CN113009445A - Target detection method and device, electronic equipment and storage medium - Google Patents
Target detection method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a target detection method, a target detection device, an electronic device and a storage medium, wherein the target detection method comprises the following steps: acquiring radar echo data; determining a clutter type of radar clutter in the radar echo data; determining a corresponding target detection algorithm at least based on the clutter types, wherein the detection algorithms corresponding to different clutter types are different; and performing target detection on the radar echo data based on the target detection algorithm.
Description
Technical Field
The present application relates to the field of radar technologies, and in particular, to a target detection method and apparatus, an electronic device, and a storage medium.
Background
When the radar detects a low-altitude low-speed small target, various interferences always exist near the target, the interferences mainly come from clutter such as ground objects, cloud rain, sea waves and the like, and when a target signal is received by a receiver, the unwanted interference signals are inevitably received. The interference is the inherent background environment in radar signal processing, so that targets are often difficult to distinguish, and the target detection effect is greatly influenced.
In the radar system, radar echo signals received by a receiver play a role in inhibiting clutter to a certain extent after passing through a signal processor, and then a target is found through constant false alarm detection. For the ground clutter, a classical constant false alarm detection (CFAR) method is often adopted, such as a constant false alarm detection method of unit average CFAR (CA-CFAR), unit average selection large (GO-CFAR) \ unit average selection small (SO-CFAR), ordered statistics class (OS-CFAR), and the like; and aiming at the sea clutter, selecting a series of methods such as Adaptive Matched Filtering (AMF), fractal feature-based detection algorithm and the like. With the wide application of radar in military and civil use and the increasingly complex clutter environment, the CFAR technology still has many problems and challenges.
The current CFAR target detection technology can only detect targets in a single environment, and generally only one CFAR detection method is used, so that the most suitable CFAR detection method cannot be automatically matched according to the change of the environment. Meanwhile, the conventional CA-CFAR strategy has the best detection performance under the uniform clutter background, but the CA-CFAR detection performance is seriously reduced under the multi-target background and the clutter edge background. In addition, the maximum choice (GO-CFAR) and minimum choice (SO-CFAR) detection algorithms as an improvement to the CA-CFAR strategy can only improve the detection performance in one of the multi-target background or clutter edge background, and another class of ordered statistics class (OS-CFAR) constant false alarm detection algorithms has better survivability than the mean class detector in the multi-interference target background by simply selecting the kth rank value in the N reference units as the estimate of the current clutter power level, but is accompanied by a larger loss of constant false alarm rate in the uniform clutter environment and has poor control capability of the constant false alarm peak in the clutter edge condition. In the related art, the CFAR detector has poor environmental adaptability, low target detection accuracy and inflexible detection mode.
Disclosure of Invention
In view of the foregoing problems, the present application provides a target detection method, an apparatus, an electronic device, and a storage medium.
The application provides a target detection method, which comprises the following steps:
acquiring radar echo data;
determining a clutter type of radar clutter in the radar echo data;
determining a corresponding target detection algorithm at least based on the clutter types, wherein the detection algorithms corresponding to different clutter types are different;
and performing target detection on the radar echo data based on the target detection algorithm.
In some embodiments, the determining a clutter type of radar clutter in the radar echo data comprises:
determining radar clutter data in the radar echo data;
determining clutter classification features of the radar clutter based on the radar clutter data;
determining a clutter type of the radar clutter based on the clutter classification characteristic.
In some embodiments, the determining a clutter classification feature of the radar clutter based on the radar clutter data comprises:
determining clutter sample data based on the radar clutter data;
determining a sample feature function based on the clutter sample data, wherein the sample feature function comprises: clutter classification features;
determining a clutter classification feature based on the sample feature function.
In some embodiments, the clutter types include sea clutter, the determining a corresponding target detection algorithm based at least on the clutter types, comprising:
and determining a self-adaptive matched filtering algorithm based on the sea clutter.
In some embodiments, the clutter type comprises ground clutter, the method further comprising:
determining a background environment type of the radar clutter;
determining a corresponding target detection algorithm based at least on the clutter type, comprising:
and determining a corresponding target detection algorithm based on the clutter type and the background environment type.
In some embodiments, the determining the background environment type of the radar clutter comprises:
determining a reference point for the radar clutter;
determining a first reference window and a second reference window in the sliding window based on the reference point, wherein the first reference window and the second reference window are positioned on two sides of the reference point;
determining a first uniform degree value of the clutter background of the first reference window, and determining a second uniform degree value of the clutter background of a second reference window;
determining the background environment type based on the first and second uniformity values.
In some embodiments, the context types include: a first type, a second type, and a third type;
wherein the first uniformity value represents that the clutter background of the first reference window is uniform, the second uniformity value represents that the clutter background of the second reference window is uniform, and the first type is the first type when the first uniformity value and the second uniformity value are equal;
the second type is determined under the condition that the first uniformity degree value represents that the clutter background of the first reference window is uniform, the second uniformity degree value represents that the clutter background of the second reference window is uniform, and the first uniformity degree value and the second uniformity degree value are not equal;
the third type if the first homogeneity degree value characterizes clutter background non-uniformity of a first reference window and/or the second homogeneity degree value characterizes clutter background non-uniformity of a second reference window.
In some embodiments, when the background environment type is the first type, the target detection algorithm is a cell average constant false alarm detection algorithm;
when the background environment type is the second type, the target detection algorithm is a constant false alarm rate detection algorithm for averagely selecting large units on two sides;
and when the background environment type is a third type, selecting a constant false alarm rate detection algorithm as the target detection algorithm.
An embodiment of the present application provides a target detection apparatus, including:
the acquisition module is used for acquiring radar echo data;
the first determination module is used for determining a clutter type of radar clutter in the radar echo data;
a second determining module, configured to determine a corresponding target detection algorithm based on at least the clutter types, where the detection algorithms corresponding to different clutter types are different;
and the detection module is used for carrying out target detection on the radar echo data based on the target detection algorithm.
An embodiment of the present application provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, the electronic device executes the object detection method described in any one of the above items.
Embodiments of the present application provide a storage medium storing a computer program, which is executable by one or more processors, and is operable to implement any one of the object detection methods described above.
According to the target detection method, the target detection device, the electronic equipment and the storage medium, when radar echo data are obtained, the clutter type of radar clutter in the radar echo data is determined; determining a corresponding target detection algorithm at least based on the clutter types, wherein the detection algorithms corresponding to different clutter types are different; therefore, target detection is carried out on the radar echo data based on a target detection algorithm, the detection performance of the electronic equipment is improved, the detection flexibility is improved, and a target can be detected accurately.
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The present application will be described in more detail below on the basis of embodiments and with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart illustrating an implementation of a target detection method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating a method for determining a clutter classification characteristic of the radar clutter based on the radar clutter data according to an embodiment of the present disclosure;
fig. 3 is a schematic flow chart of an implementation of determining a background environment type of the radar clutter according to an embodiment of the present application;
fig. 4 is a schematic flow chart illustrating an implementation of another target detection method according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an object detection apparatus according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
In the drawings, like parts are designated with like reference numerals, and the drawings are not drawn to scale.
Detailed Description
In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described in further detail with reference to the attached drawings, the described embodiments should not be considered as limiting the present application, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.
The following description will be added if a similar description of "first \ second \ third" appears in the application file, and in the following description, the terms "first \ second \ third" merely distinguish similar objects and do not represent a specific ordering for the objects, and it should be understood that "first \ second \ third" may be interchanged under certain circumstances in a specific order or sequence, so that the embodiments of the application described herein can be implemented in an order other than that shown or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.
The embodiment of the application provides a target detection method, and the method is applied to electronic equipment. In this embodiment, the electronic device may be a receiver of a radar, and the functions implemented by the target detection method provided in this embodiment may be implemented by a processor of the electronic device calling a program code, where the program code may be stored in a computer storage medium. An embodiment of the present application provides a target detection method, and fig. 1 is a schematic flow chart illustrating an implementation of the target detection method provided in the embodiment of the present application, and as shown in fig. 1, the method includes:
and step S101, radar echo data are obtained.
In the embodiment of the present application, the radar echo data generally includes radar clutter data and data of a target object. In the embodiment of the application, the radar echo data is sent to the electronic device by a radar, so that the electronic device acquires the radar echo data.
And step S102, determining the clutter type of the radar clutter in the radar echo data.
In the embodiment of the application, after the electronic equipment receives radar echo data, radar clutter data in the radar echo data are determined; determining clutter classification features of the radar clutter based on the radar clutter data; determining a clutter type of the radar clutter based on the clutter classification characteristic.
In an embodiment of the application, the determining of the clutter classification characteristic of the radar clutter based on the radar clutter data includes: determining clutter sample data based on the radar clutter data; determining a sample feature function based on the clutter sample data, wherein the sample feature function comprises: clutter classification features; determining a clutter classification feature based on the sample feature function.
In an embodiment of the present application, the clutter types include at least one of: sea clutter, ground clutter.
Step S103, determining a corresponding target detection algorithm at least based on the clutter type.
In the embodiment of the application, the detection algorithms corresponding to different clutter types are different; after the clutter type is determined, a target detection algorithm corresponding to the clutter type may be determined based on a pre-stored correspondence between the clutter type and the detection algorithm. The method comprises the steps that the corresponding relation between sea clutter and a self-adaptive matched filtering algorithm is stored in the electronic equipment in advance, various detection algorithms are stored in advance aiming at the ground clutter, and when the ground clutter is determined, the background environment type of the radar clutter can be determined; and determining a corresponding target detection algorithm based on the clutter type and the background environment type. The ground clutter corresponds to a plurality of detection algorithms which may include: a unit average constant false alarm rate detection algorithm, a two-side unit average big-selection constant false alarm rate detection algorithm, a selection constant false alarm rate detection algorithm and the like. In the embodiment of the present application, the selecting the constant false alarm detection algorithm may be a minimum selecting constant false alarm detection algorithm.
And step S104, performing target detection on the radar echo data based on the target detection algorithm.
In the embodiment of the application, after the target detection algorithm is determined, the target detection can be performed on the radar echo data based on the target detection algorithm to detect the target.
According to the target detection method, when radar echo data are acquired, the clutter type of radar clutter in the radar echo data is determined; determining a corresponding target detection algorithm at least based on the clutter types, wherein the detection algorithms corresponding to different clutter types are different; therefore, target detection is carried out on the radar echo data based on a target detection algorithm, the detection performance of the electronic equipment is improved, the detection flexibility is improved, and a target can be detected accurately.
In some embodiments, the step S102 "determining a clutter type of radar clutter in the radar echo data" may be implemented by:
and step S1021, determining radar clutter data in the radar echo data.
In the embodiment of the application, radar clutter data can be extracted from radar echo data.
Step S1022, determining a clutter classification characteristic of the radar clutter based on the radar clutter data.
In an embodiment of the present application, a sample feature function may be determined based on radar clutter data, wherein the sample feature function includes: clutter classification features; in the embodiment of the present application, the sample feature function may be a model of Alpha stable distribution, Alpha stable distribution Sα(σ, β, μ) has 4 parameters: characteristic parameter alpha, scale parameter sigma, deflection parameter beta and displacement parameter mu. The characteristic parameter alpha and the scale parameter sigma can be used as clutter classification characteristics.
And step S1023, determining the clutter type of the radar clutter based on the clutter classification characteristic.
In the embodiment of the application, the corresponding relation between the clutter classification characteristics and the clutter types can be pre-established, the clutter classification characteristics of different clutter are different generally, and the radar clutter data can be determined to be Rayleigh distribution, Weibull distribution, logarithm positive-phase distribution, K distribution and the like through the clutter classification characteristics. Wherein, if the distribution is judged to be Rayleigh distribution, the ground clutter is obtained; if the data is judged to be Weibull distribution, logarithm positive-phase distribution and K distribution, the data is the sea clutter.
In some embodiments, the step S1022 "determining the clutter classification characteristic of the radar clutter based on the radar clutter data" may be implemented by the following steps, and fig. 2 is a schematic flow chart of determining the clutter classification characteristic of the radar clutter based on the radar clutter data according to an embodiment of the present application, as shown in fig. 2, including:
step S1, based on the radar clutter data, determining clutter sample data.
In the embodiment of the application, sample data in radar clutter data can be extracted, and is represented by x.
Step S2, determining a sample feature function based on the clutter sample data, wherein the sample feature function includes: and (4) clutter classification characteristics.
In the embodiment of the present application, the sample feature function is described by taking an Alpha stable distribution as an example, and the Alpha stable distribution Sα(σ, β, μ), the characteristic function of which is seen in equation (1),
wherein,
is a sign function.
In the embodiment of the application, the property of the tail of the stable distribution can be determined through a sample characteristic function.
If X to Sα(σ,β,μ),0<α<2. Then equation (2) holds, see equation (2):
wherein,
when β ═ 1, the tail of the distribution satisfies formula (3), see formula (3):
complex ground and sea clutter have significant "clutter spikes", often causing the probability density function to have a heavy tail with statistical features that deviate heavily from gaussian distributions. Alpha stable distribution is a statistical model for describing good distribution of heavy tails, and a wide range of non-Gaussian characteristics can be described by changing characteristic parameters of the Alpha stable distribution. The key for distinguishing different types of clutter is the tailing part of distribution, and the tailing part of the distribution is determined by the model parameters alpha and sigma known by formula (3), so that alpha and sigma can be used as clutter classification features for distinguishing different types of clutter.
Step S3, determining clutter classification features based on the sample feature function.
In the embodiment of the application, the sample characteristic function method is adopted to estimate Alpha stable distribution parameters, and the clutter sequence x adopts the following estimation methodi{1 ≦ i ≦ N }, calculating the sample feature function, see equation (4):
the logarithm ψ (w) is taken at both sides of equation (1) to ln (Φ (w)), and the real part and imaginary part thereof are respectively referred to equation (5):
taking logarithm of two sides of the formula (5) to obtain a formula (6):
the estimation of the parameters, α and σ, is achieved by linear regression from equation (6), and the values of α and σ are determined. Thereby determining clutter classification characteristics.
In some embodiments, when the clutter type comprises clutter, the method further comprises:
and step S105, determining the background environment type of the radar clutter.
In the embodiment of the application, a reference point of the radar clutter can be determined; determining a first reference window and a second reference window in the sliding window based on the reference point, wherein the first reference window and the second reference window are positioned on two sides of the reference point; determining a first uniform degree value of the clutter background of the first reference window, and determining a second uniform degree value of the clutter background of a second reference window; determining the background environment type based on the first and second uniformity values.
In the embodiment of the application, the uniformity degree of the first reference window and the second reference can be determined through the first uniformity degree value and the second uniformity degree value, and whether the first uniformity degree value is the same as the second uniformity degree value is also determined. The background environment types include: a first type, a second type, and a third type. The clutter background of the first reference window is represented to be uniform by the first uniformity degree value, the clutter background of the second reference window is represented to be uniform by the second uniformity degree value, and the first type is determined under the condition that the first uniformity degree value is equal to the second uniformity degree value; the second type is determined under the condition that the first uniformity degree value represents that the clutter background of the first reference window is uniform, the second uniformity degree value represents that the clutter background of the second reference window is uniform, and the first uniformity degree value and the second uniformity degree value are not equal; the third type if the first homogeneity degree value characterizes clutter background non-uniformity of a first reference window and/or the second homogeneity degree value characterizes clutter background non-uniformity of a second reference window.
In this embodiment of the application, the step S103 of determining a corresponding target detection algorithm based on at least the clutter type may be implemented by:
and step S1031, determining a corresponding target detection algorithm based on the clutter type and the background environment type.
In the embodiment of the application, when the clutter type is determined to be the ground clutter, the clutter background environment needs to be determined, and in the embodiment of the application, different clutter background types can be set to correspond to different detection algorithms. When the background environment type is the first type, the target detection algorithm is a unit average constant false alarm rate detection algorithm; when the background environment type is the second type, the target detection algorithm is a constant false alarm rate detection algorithm for averagely selecting large units on two sides; and when the background environment type is a third type, selecting a constant false alarm rate detection algorithm as the target detection algorithm.
In some embodiments, in step S105, determining the background type of the radar clutter may be implemented by the following steps, and fig. 3 is a schematic flow chart of an implementation of determining the background type of the radar clutter according to an embodiment of the present application, as shown in fig. 3:
and step S1051, determining a reference point of the radar clutter.
In the embodiment of the present application, let xiIs the ith reference point of the N reference sample units within the reference sliding window.
Step S1052, determining a first reference window and a second reference window in the sliding window based on the reference point, wherein the first reference window and the second reference window are located on both sides of the reference point;
in an embodiment of the present application, after the reference point is determined, the first reference window and the second reference window may be determined.
Step S1053, determining a first uniform degree value of the clutter background of the first reference window, and determining a second uniform degree value of the clutter background of the second reference window.
In the embodiment of the present application, the utilization statistic V can be utilizedviTo measure the uniformity of background noise in the first and second reference windows, using the statistic VmrTo check whether the first reference window and the second reference window have the same statistical mean.
In the examples of this application, VviSee (7):
in the examples of this application, VmrSee (8):
in the formula
A reference cell sample mean;
XA: a sample mean of a first reference window;
XB: a sample mean of a second reference window;
step S1054, determining the background environment type based on the first and second uniformity degree values.
In the embodiment of the present application, after the first uniform degree value and the second uniform degree value are determined, the clutter background may be determined, and the determination is performed in the following manner, see formula (9) and formula (10).
In the formula Kvi: statistic VviThe threshold of (2);
Kmr: statistic VmrThe threshold of (2);
Kvi、Kmris set in relation to the desired false alarm probability.
In the embodiment of the application, a corresponding target detection algorithm is determined based on the clutter type and the background environment type. In the embodiment of the application, the conditions that the reference sliding window on one side is uniform and the reference sliding window on the other side is not uniform and the reference sliding windows on both sides are not uniform are all performed by adopting an S-CFAR method, while the original detection method of VI-CFAR is still adopted in other conditions, in addition, the VI frame is simplified according to engineering practice, and the adaptive strategy selection table 1 shows that:
table 1 is another algorithm selection table provided in the embodiment of the present application;
based on the foregoing embodiments, an embodiment of the present application further provides a target detection method, and fig. 4 is a schematic flow chart illustrating an implementation of another target detection method provided in the embodiment of the present application, as shown in fig. 4:
step S401, receiving radar echo data;
and step S402, judging a clutter environment.
And estimating Alpha stable distribution model parameters of the echo sequence according to the acquired radar clutter data, and taking the estimated Alpha stable distribution model parameters as clutter classification characteristics to establish a corresponding relation between the Alpha stable distribution parameters and clutter types and distinguish the ground/sea clutter.
In the embodiment of the present application, when the signal is ground clutter, step S403 is performed, and when the signal is ground clutter, step S404 is performed.
In the embodiment of the application, after the type of the clutter is judged, the clutter is suppressed by means of pulse compression, MTI, MTD and the like.
And step S403, performing multi-strategy adaptive target detection algorithm to perform target identification.
In step S404, the AMF target detection algorithm performs target recognition.
Step S405, whether the target exists.
According to the target detection algorithm provided by the embodiment of the application, the covariance is estimated by using the sample of the reference unit, so that the calculation complexity in processing actual data is reduced, and the real-time performance of the algorithm is improved. Compared with the traditional target detection technology under the single clutter environment, the method has the advantages of greatly enhancing the environmental adaptability of the radar detection target while detecting the target to the maximum extent, along with low cost, simplicity, easiness in implementation and the like.
In the embodiment of the application, an Adaptive Matched Filter (AMF) detector is mainly used for constructing likelihood ratio test, echo power after whitening and coherent accumulation is compared with a threshold to judge a target, and the target is arranged in a complex observation vector z ═ zj+jzdCarry out hypothesis testing on H0Under the assumption that the data only contains sea clutter at H1Under the assumption that the data comprises a target s and a sea clutter, the corresponding binary assumption is as follows:
wherein d is the sea clutter vector, s is the target signal vector, and s ═ χ p. χ is an unknown constant associated with the target RCS, and p is the target steering vector.
The AMF detector has the structure that:
The target detection method provided by the embodiment of the application aims at the problem that the existing CFAR target detection technology is poor in environmental adaptation, and has the following beneficial effects:
the traditional VI algorithm is simplified, the application in engineering is facilitated, the target detection function in the ground clutter environment is improved, and the influence of factors such as multi-target interference and clutter edges can be well overcome.
The automatic judgment of two clutter modes of the ground clutter and the sea clutter can be realized, and the radar automation and the improvement of the adaptability of the radar in a complex environment are further realized.
By fusing a plurality of effective classical detection methods and utilizing clutter background judgment, the accurate application of a target detection method is realized, and the effect of automatically switching different target detection means under different clutter modes is achieved.
By selecting and integrating various classical target detection algorithms, the effect of improving the adaptability of the radar in a complex environment can be achieved.
Aiming at target detection in the ground clutter environment, the traditional VI algorithm framework is simplified, and the method has a good detection effect on the conditions of clutter uniform background, multi-interference targets, clutter edges and the like in the ground clutter environment.
Under the judgment of different scenes and different environments, a better target detection method is adopted, the automatic judgment of two modes of the ground clutter and the sea clutter is realized, and the suitable target detection method is automatically switched according to the different modes.
Based on the foregoing embodiments, the present application provides an object detection apparatus, where the apparatus includes modules and units included in the modules, and the modules and the units may be implemented by a processor in a computer device; of course, the implementation can also be realized through a specific logic circuit; in the implementation process, the processor may be a Central Processing Unit (CPU), a Microprocessor Unit (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like.
An embodiment of the present application provides a target detection apparatus, and fig. 5 is a schematic structural diagram of the target detection apparatus provided in the embodiment of the present application, and as shown in fig. 5, a target detection apparatus 500 includes:
an obtaining module 501, configured to obtain radar echo data;
a first determining module 502 for determining a clutter type of radar clutter in the radar echo data;
a second determining module 503, configured to determine a corresponding target detection algorithm based on at least the clutter types, where the detection algorithms corresponding to different clutter types are different;
a detection module 504, configured to perform target detection on the radar echo data based on the target detection algorithm.
In some embodiments, the first determining module 502 comprises:
a first determination unit configured to determine radar clutter data in the radar echo data;
a second determination unit configured to determine a clutter classification characteristic of the radar clutter based on the radar clutter data;
a third determining unit, configured to determine a clutter type of the radar clutter based on the clutter classification characteristic.
In some embodiments, the second determination unit comprises:
a first determining subunit, configured to determine clutter sample data based on the radar clutter data;
a second determining subunit, configured to determine a sample feature function based on the clutter sample data, wherein the sample feature function includes: clutter classification features;
a third determining subunit for determining a clutter classification feature based on the sample feature function.
In some embodiments, the clutter types include sea clutter, and the second determination module 503 is configured to determine an adaptive matched filtering algorithm based on the sea clutter.
In some embodiments, the clutter types include ground clutter, the target detection apparatus 500 further comprising:
a third determination module, configured to determine a background environment type of the radar clutter;
the second determining module 503 is further configured to determine a corresponding target detection algorithm based on the clutter type and the background environment type.
In some embodiments, the third determining module comprises:
a fourth determination unit configured to determine a reference point of the radar clutter;
a fifth determining unit, configured to determine a first reference window and a second reference window in the sliding window based on the reference point, where the first reference window and the second reference window are located on two sides of the reference point;
a sixth determining unit, configured to determine a first uniform degree value of the clutter background of the first reference window, and determine a second uniform degree value of the clutter background of the second reference window;
a seventh determining unit, configured to determine the background environment type based on the first uniformity degree value and the second uniformity degree value.
In some embodiments, the context types include: a first type, a second type, and a third type;
wherein the first uniformity value represents that the clutter background of the first reference window is uniform, the second uniformity value represents that the clutter background of the second reference window is uniform, and the first type is the first type when the first uniformity value and the second uniformity value are equal;
the second type is determined under the condition that the first uniformity degree value represents that the clutter background of the first reference window is uniform, the second uniformity degree value represents that the clutter background of the second reference window is uniform, and the first uniformity degree value and the second uniformity degree value are not equal;
the third type if the first homogeneity degree value characterizes clutter background non-uniformity of a first reference window and/or the second homogeneity degree value characterizes clutter background non-uniformity of a second reference window.
In some embodiments, when the background environment type is the first type, the target detection algorithm is a cell average constant false alarm detection algorithm;
when the background environment type is the second type, the target detection algorithm is a constant false alarm rate detection algorithm for averagely selecting large units on two sides;
and when the background environment type is a third type, selecting a constant false alarm rate detection algorithm as the target detection algorithm.
It should be noted that, in the embodiment of the present application, if the target detection method is implemented in the form of a software functional module and sold or used as a standalone product, the target detection method may also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or portions thereof contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, or an optical disk. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
Accordingly, an embodiment of the present application provides a storage medium, on which a computer program is stored, wherein the computer program is implemented to implement the steps in the object detection method provided in the above embodiment when executed by a processor.
The embodiment of the application provides an electronic device; fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 6, the electronic device 600 includes: a processor 601, at least one communication bus 602, a user interface 603, at least one external communication interface 604, memory 605. Wherein the communication bus 602 is configured to enable connective communication between these components. The user interface 603 may comprise a display screen, and the external communication interface 604 may comprise a standard wired interface and a wireless interface, among others. The processor 601 is configured to execute a program of the object detection method stored in the memory to implement the steps in the object detection method provided in the above-described embodiments.
The above description of the display device and storage medium embodiments is similar to the description of the method embodiments above, with similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the computer device and the storage medium of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.
Here, it should be noted that: the above description of the storage medium and device embodiments is similar to the description of the method embodiments above, with similar advantageous effects as the method embodiments. For technical details not disclosed in the embodiments of the storage medium and apparatus of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.
Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a controller to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
The above description is only for the embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (11)
1. A method of object detection, the method comprising:
acquiring radar echo data;
determining a clutter type of radar clutter in the radar echo data;
determining a corresponding target detection algorithm at least based on the clutter types, wherein the detection algorithms corresponding to different clutter types are different;
and performing target detection on the radar echo data based on the target detection algorithm.
2. The method of claim 1, wherein the determining a clutter type for radar clutter in the radar echo data comprises:
determining radar clutter data in the radar echo data;
determining clutter classification features of the radar clutter based on the radar clutter data;
determining a clutter type of the radar clutter based on the clutter classification characteristic.
3. The method of claim 2, wherein the determining clutter classification features for the radar clutter based on the radar clutter data comprises:
determining clutter sample data based on the radar clutter data;
determining a sample feature function based on the clutter sample data, wherein the sample feature function comprises: clutter classification features;
determining a clutter classification feature based on the sample feature function.
4. The method of claim 1, wherein the clutter type comprises sea clutter, and wherein determining a corresponding target detection algorithm based at least on the clutter type comprises:
and determining a self-adaptive matched filtering algorithm based on the sea clutter.
5. The method of claim 1, wherein the clutter type comprises ground clutter, the method further comprising:
determining a background environment type of the radar clutter;
determining a corresponding target detection algorithm based at least on the clutter type, comprising:
and determining a corresponding target detection algorithm based on the clutter type and the background environment type.
6. The method of claim 5, wherein the determining the background type of the radar clutter comprises:
determining a reference point for the radar clutter;
determining a first reference window and a second reference window in the sliding window based on the reference point, wherein the first reference window and the second reference window are positioned on two sides of the reference point;
determining a first uniform degree value of the clutter background of the first reference window, and determining a second uniform degree value of the clutter background of a second reference window;
determining the background environment type based on the first and second uniformity values.
7. The method of claim 6, wherein the context types comprise: a first type, a second type, and a third type;
wherein the first uniformity value represents that the clutter background of the first reference window is uniform, the second uniformity value represents that the clutter background of the second reference window is uniform, and the first type is the first type when the first uniformity value and the second uniformity value are equal;
the second type is determined under the condition that the first uniformity degree value represents that the clutter background of the first reference window is uniform, the second uniformity degree value represents that the clutter background of the second reference window is uniform, and the first uniformity degree value and the second uniformity degree value are not equal;
the third type if the first homogeneity degree value characterizes clutter background non-uniformity of a first reference window and/or the second homogeneity degree value characterizes clutter background non-uniformity of a second reference window.
8. The method of claim 7,
when the background environment type is the first type, the target detection algorithm is a unit average constant false alarm rate detection algorithm;
when the background environment type is the second type, the target detection algorithm is a constant false alarm rate detection algorithm for averagely selecting large units on two sides;
and when the background environment type is a third type, selecting a constant false alarm rate detection algorithm as the target detection algorithm.
9. An object detection device, comprising:
the acquisition module is used for acquiring radar echo data;
the first determination module is used for determining a clutter type of radar clutter in the radar echo data;
a second determining module, configured to determine a corresponding target detection algorithm based on at least the clutter types, where the detection algorithms corresponding to different clutter types are different;
and the detection module is used for carrying out target detection on the radar echo data based on the target detection algorithm.
10. An electronic device, comprising a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, performs the object detection method of any one of claims 1 to 8.
11. A storage medium storing a computer program, the storage medium, when executed, for implementing the object detection method of any one of claims 1 to 8.
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