CN112882007B - Realization method of single-pulse multi-target super-resolution angle measurement based on sparse array radar - Google Patents

Abstract

The invention provides a single-pulse multi-target super-resolution angle measurement implementation method based on a sparse array radar. The method comprises the following steps: performing one-dimensional range profile constant false alarm detection on target echo signals of a plurality of channels of the single-pulse sparse array radar to obtain array signals of range units where all targets are located, and constructing an angle observation vector of the targets according to the array signals of the range units where the targets are located; constructing an angle measurement dictionary matrix according to the working parameters of the sparse array radar system, and constructing an angle observation matrix according to the angle measurement dictionary matrix and the working parameters of the sparse array radar; based on the angle observation vector and the angle observation matrix of the target, reconstructing phase delay vectors of different targets by using a minimum L1 norm optimization algorithm to form images, and realizing multi-target super-resolution and angle measurement of the target echo signals. The method can be used for carrying out angle resolution and accurate measurement on a plurality of targets with similar distances and angles in the wave beam from radar monopulse echo data.

Description

Realization method of single-pulse multi-target super-resolution angle measurement based on sparse array radar

technical field

The invention relates to the technical field of radar signal processing, in particular to a method for implementing single-pulse multi-target super-resolution angle measurement based on sparse array radar.

Background technique

Array radar has a wide range of applications in the field of target detection. In the radar detection technology, since the range image of the target is limited by the range resolution, and the angular resolution is limited by the radar beam width, the high-resolution technology of dense multi-targets is a difficult point in the field of radar detection.

At present, the target detection and angle measurement methods based on array radar in the prior art mainly include constant false alarm detection technology based on one-dimensional range image and beam scanning method based on array. The above target detection and angle measurement methods have certain shortcomings in dense multi-target detection. The constant false alarm detection technology based on the one-dimensional range image cannot realize the resolution of multiple targets with similar distances in the beam; the array-based beam scanning method cannot accurately measure multiple targets with similar angles, when the distance between multiple targets is less than When the interval between a range unit and the angle interval is smaller than the half-power beam width, none of the above methods can correctly distinguish and accurately measure multiple targets in space.

To sum up, how to achieve super-resolution and angle-accurate measurement of space-dense multi-targets based on the array radar detection system is an urgent problem to be solved.

Contents of the invention

Embodiments of the present invention provide a method for implementing single-pulse multi-target super-resolution angle measurement based on sparse array radar, so as to realize multi-target resolution and angle measurement for radar single-pulse echo data.

In order to achieve the above object, the present invention adopts the following technical solutions.

A method for implementing single-pulse multi-target super-resolution angle measurement based on sparse array radar, the method comprising:

One-dimensional range image constant false alarm detection is performed on the target echo signals of multiple channels of the single pulse sparse array radar, and the array signals of the range units where each target is located are obtained, and the angular observation vector of the target is constructed according to the array signals of the range units where the target is located;

The angle measurement dictionary matrix is constructed by using the working parameters of the sparse array radar system, and the angle measurement matrix is constructed according to the angle measurement dictionary matrix and the working parameters of the sparse array radar;

Based on the angle observation vector of the target and the angle observation matrix, an optimization algorithm is used to reconstruct and image the phase delay vectors of different targets, so as to realize multi-target super-resolution and angle measurement of the target echo signals.

Preferably, the one-dimensional range image constant false alarm detection is performed on the target echo signals of multiple channels of the single-pulse sparse array radar, and the array signals of the distance units where each target is located are obtained. The angle observation vector of the target, including:

Perform digital beamforming on the target echo signals of multiple channels of the single pulse sparse array radar, and perform matching filtering on the echo data after digital beamforming to obtain the target one-dimensional range image data;

The one-dimensional range CFAR constant false alarm detection is performed on the echo data of each range unit in the target one-dimensional range image data to detect targets at different distances and obtain the array signals of the range units where each target is located.

The angle observation vector of the target is constructed by using the detected array signals of the target range unit.

Preferably, the angular observation vector of the target is expressed as:

S(k)＝[s ₁ (k) s ₂ (k) … s _P (k)] (1)

In formula (1), _sp (k) is the range unit complex signal data of the kth target detected by the one-dimensional range image CFAR in the pth channel of the sparse array radar, p=1,...,P, P< N, N is the number of channels in the array radar array.

Preferably, the construction of the angle measurement dictionary matrix using the working parameters of the sparse array radar system includes:

The angle measurement dictionary matrix based on the array radar is constructed according to the arrangement of the full array radar array corresponding to the sparse array radar system, the working wavelength, the number of full array sub-arrays, the distance between adjacent sub-arrays and the angular resolution interval. The angle measurement dictionary matrix is represented as a matrix of N×M:

为角度θ_m的导向矢量，θ_m＝(m-N/2)Δθ，M为空间角度划分个数，Δθ为空间角度划分间隔，d为阵列雷达相邻两子阵间距，λ为雷达工作频率。In formula (2),

is the steering vector of angle θ _m , θ _m = (mN/2)Δθ, M is the number of space angle divisions, Δθ is the space angle division interval, d is the distance between two adjacent sub-arrays of the array radar, and λ is the radar operating frequency.

Preferably, the use of the angle measurement dictionary matrix and the working parameters of the sparse array radar to construct the angle observation matrix includes:

Using the angle measurement dictionary matrix, the array position and the number of sub-arrays of the sparse array radar to construct the corresponding angle observation matrix, the angle observation matrix is expressed as:

A＝[Φ ₁ Φ ₂ ... Φ _P ] ^T (3)

In formula (3), [·] ^T represents the transposition of the matrix, Φ _p represents the pth row vector of the angle measurement dictionary matrix Φ, p=1,...,P, P<N.

Preferably, based on the angle observation vector of the target and the angle observation matrix, an optimization algorithm is used to reconstruct and image the phase delay vectors of different targets, so as to realize the multi-target super-resolution and angle Measurements, including:

Using the target angle observation vector and angle observation matrix of the sparse array radar, the optimization algorithm is used to reconstruct the projection coefficient of the phase delay vector of different targets under the base vector of the observation matrix;

Taking the minimum l ₁ norm as the criterion, according to the angle observation matrix A and angle observation vector S(k) of the sparse array radar, the optimization algorithm is used to solve the following optimization model:

为空间各角度单元的目标后向散射系数估计值，argmin为取函数最小值，根据求得的最优解/>

将第k个距离单元处的各目标进行分辨，得到各目标的角度。In formula (4),

is the estimated value of the backscattering coefficient of the target in each angular unit of the space, argmin is the minimum value of the function, according to the obtained optimal solution />

Each target at the kth distance unit is distinguished to obtain the angle of each target.

It can be seen from the technical solutions provided by the above-mentioned embodiments of the present invention that the embodiments of the present invention provide a dense multi-target resolution and angle measurement method based on a sparse array radar, which can detect objects within a beam from the radar single-pulse echo data. Multiple targets with similar distances and angles achieve angular resolution and precise measurement.

Additional aspects and advantages of the invention will be set forth in part in the description which follows, and will become apparent from the description, or may be learned by practice of the invention.

Description of drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings that need to be used in the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For Those of ordinary skill in the art can also obtain other drawings based on these drawings without making creative efforts.

Fig. 1 is a schematic diagram of a multi-target super-resolution angle measurement method based on sparse array radar provided by an embodiment of the present invention;

FIG. 2 is a schematic diagram of an observation model of a sparse array radar for space-dense multi-targets provided by an embodiment of the present invention;

FIG. 3 is a schematic diagram of wave path delays of echoes from each array of targets within a beam provided by an embodiment of the present invention;

Fig. 4 is a schematic diagram of a one-dimensional range image after pulse compression based on the sparse array radar single-pulse multi-target echo data provided by the embodiment of the present invention;

Fig. 5 is a schematic diagram of angle measurement dictionary matrix data constructed by utilizing the operating parameters of the sparse array radar system provided by the embodiment of the present invention;

Fig. 6 is a schematic diagram of angle observation matrix data constructed by using the working parameters of the sparse array radar system provided by the embodiment of the present invention;

Figure 7 is a diagram of the angle observation data and angle reconstruction results of a single-target sparse array radar for detecting the algorithm provided by the embodiment of the present invention; the left figure is the angle observation data of a single target in the range unit of the sparse array radar; the right figure Schematic diagram of the results of angle reconstruction imaging of a single target using the optimization algorithm;

Figure 8 is a diagram of the angle observation data and angle reconstruction results of a dual-target sparse array radar with the same distance to detect the algorithm provided by the embodiment of the present invention; the left figure is the angle observation data of dual targets in the range unit of the sparse array radar ; The right figure is a schematic diagram of the angle reconstruction imaging results of two targets using the optimization algorithm.

Detailed ways

Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

Those skilled in the art will understand that unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the word "comprising" used in the description of the present invention refers to the presence of said features, integers, steps, operations, elements and/or components, but does not exclude the presence or addition of one or more other features, Integers, steps, operations, elements, components, and/or groups thereof.

Those skilled in the art can understand that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It should also be understood that terms such as those defined in commonly used dictionaries should be understood to have a meaning consistent with the meaning in the context of the prior art, and unless defined as herein, are not to be interpreted in an idealized or overly formal sense explain.

In order to facilitate the understanding of the embodiments of the present invention, several specific embodiments will be taken as examples for further explanation below in conjunction with the accompanying drawings, and each embodiment does not constitute a limitation to the embodiments of the present invention.

The implementation schematic diagram of a single-pulse multi-target super-resolution angle measurement method based on sparse array radar provided by the embodiment of the present invention is shown in Figure 1, including the following processing steps:

Step 101: Perform one-dimensional range image constant false alarm detection on the target echo, and construct an angular observation vector of the target by using the array signals of the range unit where the target is located.

The one-dimensional distance is like the distance projection of the target reflection echo intensity at different distances from the radar in space. The one-dimensional range image CFAR detection of the target echo can distinguish the strong reflective targets with different spatial distances. At the same time, the angular observation vector of the target is constructed by using the array data of the range unit where the target is detected by CFAR.

FIG. 2 is a schematic diagram of an observation model of a sparse array radar for space-dense multi-targets provided by Embodiment 1 of the present invention. Firstly, digital beamforming is performed on the target echo signals of multiple channels of the single-pulse sparse array radar to improve the signal-to-noise ratio of the detection data; matching filtering is performed on the echo data after digital beamforming to obtain the one-dimensional range image data of the target Then, carry out one-dimensional distance CFAR (Constant FalseAlarm Rate, constant false alarm rate) detection to the echo data of each range unit in the one-dimensional range image data of the target, detect the target of different distances, obtain the distance unit where each target is located array signal.

Using the detected array signals of the distance unit where the target is located, the angle observation vector of the target is constructed, and the angle observation vector of the target can be expressed as:

S(k)＝[s ₁ (k) s ₂ (k) … s _P (k)] (5)

In formula (1), _sp (k) is the range unit complex signal data of the kth target detected by the one-dimensional range image CFAR in the pth channel of the sparse array radar, p=1,...,P, P< N, N is the number of channels in the array radar array.

Step 102: Construct an angle measurement dictionary matrix by using the operating parameters of the sparse array radar system, and construct an angle measurement matrix according to the angle measurement dictionary matrix and the operating parameters of the sparse array radar.

The angle measurement dictionary matrix based on the array radar is constructed according to the arrangement of the full array radar array corresponding to the sparse array radar system, the working wavelength, the number of full array sub-arrays, the distance between adjacent sub-arrays and the angular resolution interval. The angle measurement dictionary matrix can be expressed as a matrix of N×M:

为角度θ_m的导向矢量，θ_m＝(m-N/2)Δθ，M为空间角度划分个数，Δθ为空间角度划分间隔，d为阵列雷达相邻两子阵间距，λ为雷达工作频率。In formula (2),

is the steering vector of angle θ _m , θ _m = (mN/2)Δθ, M is the number of space angle divisions, Δθ is the space angle division interval, d is the distance between two adjacent sub-arrays of the array radar, and λ is the radar operating frequency.

Then, the angle measurement matrix is constructed by using the angle measurement dictionary matrix, the array position and the number of sub-arrays of the sparse array radar, and the angle observation matrix can be expressed as:

A＝[Φ ₁ Φ ₂ ... Φ _P ] ^T (7)

In formula (3), [·] ^T represents the transposition of the matrix, Φ _p represents the pth row vector of the angle measurement dictionary matrix Φ, p=1,...,P, P<N.

Step 103: Based on the target angle observation vector and the angle observation matrix, use an optimization algorithm to reconstruct the image of the target angle, and realize multi-target super-resolution and angle measurement.

FIG. 3 is a schematic diagram of wave path delays of echoes from each array of targets within a beam provided by Embodiment 1 of the present invention. When the distances of multiple targets in the beam are the same and the space angles are similar, the radar one-dimensional range images of multiple targets overlap on the range unit. At this time, the one-dimensional range images cannot distinguish multiple targets with the same distance. At the same time, since the angular interval of multiple targets is smaller than the radar beam width, multiple targets cannot be distinguished by the radar beam width. In this case, since the phase delays of the target echoes from different angles to the radar arrays are different, the projections of the phase differences of the echoes of different targets in each array under the basis vectors of the system measurement dictionary matrix are also different. Based on the above properties, using the target angle observation vector and angle observation matrix of the sparse array radar, and using the optimization algorithm to reconstruct the projection coefficients of the phase delay vectors of different targets under the base vector of the observation matrix, the distances with the same distance, Multiple targets with similar angles are super-resolved, and accurate measurement of the angle of each target can be realized.

Specifically, with the minimum l ₁ norm as the criterion, according to the angle observation matrix A and the angle observation vector S(k) of the sparse array radar, the optimization algorithm is used to solve the following optimization model:

为空间各角度单元的目标后向散射系数估计值，arg min为取函数最小值，根据求得的最优解/>

就可以将第k个距离单元处的各目标进行分辨，并可得到各目标的精确角度。In formula (4),

is the estimated value of the backscatter coefficient of the target in each angular unit of the space, arg min is the minimum value of the function, according to the obtained optimal solution />

Then the targets at the kth distance unit can be distinguished, and the precise angle of each target can be obtained.

The technical solutions of the present invention will be further described in detail below in conjunction with specific embodiments.

Example 1

Fig. 4 is a schematic diagram of one-dimensional range image data of sparse array radar for testing the algorithm of the embodiment of the present invention. Among them, the operating frequency of the sparse array radar is 9.2GHz, the transmission signal bandwidth is 3MHz, and the beam width is 0.45°. The distances of the three targets in the beam to the radar are: [10, 10, 15]km, and the azimuth angles of the targets are: [1.0 , 1.3, 0.9]°. It can be seen from the figure that the 1st and 2nd targets are indistinguishable from the one-dimensional range image due to the same distance, while the third target can be distinguished from the first two targets from the one-dimensional range image.

Figure 5 is a data schematic diagram of the angle measurement dictionary matrix constructed by using the parameters of the full-array radar system, wherein the number of sub-arrays of the full-array radar is 128, the angle range of the dictionary matrix is [-5,5]°, and the angle division interval is 0.1° .

Figure 6 shows the angular observation matrix constructed by using the array arrangement of the sparse array radar system. The number of sparse array radar sub-arrays is 16, which is 1/8 of the number of full-array radar sub-arrays.

Fig. 7 is a schematic diagram of the target angle image obtained by using the sparse array radar angle measurement data and reconstruction of the distance unit where the third target is located. Among them, the left figure is the angular observation vector data diagram of the target by the full-array radar and the sparse-array radar. From Figure 7, it can be seen that the sparse-array radar is 1/8 of the observation data of the full-array radar. The figure on the right shows the angle image data of the target obtained by reconstruction. It can be seen from Figure 7 that since there is only one target at this distance unit, there is only one peak in the angle reconstruction image, and the peak position is the precise angle value of the target.

Fig. 8 is a schematic diagram of the target angle image obtained by using the sparse array radar angle measurement data and reconstruction of the first and second target distance units. Among them, the left figure is the angle observation vector data diagram of the range unit of the full-array radar and the sparse array radar, and the right figure is the angle data diagram of each target at the range unit obtained by reconstruction. It can be seen from Figure 8 that since the distance unit contains two targets 1 and 2, two peaks appear in the reconstructed angle image, and the two targets can be clearly distinguished, and each peak position corresponds to the two targets The exact value of the angle. The sparse-array radar only uses 1/8 of the data volume of the full-array radar to achieve angular super-resolution and precise measurement of two targets at the same distance and with an angle smaller than the beam width.

In summary, the embodiment of the present invention provides a dense multi-target resolution and angle measurement method based on sparse array radar, which can measure the angles of multiple targets with similar distances and angles in the beam from the radar single pulse echo data. Resolution and precise measurement.

Through the method of the embodiment of the present invention, not only the amount of data in the array radar system can be greatly reduced, but also super-resolution and precise angle measurement can be performed on multiple targets within the beam at the same distance and close to the angle.

Those skilled in the art can understand that the accompanying drawing is only a schematic diagram of an embodiment, and the modules or processes in the accompanying drawing are not necessarily necessary for implementing the present invention.

It can be known from the above description of the implementation manners that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the essence of the technical solution of the present invention or the part that contributes to the prior art can be embodied in the form of software products, and the computer software products can be stored in storage media, such as ROM/RAM, disk , CD, etc., including several instructions to make a computer device (which may be a personal computer, server, or network device, etc.) execute the methods described in various embodiments or some parts of the embodiments of the present invention.

Each embodiment in this specification is described in a progressive manner, the same and similar parts of each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments.

The above is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present invention can easily think of changes or Replacement should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the protection scope of the claims.

Claims (6)

1. A single-pulse multi-target super-resolution angle measurement method based on sparse array radar, is characterized in that the method comprises: One-dimensional range image constant false alarm detection is performed on the target echo signals of multiple channels of the single pulse sparse array radar, and the array signals of the range units where each target is located are obtained, and the angular observation vector of the target is constructed according to the array signals of the range units where the target is located; Construct the angle measurement dictionary matrix according to the working parameters of the sparse array radar system, and construct the angle measurement matrix according to the angle measurement dictionary matrix and the working parameters of the sparse array radar; Based on the angle observation vector of the target and the angle observation matrix, the minimum L1 norm optimization algorithm is used to reconstruct and image the phase delay vectors of different targets, so as to realize multi-target super-resolution and angle measurement of the target echo signal. 2. The method according to claim 1, characterized in that, the one-dimensional range image constant false alarm detection is carried out to the target echo signals of multiple channels of the single-pulse sparse array radar to obtain the distance unit of each target. Array signal, constructing the angle observation vector of the target according to the array signal of the range unit where the target is located, including: Perform digital beamforming on the target echo signals of multiple channels of the single pulse sparse array radar, and perform matching filtering on the echo data after digital beamforming to obtain the target one-dimensional range image data; Perform one-dimensional range CFAR constant false alarm detection on the echo data of each range unit in the one-dimensional range image data of the target, detect targets at different distances, and obtain the array signal of the range unit where each target is located; According to the detected array signals of the target range unit, the angle observation vector of the target is constructed. 3. The method according to claim 2, wherein the angular observation vector of the target is expressed as: S(k)＝[s ₁ (k) s ₂ (k) … s _P (k)] (1) In formula (1), _sp (k) is the range unit complex signal data of the kth target detected by the one-dimensional range image CFAR in the pth channel of the sparse array radar, p=1,...,P, P< N, N is the number of channels in the array radar array. 4. The method according to claim 2, wherein said construction of an angle measurement dictionary matrix according to the working parameters of the sparse array radar system comprises: According to the arrangement of the full-array radar array corresponding to the sparse array radar system, the working wavelength, the number of full-array sub-arrays, the distance between adjacent sub-arrays, and the angular resolution interval, the angle measurement dictionary matrix based on the array radar is constructed. The angle measurement dictionary matrix represents is an N×M matrix, where N is the number of channels in the array radar array:

In formula (2),

is the steering vector of angle θ _m , θ _m = (mN/2)Δθ, M is the number of space angle divisions, Δθ is the space angle division interval, d is the distance between two adjacent sub-arrays of the array radar, and λ is the radar operating frequency. 5. method according to claim 4, is characterized in that, described construction angle observation matrix according to the working parameter of angle measurement dictionary matrix and sparse array radar, comprises: According to the angle measurement dictionary matrix, the array position and the number of sub-arrays of the sparse array radar, the corresponding angle observation matrix is constructed, and the angle observation matrix is expressed as: A＝[Φ ₁ Φ ₂ ... Φ _P ] ^T (3) In formula (3), [ ] ^T represents the transposition of the matrix, Φ _p represents the pth row vector of the angle measurement dictionary matrix Φ, p=1,...,P, P<N, N is the full array channel of the array radar number. 6. The method according to any one of claims 1 to 5, wherein, based on the angle observation vector and the angle observation matrix of the target, the minimum L1 norm optimization algorithm is used to analyze the phases of different targets. The delay vector is used for reconstruction and imaging to realize the multi-target super-resolution and angle measurement of the target echo signal, including: According to the target angle observation vector and angle observation matrix of the sparse array radar, the minimum L1 norm optimization algorithm is used to reconstruct and solve the projection coefficients of the phase delay vectors of different targets under the base vector of the observation matrix; Taking the minimum L1 norm as the criterion, according to the angle observation matrix A and angle observation vector S(k) of the sparse array radar, the following optimization model is solved by using the minimum L1 norm optimization algorithm:

In formula (4),

is the estimated value of the backscattering coefficient of the target in each angular unit of the space, argmin is the minimum value of the function, according to the obtained optimal solution />

Each target at the kth distance unit is distinguished to obtain the angle of each target.

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