection target, and the simulated detection area includes a special area with the simulated detection target and the special area with the simulated detection target. ordinary areas outside the area;

dividing the simulated detection area into a plurality of surfels, wherein the plurality of surfels include special surfels located in the special area and common surfels located in the general area;

According to the propagation speed of the electromagnetic wave in the sand layer and the depth of the simulated detection target, the time when the electromagnetic wave actually propagates to the simulated detection target is obtained as the first time;

According to the propagation speed of the electromagnetic wave in the air layer, the incident angle, the physical parameters of the sand layer, the position of the special surface element and the first time, the corresponding value of the special surface element is obtained. the position of the virtual surfel;

The position of the oblique height surface element corresponding to the special surface element is obtained according to the position of the virtual surface element, wherein the position of the oblique height surface element is the distance between the virtual surface element and the sand layer in the oblique height direction. the intersection of the upper surface;

Calculate the distance _{RT_N} from the virtual surface element to the radar;

Calculate the distance R _{S_N} from the sloping surface element to the radar;

Inputting the initial incident wave to the signal backscattering model to obtain the signal E _{S1_N} ;

The signal E _{T1_N} is obtained by calculating the initial incident wave through the signal refraction model, the signal penetration attenuation model, the signal backscattering model, the signal penetration attenuation model and the signal refraction model in sequence;

The following formula is used to calculate the radar echo signal E _{1_N} corresponding to the special surface element:

E _{1_N} =E _{S2_N} *exp(-j4πR _{S_N} /λ)+E _{T2_N} *exp(-j4πR _{T_N} /λ);

The following formula is used to calculate the radar echo signal E _{1_N} ' corresponding to the common surface element:

E _{1_N} '=E _{S2_N} *exp(-j4πR _{S_N} /λ)

in,

E _{S2_N} =Γ(b _N ,E _{RS2_N} ), E _{RS2_N} =E _{S1_N} *(randn ₁ +i*randn ₂ )

E _{T2_N} = Γ(b _N , E _{RT2_N} ), E _{RT2_N} = E _{T1_N} *(randn ₁ +i*randn ₂ )

Г is the spatial decoherence model function, b _N is the spatial baseline of the radar, N is a natural number, indicating that the radar has performed N multiple baseline observations, randn ₁ , randn ₂ are all subject to N(0,1) distribution random variable, λ is the wavelength of the electromagnetic wave;

splicing the radar echo signals corresponding to each of the special surface element and the common surface element according to the positions of the special surface element and the common surface element to obtain a SAR image sequence corresponding to the simulated detection area;

The multi-view TomoSAR inversion algorithm is used for the SAR image sequence to obtain the position and signal strength of the virtual bin and the oblique height bin corresponding to each of the special bins, as well as the oblique height corresponding to each ordinary bin. The location and signal strength of the bin;

converting the position of the virtual surfel corresponding to each of the special surfels to the position of the special surfel;

Obtain the three-dimensional data of the simulated detection target and the surface of the sand layer according to the positions of all the special surface elements and the positions of the oblique height surface elements corresponding to the special surface elements;

According to the comparison result of the three-dimensional data of the simulated detection target and the surface of the sand layer and the data model of the simulated detection target, the ability of the SAR signal to detect the subsurface target to be detected is determined.

Further, after obtaining the radar echo signals corresponding to all the bins, the radar echo signals corresponding to each of the special bins and the ordinary bins are processed according to the positions of the special bins and the common bins. Before splicing, the method further includes:

adding random thermal noise to the radar echo signal E _{1_N} corresponding to the special surface element to obtain the final echo signal E _{2_N} corresponding to the special surface element;

Add random thermal noise to the radar echo signal E _{1_N} ' corresponding to the common surface element to obtain the final echo signal E _{2_N} ' corresponding to the common surface element,

Wherein, the step of splicing the radar echo signals corresponding to each of the special panel and the common panel according to the positions of the special panel and the normal panel is specifically: combining each of the special panel and the normal panel. The final echo signal corresponding to the common bin is spliced according to the positions of the special bin and the common bin.

Further, the signal backscattering model is:

r_H为H极化分量的反射系数，n₁为入射波所在介质层的折射率，n₂为折射波所在介质层的折射率，θ_i、θ_t分别为入射角和折射角，

为非相干散射信号比例模型函数，E_a为所述信号后向散射模型的输入，E_b为所述信号后向散射模型的输出。in,

r _H is the reflection coefficient of the H polarization component, n ₁ is the refractive index of the medium layer where the incident wave is located, n ₂ is the refractive index of the medium layer where the refracted wave is located, θ _i and θ _t are the incident angle and the refraction angle, respectively,

is the scale model function of the incoherent scattering signal, E _a is the input of the signal backscattering model, and _Eb is the output of the signal backscattering model.

Further, the signal refraction model is:

t_H为H极化分量的透射系数，n₁为入射波所在介质层的折射率，n₂为折射波所在介质层的折射率，θ_i、θ_t分别为入射角和折射角，

为相干散射信号比例模型函数，E_c为所述信号折射模型的输入，E_d为所述信号折射模型的输出。in,

t _H is the transmission coefficient of the H polarization component, n ₁ is the refractive index of the medium layer where the incident wave is located, n ₂ is the refractive index of the medium layer where the refracted wave is located, θ _i and θ _t are the incident angle and the refraction angle, respectively,

is the coherent scattering signal scale model function, E _c is the input of the signal refraction model, and E _d is the output of the signal refraction model.

Further, the environmental parameter includes the dielectric constant of the electromagnetic wave in the sand layer, and the following formula is used to calculate the refractive index of the electromagnetic wave in the sand layer:

其中，Re表示取实部操作符，ε为所述砂层的介电常数。

Among them, Re represents the operator of taking the real part, and ε is the dielectric constant of the sand layer.

Further, the signal penetration attenuation model is:

Wherein, E _g is the output of the signal penetration attenuation model, E _f is the input of the signal penetration attenuation model, and _ke is the extinction coefficient.

Further, the step of using the multi-view TomoSAR inversion algorithm for the SAR image sequence to obtain the position of the virtual surfel corresponding to each of the special surfels includes:

performing de-skew processing on the SAR image sequence to obtain a de-skewed SAR image sequence;

The tomographic inversion of the de-oblique SAR image sequence, the formula is as follows:

γ'(s) _B = a ^H (s) Ra (s);

s为所述虚拟面元的斜高，r为所述虚拟面元的斜距，b_N为所述空间基线，λ为所述电磁波的波长，n＝0,1,2…N；Among them, a(s)=[exp(j2πξ ₀ s),…exp(j2πξ _N s)] ^T , R is the coherence matrix,

s is the slope height of the virtual bin, r is the slope distance of the virtual bin, b _N is the spatial baseline, λ is the wavelength of the electromagnetic wave, n=0, 1, 2...N;

The coherence matrix R is:

为对d_m求共轭操作，<·>_L为取平均操作；Among them, dn is the complex signal value of the SAR image sequence obtained by the _nth multi-baseline observation, dm is the complex signal value of the SAR image sequence obtained by the _mth multi-baseline observation,

For the conjugate operation of d _m , <·> _L is the average operation;

Calculate the first two maximum values of γ'(s) _B to obtain the positions of the slanted height bin and the virtual bin.

Further, the step of converting the virtual surfel corresponding to each special surfel to the position of the special surfel includes:

According to the geometric relationship between the special surface element and the corresponding virtual surface element, the position of the virtual surface element is converted to the special surface element, and the oblique height of the special surface element is obtained;

Convert the oblique height of the special surface element to the vertical height to obtain the position of the special surface element.

Compared with the prior art, the method for evaluating the ability of SAR signals to detect subsurface targets provided by the present invention at least achieves the following beneficial effects:

The method for evaluating the ability of a SAR signal to detect a subsurface target provided by the present invention firstly simulates the detection target and the SAR signal when the detection target is located in the environment where the target to be detected is located, and calculates the simulated detection target through the simulated SAR signal. SAR image sequence, and finally obtain the 3D image of the simulated detection target through tomographic inversion, and then compare the calculated 3D image with the simulated detection target to evaluate the SAR signal in the environment where the target to be detected is located. The ability to detect subsurface targets can evaluate the detection ability of SAR signals in the specific environment where the target to be detected is located, so as to guide the selection of signals when actually detecting the target to be detected, and to help researchers detect subsurface targets in a specific environment. It provides a strong reference for signal selection.

Other features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention.

1 is a flow chart of a method for evaluating the ability of a SAR signal to detect subsurface targets provided by Embodiment 1;

Figure 2 is a schematic diagram of the constructed data model;

3 is a flow chart of the method for evaluating the ability of SAR signals to detect subsurface targets provided by Embodiment 2;

FIG. 4 is a flowchart of a method for evaluating the ability of a SAR signal to detect subsurface targets provided in Embodiment 3. FIG.

Detailed ways

Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the invention unless specifically stated otherwise.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification.

In all examples shown and discussed herein, any specific values should be construed as illustrative only and not limiting. Accordingly, other instances of the exemplary embodiment may have different values.

It should be noted that like numerals and letters refer to like items in the following figures, so once an item is defined in one figure, it does not require further discussion in subsequent figures.

Example 1:

This embodiment provides a method for evaluating the ability of SAR signals to detect subsurface targets. FIG. 1 is a flowchart of the method for evaluating the ability of SAR signals to detect subsurface targets provided in Embodiment 1. As shown in FIG. 1 , the method includes the following steps :

S101: Obtain environmental parameters of the environment where the subsurface target to be detected is located;

Among them, the subsurface target to be detected is the target buried under the surface. Usually, when the subsurface target to be detected is located in a desert area, it is more difficult to detect the subsurface target to be detected. If different radar signals are used for actual detection multiple times In comparison with experiments, the economic cost and detection period are relatively large. Therefore, the method provided in this embodiment is mainly aimed at evaluating the detection capability of the SAR signal when the subsurface target to be detected is located in a desert area.

For desert areas, the subsurface targets to be detected are usually buried in sand layers. Therefore, in the present invention, the medium between the air and the subsurface targets to be detected is defined as sand layers, and the subsurface targets to be detected are relic layers. In this step, the environmental parameters of the environment where the subsurface target to be detected is located mainly refer to the physical parameters of the sand layer. The environment where the subsurface target is located is different, and its environmental parameters are correspondingly different. In this embodiment, for a subsurface target to be detected , before performing detection, first obtain the environmental parameters of the environment where it is located, and then evaluate the detection capability of the SAR signal by the method provided in this embodiment.

S102: Determine the observation parameters of the radar corresponding to the simulated SAR signal;

Since the observation parameters corresponding to different radars are different, before implementing this method, a radar should be selected as the radar that generates the simulated SAR signal, and the observation parameters of the radar are the observation parameters of the radar corresponding to the simulated SAR signal.

The observation parameters include the propagation speed of the electromagnetic wave of the radar in the air layer, the propagation speed of the electromagnetic wave in the sand layer, the incident angle of the electromagnetic wave emitted by the radar, the initial incident wave and the wavelength of the electromagnetic wave; wherein the propagation speed of the electromagnetic wave of the radar in the air is a fixed value, about 3×10 ⁸ m/s; for the propagation speed of electromagnetic waves in the sand layer, the sand layer in the area where the subsurface target to be detected is located can be sampled, and the propagation of electromagnetic waves in the sand layer can be obtained through experiments speed. The incident angle of the electromagnetic wave emitted by the radar, the initial incident wave and the wavelength of the electromagnetic wave can be derived from the parameters of the radar.

When selecting the radar and determining the radar observation parameters, the observation parameters of the radar corresponding to each SAR signal can be obtained separately, and then each SAR signal is used to simulate the detection of the target to be detected, and finally the results are selected from the various SAR signals. optimal signal.

S103: Build a data model for simulating the detection target;

The data model of the simulated detection target includes the simulated detection area and the depth of the simulated detection target, and the depth of the simulated detection target can be artificially set according to the situation of the simulated detection area. The simulated detection area includes a special area with simulated detection targets and an ordinary area other than the special area, and the ordinary area and the special area constitute a simulated detection area.

S104: Divide the simulated detection area into a plurality of surface elements;

The simulated detection area includes a common area and a special area. Therefore, the divided multiple bins include a special bin located in the special area and a common bin located in the common area. For example, the simulated detection area is divided into several Each rectangle represents a surfel, the rectangle in the special area is the special surfel, and the rectangle in the normal area is the ordinary surfel.

S105: Obtain the time when the electromagnetic wave actually propagates to the simulated detection target according to the propagation speed of the electromagnetic wave in the sand layer and the depth of the simulated detection target, as the first time;

Since the depth of the simulated detection target is known, and the incident angle and refraction angle of the electromagnetic wave in the sand layer are known, the straight-line distance from the intersection of the electromagnetic wave and the upper surface of the sand layer to the actual position of the simulated detection target can be obtained through trigonometric functions, and then use the The first time can be obtained by dividing the straight-line distance by the propagation speed of the electromagnetic wave in the sand layer.

S106: According to the propagation speed of the electromagnetic wave in the air layer, the incident angle, the physical parameters of the sand layer, the position of the special surface element, and the first time, obtain the location of the special surface element. The position of the corresponding virtual surfel;

The position of the virtual surface element represents the position of the virtual surface element that the electromagnetic wave reaches after the first time, assuming that the electromagnetic wave does not refract when entering the sand layer from the air layer.

S107: Obtain the position of the slant height bin corresponding to the special bin according to the position of the virtual bin;

Wherein, the position of the oblique height surface element is the intersection of the virtual surface element in the oblique height direction and the upper surface of the sand layer; the oblique height direction is the difference between the virtual surface element and the incident point of the electromagnetic wave entering the sand layer. A vertical line is made at the virtual surface element, and the intersection between the vertical line and the upper surface of the sand layer is the slanted height surface element, and the slanted height surface element is also the ground surface element.

S108: Calculate the distance _{RT_N} from the virtual surface element to the radar;

The distance of R _{T_N} can be calculated by geometric relationship according to the flying height and incident angle of the radar.

S109: Calculate the distance R _{S_N} from the sloping surface element to the radar;

The distance of R _{S_N} can be calculated by geometric relationship according to the flying height and incident angle of the radar.

S110: Input the initial incident wave into the signal backscattering model to obtain the signal E _{S1_N} ;

S111: Calculate the signal E _{T1_N} by successively passing the initial incident wave through the signal refraction model, the signal penetration attenuation model, the signal backscattering model, the signal penetration attenuation model and the signal refraction model;

Among them, Figure 2 is a schematic diagram of the constructed data model. As shown in Figure 2, when the electromagnetic wave enters the sand layer from the air layer, it will refract and change its direction, and the electric field intensity of the refracted electromagnetic wave will be attenuated, so the signal is refracted first. The model can calculate the electric field strength of the initial incident wave after refraction, as shown in Figure 2, the incident angle is θ _t , and the refraction angle is surrounded by θ _i ; the refracted electromagnetic wave goes from the incident point to the target to be detected, that is, the special surface element When T propagates through the sand layer, the electric field strength of the electromagnetic wave will be attenuated. Input the electric field strength of the refracted electromagnetic wave into the preset signal penetration attenuation model to obtain the electric field strength of the electromagnetic wave at the target to be detected. value. In the same way, refraction scattering and energy attenuation will also occur when the electromagnetic wave is scattered back to the radar along the above incident path. Therefore, the electric field strength value of the electromagnetic wave at the target to be detected is input into the preset signal penetration attenuation model and signal refraction model in turn. Signal E _{T1_N is available} .

S112: Use the following formula to calculate the radar echo signal E _{1_N} corresponding to the special surface element:

E _{1_N} =E _{S2_N} *exp(-j4πR _{S_N} /λ)+E _{T2_N} *exp(-j4πR _{T_N} /λ);

S113: Use the following formula to calculate the radar echo signal E _{1_N} ' corresponding to the common surface element:

E _{1_N} '=E _{S2_N} *exp(-j4πR _{S_N} /λ)

in,

E _{S2_N} =Γ(b _N ,E _{RS2_N} ), E _{RS2_N} =E _{S1_N} *(randn ₁ +i*randn ₂ )

E _{T2_N} = Γ(b _N , E _{RT2_N} ), E _{RT2_N} = E _{T1_N} *(randn ₁ +i*randn ₂ )

Г is the spatial decoherence model function, b _N is the spatial baseline of the radar, N is a natural number, indicating that the radar has performed N multiple baseline observations, randn ₁ , randn ₂ are all subject to N(0,1) distribution random variable, λ is the wavelength of the electromagnetic wave;

For the radar echo signal corresponding to the special surface element, it is necessary to input the electromagnetic wave backscattered by the incident wave on the surface of the sand layer and the electromagnetic wave that penetrates the surface of the sand layer and returns to the radar after refraction and energy attenuation into the preset Ruili fading model and space. After the decoherence model is decohered, the coherent sum can be obtained to obtain the echo signal of the simulated special surface element. For the radar echo signal corresponding to the common panel, you only need to input the electromagnetic wave backscattered by the incident wave into the preset Rayleigh fading model and spatial decoherence model, and then the simulated echo signal of the normal panel can be obtained.

S114: splicing the radar echo signals corresponding to each of the special bins and the common bins according to the positions of the special bins and the common bins to obtain a SAR image sequence corresponding to the simulated detection area;

Since the analog detection area has been divided into multiple bins in step 104, after obtaining the analog echo signals of each special bin and common bin, splicing is performed according to the position of each special bin and common bin, The SAR image sequence corresponding to the simulated detection area can be obtained.

S115: Use the multi-view TomoSAR inversion algorithm on the SAR image sequence to obtain the position and signal strength of the virtual bin and the slanted bin corresponding to each special bin, and the corresponding The position and signal strength of the slanted high surface element;

This method TomoSAR inversion algorithm can invert the SAR image sequence to obtain the position of each virtual bin.

S116: Convert the position of the virtual surfel corresponding to each of the special surfels to the position of the special surfel;

This step is used to obtain the position of the special surface element corresponding to each virtual surface element through the geometric relationship, that is, to obtain the position of each surface element on the target to be detected.

S117: Obtain the three-dimensional data of the simulated detection target and the surface of the sand layer according to the positions of all the special surface elements and the positions of the oblique height surface elements corresponding to the special surface elements.

S118: Determine the ability of the SAR signal to detect the subsurface target to be detected according to the comparison result of the simulated detection target and the three-dimensional data on the surface of the sand layer and the data model of the simulated detection target.

In this embodiment, a detection target is simulated for the environmental parameters of a specific area to be detected and the observation parameters of the radar, and by simulating the refraction, reflection and signal attenuation of the radar electromagnetic wave on the sand layer and the simulated detection target, The finally obtained simulated echo signal sequence, that is, the SAR signal for detecting the detection target is simulated, and then the simulated SAR signal for detecting the detection target is inverted into a three-dimensional image through the multi-view TomoSAR inversion algorithm. Comparing the 3D image generated by the calculation with the simulated detection target, the ability of the SAR signal to detect the subsurface target to be detected can be evaluated, and the detection ability of the SAR signal in the specific environment of the target to be detected can be evaluated to guide the actual situation. The selection of signals when detecting targets to be detected provides a strong reference for researchers to select signals when detecting subsurface targets in specific environments.

Example 2:

This embodiment provides a method for evaluating the ability of SAR signals to detect subsurface targets. FIG. 3 is a flowchart of the method for evaluating the ability of SAR signals to detect subsurface targets provided in Embodiment 2. As shown in FIG. 3 , the method includes the following steps :

S201: Obtain environmental parameters of the environment where the subsurface target to be detected is located;

Among them, the subsurface target to be detected is the target buried under the surface. Usually, when the subsurface target to be detected is located in a desert area, it is more difficult to detect the subsurface target to be detected. If different radar signals are used for actual detection multiple times In comparison with experiments, the economic cost and detection period are relatively large. Therefore, the method provided in this embodiment is mainly aimed at evaluating the detection capability of the SAR signal when the subsurface target to be detected is located in a desert area.

For desert areas, the subsurface targets to be detected are usually buried in sand layers. Therefore, in the present invention, the medium between the air and the subsurface targets to be detected is defined as sand layers, and the subsurface targets to be detected are relic layers. In this step, the environmental parameters of the environment where the subsurface target to be detected is located mainly refer to the physical parameters of the sand layer. The environment where the subsurface target is located is different, and its environmental parameters are correspondingly different. In this embodiment, for a subsurface target to be detected , before performing detection, first obtain the environmental parameters of the environment where it is located, and then evaluate the detection capability of the SAR signal by the method provided in this embodiment.

Optionally, the physical parameters of the sand layer include the complex dielectric constant of the sand layer. After obtaining the complex dielectric constant of the sand layer, the refractive index of the sand layer can be obtained by the following formula:

其中，Re表示取实部操作符，ε为所述砂层的复电介质常数，由于复电介质常数为砂层的固有属性，能够反应出待探测次地表目标所在的环境情况。

Among them, Re represents the operator of taking the real part, and ε is the complex dielectric constant of the sand layer. Since the complex dielectric constant is an inherent property of the sand layer, it can reflect the environmental conditions where the subsurface targets to be detected are located.

S202: Determine the observation parameters of the radar corresponding to the simulated SAR signal;

Since the observation parameters corresponding to different radars are different, before implementing this method, a radar should be selected as the radar that generates the simulated SAR signal, and the observation parameters of the radar are the observation parameters of the radar corresponding to the simulated SAR signal.

The observation parameters include the propagation speed of the electromagnetic wave of the radar in the air layer, the propagation speed of the electromagnetic wave in the sand layer, the incident angle of the electromagnetic wave emitted by the radar, the initial incident wave and the wavelength of the electromagnetic wave; wherein the propagation speed of the electromagnetic wave of the radar in the air is a fixed value, about 3×10 ⁸ m/s; for the propagation speed of electromagnetic waves in the sand layer, the sand layer in the area where the subsurface target to be detected is located can be sampled, and the propagation of electromagnetic waves in the sand layer can be obtained through experiments speed. The incident angle of the electromagnetic wave emitted by the radar, the initial incident wave and the wavelength of the electromagnetic wave can be derived from the parameters of the radar.

When selecting the radar and determining the radar observation parameters, the observation parameters of the radar corresponding to each SAR signal can be obtained separately, and then each SAR signal is used to simulate the detection of the target to be detected, and finally the results are selected from the various SAR signals. optimal signal.

S203: Build a data model for simulating the detection target;

The data model of the simulated detection target includes the simulated detection area and the depth of the simulated detection target, and the depth of the simulated detection target can be artificially set according to the situation of the simulated detection area. The simulated detection area includes the special area with the simulated detection target and the general area other than the special area. The simulated detection area is the area containing the simulated detection target, and the part containing the simulated detection target is the special area, and the area that does not contain the simulated detection target It is an ordinary area, and the union of the ordinary area and the special area constitutes a simulated detection area.

S204: Divide the simulated detection area into a plurality of surface elements;

The simulated detection area includes a common area and a special area. Therefore, the divided multiple bins include a special bin located in the special area and a common bin located in the common area; that is, the simulated detection area is divided into several Each rectangle represents a surfel, the rectangle in the special area is the special surfel, and the rectangle in the normal area is the ordinary surfel.

S205: Obtain the time when the electromagnetic wave actually propagates to the simulated detection target according to the propagation speed of the electromagnetic wave in the sand layer and the depth of the simulated detection target, as the first time;

Since the depth of the simulated detection target is known, and the incident angle and refraction angle of the electromagnetic wave in the sand layer are known, the straight-line distance from the intersection of the electromagnetic wave and the upper surface of the sand layer to the actual position of the simulated detection target can be obtained through trigonometric functions, and then use the The first time can be obtained by dividing the straight-line distance by the propagation speed of the electromagnetic wave in the sand layer.

S206: According to the propagation speed of the electromagnetic wave in the air layer, the incident angle, the physical parameters of the sand layer, the position of the special surface element, and the first time, obtain the location of the special surface element. The position of the corresponding virtual surfel;

The position of the virtual surface element represents the position of the virtual surface element that the electromagnetic wave reaches after the first time, assuming that the electromagnetic wave does not refract when entering the sand layer from the air layer.

S207: Obtain the position of the slant height bin corresponding to the special bin according to the position of the virtual bin;

Wherein, the position of the oblique height surface element is the intersection of the virtual surface element in the oblique height direction and the upper surface of the sand layer; the oblique height direction is the difference between the virtual surface element and the incident point of the electromagnetic wave entering the sand layer. A vertical line is made at the virtual surface element, and the intersection between the vertical line and the upper surface of the sand layer is the slanted height surface element, and the slanted height surface element is also the ground surface element.

S208: Calculate the distance _{RT_N} from the virtual surface element to the radar;

The distance of R _{T_N} can be calculated by geometric relationship according to the flying height and incident angle of the radar.

S209: Calculate the distance R _{S_N} from the sloping surface element to the radar;

The distance of R _{S_N} can be calculated by geometric relationship according to the flying height and incident angle of the radar.

S210: Input the initial incident wave into the signal backscattering model to obtain the signal E _{S1_N} ;

The signal backscattering model is:

r_H为H极化分量的反射系数，n₁为入射波所在介质层的折射率，n₂为折射波所在介质层的折射率，θ_t、θ_i分别为入射角和折射角，

为非相干散射信号比例模型函数，E_a为所述信号后向散射模型的输入，E_b为所述信号后向散射模型的输出，在该步骤中，初始入射波为信号后向散射模型的输入。in,

r _H is the reflection coefficient of the H polarization component, n ₁ is the refractive index of the medium layer where the incident wave is located, n ₂ is the refractive index of the medium layer where the refracted wave is located, θ _t and θ _i are the incident angle and the refraction angle, respectively,

is the scale model function of the incoherent scattering signal, E _a is the input of the signal backscattering model, _Eb is the output of the signal backscattering model, in this step, the initial incident wave is the signal backscattering model. enter.

S211: Calculate the signal by sequentially passing the initial incident wave through the signal refraction model, the signal penetration attenuation model, the signal backscattering model, the signal penetration attenuation model, and the signal refraction model;

The signal refraction model is:

t_H为H极化分量的透射系数，n₁为入射波所在介质层的折射率，n₂为折射波所在介质层的折射率，θ_i、θ_t分别为折射角和入射角，

为相干散射信号比例模型函数，E_c为所述信号折射模型的输入，E_d为所述信号折射模型的输出。in,

t _H is the transmission coefficient of the H polarization component, n ₁ is the refractive index of the medium layer where the incident wave is located, n ₂ is the refractive index of the medium layer where the refracted wave is located, θ _i and θ _t are the refraction angle and the incident angle, respectively,

is the coherent scattering signal scale model function, E _c is the input of the signal refraction model, and E _d is the output of the signal refraction model.

The signal penetration attenuation model is:

Among them, E _g is the output of the signal penetration attenuation model, E _f is the input of the signal penetration attenuation model, _ke is the extinction coefficient, and X is the distance of the electromagnetic wave passing through the sand layer. On the premise that other parameters remain unchanged , the larger X is, the smaller the output signal E _g is. In the signal penetration attenuation model, since E _f is known and _ke is a constant, the electric field strength value of the attenuated electromagnetic wave can be obtained only by knowing the path X traversed by the electromagnetic wave.

In this step, the initial incident wave is used as the input of the signal refraction model to obtain the output of the signal refraction model; then the output of the signal refraction model is used as the input of the signal penetration attenuation model to obtain the output of the signal penetration attenuation model; The output of the signal penetration attenuation model is used as the input of the signal backscattering model, and the output of the signal backscattering model is obtained; then the output of the signal backscattering model is input into the signal penetration attenuation model twice, and the signal penetration attenuation model is obtained. The second output of the signal penetration attenuation model is then input to the signal refraction model twice to obtain the second output of the signal refraction model, that is, the signal E _{T1_N} .

S212: Use the following formula to calculate the radar echo signal E _{1_N} corresponding to the special surface element:

E _{1_N} =E _{S2_N} *exp(-j4πR _{S_N} /λ)+E _{T2_N} *exp(-j4πR _{T_N} /λ);

The following formula is used to calculate the radar echo signal E _{1_N} ' corresponding to the common surface element:

E _{1_N} '=E _{S2_N} *exp(-j4πR _{S_N} /λ);

in,

E _{S2_N} =Γ(b _N ,E _{RS2_N} ), E _{RS2_N} =E _{S1_N} *(randn ₁ +i*randn ₂ )

E _{T2_N} = Γ(b _N , E _{RT2_N} ), E _{RT2_N} = E _{T1_N} *(randn ₁ +i*randn ₂ )

Г is the spatial decoherence model function, b _N is the spatial baseline of the radar, N is a natural number, indicating that the radar has performed N multiple baseline observations, randn ₁ , randn ₂ are all subject to N(0,1) distribution random variable, λ is the wavelength of the electromagnetic wave;

For the radar echo signal of a special surface element, the electromagnetic wave backscattered by the incident wave on the surface of the sand layer and the electromagnetic wave that penetrates the surface of the sand layer and returns to the radar after refraction and energy attenuation need to be input into the preset Rayleigh fading model and spatial decompression model. After the coherent model is established, the coherent sum can be obtained to obtain the echo signal of the simulated special surface element. For the radar echo signal corresponding to the common panel, you only need to input the electromagnetic wave backscattered by the incident wave into the preset Rayleigh fading model and spatial decoherence model, and then the simulated echo signal of the normal panel can be obtained.

S213: Add random thermal noise to the radar echo signal E _{1_N} corresponding to the special panel to obtain the final echo signal E _{2_N} corresponding to the special panel; add the radar echo signal E _{1_N} ′ corresponding to the ordinary panel Add random thermal noise to obtain the final echo signal E _{2_N} ' corresponding to the common surface element;

Since there is a lot of noise in the actual detection process of the radar, in order to make the simulation result more realistic, the radar echo signal E 1_N corresponding to the special panel and the radar echo signal E _{1_N} corresponding to the ordinary _{panel are} ' to add random noise.

S214: splicing the radar echo signals corresponding to each of the special bins and the common bins according to the positions of the bins to obtain a SAR image sequence corresponding to the simulated detection area;

Since the analog detection area has been divided into multiple bins in step 204, after obtaining the analog echo signal of each special bin and common bin, splicing is performed according to the position of each special bin and common bin, The SAR image sequence corresponding to the simulated detection area can be obtained.

S215: Perform de-skew processing on the SAR image sequence to obtain a de-skewed SAR image sequence;

S216: Perform tomographic inversion on the de-oblique SAR image sequence, the formula is as follows:

γ'(s) _B = a ^H (s) Ra (s);

s为所述虚拟面元的斜高，r为所述虚拟面元的斜距，b_N为所述空间基线，λ为所述电磁波的波长，n＝0,1,2…N；Among them, a(s)=[exp(j2πξ ₀ s),…exp(j2πξ _N s)] ^T , R is the coherence matrix,

s is the slope height of the virtual bin, r is the slope distance of the virtual bin, b _N is the spatial baseline, λ is the wavelength of the electromagnetic wave, n=0, 1, 2...N;

The coherence matrix R is:

为对d_m求共轭操作，<·>_L为取平均操作；Among them, dn is the complex signal value of the SAR image sequence obtained by the _nth multi-baseline observation, dm is the complex signal value of the SAR image sequence obtained by the _mth multi-baseline observation,

For the conjugate operation of d _m , <·> _L is the average operation;

S217: Calculate the first two maximum values of γ'(s) _B to obtain the positions of the slanted height surface element and the virtual surface element.

For ordinary bins, since there is no virtual bin signal, when calculating the γ'(s) _B of the SAR image corresponding to the ordinary bin, the second maximum value is actually the main lobe representing the signal of the oblique high bin Sidelobe interfering signal on one side. Here, the first two maximum values of γ'(s) _B are usually taken, and a threshold is set, and the second maximum value of all bins greater than the threshold is used as the inversion result of the virtual bin.

S218: Convert the position of the virtual surfel corresponding to each of the special surfels to the position of the special surfel;

This step is used to obtain the position of the special surface element corresponding to each virtual surface element through the geometric relationship, that is, to obtain the position of each surface element on the simulated detection target.

S219: Obtain three-dimensional data of the simulated detection target and the surface of the sand layer according to the positions of all the special surface elements and the positions of the oblique height surface elements corresponding to the special surface elements;

S220: Determine the ability of the SAR signal to detect the subsurface target to be detected according to the comparison result of the three-dimensional data of the simulated detection target and the surface of the sand layer and the data model of the simulated detection target.

This method is a simulated detection for a known area. Due to the high time cost and economic cost of using SAR radar for detection, and SAR radar detection technology has no precedent for subsurface target detection, the researchers are in the known area. When detecting subsurface targets at a location, it is impossible to accurately know which type of radar should be used and the parameters of the radar. Therefore, this method is used to simulate the detection process of SAR radar, which can simultaneously simulate and image SAR signals with different observation parameters. By comparison, the optimal observation parameters are selected according to the comparison results, so as to provide accurate guidance for the staff to select the observation parameters of the radar and avoid meaningless cost waste.

Example 3:

On the basis of Embodiment 2, this embodiment provides a preferred method for evaluating the ability of a SAR signal to detect subsurface targets, and the description of Embodiment 2 can be referred to for relevant details.

FIG. 4 is a flowchart of a method for evaluating the ability of SAR signals to detect subsurface targets provided in Embodiment 3. As shown in FIG. 4 , in this embodiment, parameters are determined before evaluation, specifically determining observation parameters and environmental parameters; and then constructing The data model of the simulated detection target, on the basis of the data model, the initial incident wave is obtained after a series of calculations such as penetration attenuation model, signal backscattering model, signal refraction model, surface target and spatial decoherence model. SAR image sequence, and then perform de-slope and tomographic inversion on the simulated SAR image sequence. Finally, according to the results of tomographic inversion, the actual position of the target to be detected can be obtained, and then a three-dimensional display can be performed. By comparing the data models of the detected targets, the ability of SAR signals to detect subsurface targets can be evaluated.

The difference between this embodiment and Embodiment 2 is that after the SAR image sequence corresponding to the simulated detection area is obtained, for the SAR image sequence obtained by N times of multi-baseline observations, the amplitude of the image sequence obtained by each observation is calculated. The sum of the squares is divided by N to obtain the time-domain average intensity map of the SAR image sequence. Since the radar performs N multi-baseline observations of the target to be detected during the actual observation, the orbit of the radar is different in each observation. Therefore, for the SAR image sequence obtained by each simulated observation, the average value of the square of its amplitude is calculated, that is, The time-domain average intensity map of the SAR image sequence can be obtained.

Although some specific embodiments of the present invention have been described in detail by way of examples, those skilled in the art should understand that the above examples are provided for illustration only and not for the purpose of limiting the scope of the present invention. Those skilled in the art will appreciate that modifications may be made to the above embodiments without departing from the scope and spirit of the present invention. The scope of the invention is defined by the appended claims.

Claims (8)

1. a method for evaluating SAR signal detection subsurface target capability, is characterized in that, comprises: Obtain environmental parameters of the environment where the subsurface target to be detected is located, wherein the film layer where the subsurface target to be detected is located is a relic layer, and there is a sand layer between the relic layer and the air layer, and the environmental parameters include the Physical parameters of the sand layer; Determine the observation parameters of the radar corresponding to the simulated SAR signal, wherein the propagation speed of the electromagnetic wave of the radar in the air layer, the propagation speed of the electromagnetic wave in the sand layer, the incident angle of the electromagnetic wave emitted by the radar, the wavelength of the initial incident wave and said electromagnetic wave; Build a data model of the simulated detection target, wherein the data model of the simulated detection target includes a simulated detection area and the depth of the simulated detection target, and the simulated detection area includes a special area with the simulated detection target and the special area with the simulated detection target. ordinary areas outside the area; dividing the simulated detection area into a plurality of surfels, wherein the plurality of surfels include special surfels located in the special area and common surfels located in the general area; According to the propagation speed of the electromagnetic wave in the sand layer and the depth of the simulated detection target, the time when the electromagnetic wave actually propagates to the simulated detection target is obtained as the first time; According to the propagation speed of the electromagnetic wave in the air layer, the incident angle, the physical parameters of the sand layer, the position of the special surface element and the first time, the corresponding value of the special surface element is obtained. the position of the virtual surfel; The position of the oblique height surface element corresponding to the special surface element is obtained according to the position of the virtual surface element, wherein the position of the oblique height surface element is the distance between the virtual surface element and the sand layer in the oblique height direction. The intersection point of the upper surface, and the oblique height direction is the connection line between the virtual surface element and the incident point of the electromagnetic wave entering the sand layer; Calculate the distance _{RT_N} from the virtual surface element to the radar; Calculate the distance R _{S_N} from the sloping surface element to the radar; Inputting the initial incident wave to the signal backscattering model to obtain the signal E _{S1_N} ; The signal E _{T1_N} is obtained by calculating the initial incident wave through the signal refraction model, the signal penetration attenuation model, the signal backscattering model, the signal penetration attenuation model and the signal refraction model in sequence; The following formula is used to calculate the radar echo signal E _{1_N} corresponding to the special surface element: E _{1_N} =E _{S2_N} *exp(-j4πR _{S_N} /λ)+E _{T2_N} *exp(-j4πR _{T_N} /λ); The following formula is used to calculate the radar echo signal E _{1_N} ' corresponding to the common surface element: E _{1_N} '=E _{S2_N} *exp(-j4πR _{S_N} /λ) in, E _{S2_N} =Γ(b _N ,E _{RS2_N} ), E _{RS2_N} =E _{S1_N} *(randn ₁ +i*randn ₂ ) E _{T2_N} = Γ(b _N , E _{RT2_N} ), E _{RT2_N} = E _{T1_N} *(randn ₁ +i*randn ₂ ) Г is the spatial decoherence model function, b _N is the spatial baseline of the radar, N is a natural number, indicating that the radar has performed N multiple baseline observations, randn ₁ , randn ₂ are all subject to N(0,1) distribution random variable, λ is the wavelength of the electromagnetic wave; splicing the radar echo signals corresponding to each of the special surface element and the common surface element according to the positions of the special surface element and the common surface element to obtain a SAR image sequence corresponding to the simulated detection area; The multi-view TomoSAR inversion algorithm is used for the SAR image sequence to obtain the position and signal strength of the virtual bin and the oblique height bin corresponding to each of the special bins, as well as the oblique height corresponding to each ordinary bin. The location and signal strength of the bin; converting the position of the virtual surfel corresponding to each of the special surfels to the position of the special surfel; Obtain the three-dimensional data of the simulated detection target and the surface of the sand layer according to the positions of all the special surface elements and the positions of the oblique height surface elements corresponding to the special surface elements; According to the comparison result of the three-dimensional data of the simulated detection target and the surface of the sand layer and the data model of the simulated detection target, the ability of the SAR signal to detect the subsurface target to be detected is determined. 2. the method for evaluating SAR signal detection subsurface target capability according to claim 1, is characterized in that, After obtaining the radar echo signals corresponding to all the bins, before splicing the radar echo signals corresponding to the special bins and the common bins according to the positions of the special bins and the common bins, The method also includes: adding random thermal noise to the radar echo signal E _{1_N} corresponding to the special surface element to obtain the final echo signal E _{2_N} corresponding to the special surface element; Add random thermal noise to the radar echo signal E _{1_N} ' corresponding to the common surface element to obtain the final echo signal E _{2_N} ' corresponding to the common surface element, Wherein, the step of splicing the radar echo signals corresponding to each of the special panel and the common panel according to the positions of the special panel and the normal panel is specifically: combining each of the special panel and the normal panel. The final echo signal corresponding to the common bin is spliced according to the positions of the special bin and the common bin. 3. The method for evaluating the ability of SAR signals to detect subsurface targets according to claim 1, wherein the signal backscattering model is:

in,

r _H is the reflection coefficient of the H polarization component, n ₁ is the refractive index of the medium layer where the incident wave is located, n ₂ is the refractive index of the medium layer where the refracted wave is located, θ _i and θ _t are the incident angle and the refraction angle, respectively,

is the scale model function of the incoherent scattering signal, E _a is the input of the signal backscattering model, and _Eb is the output of the signal backscattering model. 4. the method for evaluating SAR signal detection subsurface target capability according to claim 1, is characterized in that, described signal refraction model is:

in,

t _H is the transmission coefficient of the H polarization component, n ₁ is the refractive index of the medium layer where the incident wave is located, n ₂ is the refractive index of the medium layer where the refracted wave is located, θ _i and θ _t are the incident angle and the refraction angle, respectively,

is the coherent scattering signal scale model function, E _c is the input of the signal refraction model, and E _d is the output of the signal refraction model. 5. The method for evaluating the ability of SAR signals to detect subsurface targets according to claim 3 or 4, wherein the environmental parameter includes the dielectric constant of the electromagnetic wave in the sand layer, and the following formula is used to calculate the The refractive index of electromagnetic waves in the sand layer:

Among them, Re represents the operator of taking the real part, and ε is the dielectric constant of the sand layer. 6. The method for evaluating the ability of SAR signals to detect subsurface targets according to claim 1, wherein the signal penetration attenuation model is:

Wherein, E _g is the output of the signal penetration attenuation model, X is the distance of the electromagnetic wave passing through the sand layer, E _f is the input of the signal penetration attenuation model, and _ke is the extinction coefficient. 7. The method for evaluating the ability of SAR signals to detect subsurface targets according to claim 1, wherein a multi-view TomoSAR inversion algorithm is used for the SAR image sequence to obtain the corresponding The steps for the location of virtual bins include: performing de-skew processing on the SAR image sequence to obtain a de-skewed SAR image sequence; The tomographic inversion of the de-oblique SAR image sequence, the formula is as follows: γ'(s) _B = a ^H (s) Ra (s); Among them, a(s)=[exp(j2πξ ₀ s),…exp(j2πξ _N s)] ^T , R is the coherence matrix,

s is the slope height of the virtual bin, r is the slope distance of the virtual bin, b _N is the spatial baseline, λ is the wavelength of the electromagnetic wave, n=0, 1, 2...N; The coherence matrix R is:

Among them, dn is the complex signal value of the SAR image sequence obtained by the _nth multi-baseline observation, dm is the complex signal value of the SAR image sequence obtained by the _mth multi-baseline observation,

For the conjugate operation of d _m , <·> _L is the average operation; Calculate the first two maximum values of γ'(s) _B to obtain the positions of the slanted height bin and the virtual bin. 8. The method for evaluating the ability of a SAR signal to detect subsurface targets according to claim 7, wherein the step of converting the virtual surface element corresponding to each special surface element to the position of the special surface element comprises: According to the geometric relationship between the special surface element and the corresponding virtual surface element, the position of the virtual surface element is converted to the special surface element, and the oblique height of the special surface element is obtained; Convert the oblique height of the special surface element to the vertical height to obtain the position of the special surface element.

Images