Low, slow and small radar target detection method, device and equipment and storage medium

Abstract

The invention relates to the technical field of low, slow and small radars, and provides a low, slow and small radar target detection method, device and equipment and a storage medium, and the method comprises the steps: obtaining a pulse pressure echo signal of a low, slow and small radar; for each range gate of the pulse pressure echo signal, judging whether the echo intensity of the range gate is greater than a threshold value of the range gate corresponding to the clutter map; wherein the clutter map is a dynamic clutter map; if the distance gate is smaller than the target distance gate, the corresponding distance gate is removed, and the remaining distance gate is used as the target distance gate; respectively performing low-order fitting on the real part and the imaginary part of the azimuth sampling signal of each target range gate to obtain a real part fitting signal and an imaginary part fitting signal; for each target range gate, subtracting the corresponding real part fitting signal and imaginary part fitting signal from the azimuth sampling signal of the target range gate in the time domain to obtain a target pulse pressure echo signal; and performing target detection based on the target pulse pressure echo signal. According to the invention, the accuracy of low-speed small target detection in a complex environment can be improved, and the operation complexity can be reduced.

Description

Technical Field

[0001] The present application relates to the technical field of low, slow and small radars, and in particular to a low, slow and small radar target detection method, device, equipment and storage medium. Background Art

[0002] A low-speed, slow, and small radar is a radar system specifically used to detect and track low-altitude, slow, and small targets. The accuracy of low-speed, slow, and small target detection is affected by clutter. In order to improve the speed and effect of clutter suppression, the existing method screens out target range gates with strong ground clutter based on statistical energy and fixed empirical threshold values, and then extracts clutter by low-order fitting based on the screened target range gates, and finally suppresses the extracted clutter. However, the inventors found that this method only screens out range gates with strong ground clutter for clutter suppression, and does not consider other range gates with non-strong ground clutter and suppress them. In a complex clutter environment, the detection accuracy of the target signal after its clutter suppression is poor. If the accuracy of its target detection is to be improved, the clutter signals of other non-strong ground clutter range gates must be analyzed and suppressed, and the operation is complicated. Therefore, how to improve it, improve the detection accuracy of low-speed, slow, and small targets in complex environments, and reduce the complexity of operation is a technical problem that needs to be solved urgently. Summary of the invention

[0003] In response to the above technical problems, the purpose of this application is to provide a low, slow and small radar target detection method, device, equipment and storage medium, aiming to improve the detection accuracy of low, slow and small targets in complex environments and reduce the complexity of operation.

[0004] The present application provides a method for detecting a low, slow, and small radar target, the method comprising:

[0005] Get the pulse pressure echo signal of the slow and small radar;

[0006] For each range gate of the pulse pressure echo signal, determine whether its echo intensity is greater than a threshold value of the range gate corresponding to the clutter map; wherein the clutter map is a dynamic clutter map;

[0007] If it is less than, the corresponding range gate is eliminated and the remaining range gate is used as the target range gate;

[0008] Performing low-order fitting on the real part and the imaginary part of the azimuth sampling signal of each target range gate to obtain a real part fitting signal and an imaginary part fitting signal;

[0009] For each of the target range gates, in the time domain, the corresponding real part fitting signal and imaginary part fitting signal are subtracted from the azimuth sampling signal to obtain the target pulse pressure echo signal;

[0010] Target detection is performed based on the target pulse pressure echo signal.

[0011] Further, the threshold value of each distance gate is equal to the mean clutter intensity stored in each azimuth - distance gate in the clutter map multiplied by a preset threshold factor.

[0012] Further, the steps of performing target detection based on the target pulse - compressed echo signal include:

[0013] Determine the speed, position, shape, and size of the target based on the target pulse - compressed echo signal;

[0014] Input the shape and size of the target into a preset target recognition model, and output a first target type and a second target type; wherein, the first target type is the target type corresponding to the maximum confidence level, and the second target type is the target type with the second - largest confidence level;

[0015] Compare the confidence level of the first target type with the confidence level of the second target type. If the absolute value of their difference is greater than or equal to a preset threshold, then use the target type corresponding to the first - ranked confidence level as the detection result;

[0016] If the absolute value of their difference is less than the preset threshold, then obtain the longitude and latitude information of each of the second target distance gates;

[0017] Determine the target smart phone based on the longitude and latitude information;

[0018] Send a low - slow - small target type input request and a photo - taking request to the target smart phone;

[0019] Receive the low - slow - small target type input by the smart phone and the image obtained by photographing the low - altitude area of the position where the target is located;

[0020] Compare the first target type, the second target type with the low - slow - small target type;

[0021] If the first target type is the same as the low - slow - small target type, then use the first target type as the final detection result;

[0022] If the second target type is the same as the low - slow - small target type, then use the second target type as the final detection result;

[0023] If the first target type, the second target type are both different from the low - slow - small target type, then use the first target type as the final detection result.

[0024] Further, the steps of performing target detection based on the target pulse - compressed echo signal include:

[0025] Determine the speed, position, shape, and size of the target based on the target pulse - compressed echo signal;

[0026] Input a preset target recognition model based on the shape and size of the target, and output a first target type and a second target type; wherein, the first target type is the target type corresponding to the maximum confidence level, and the second target type is the target type with the second-largest confidence level;

[0027] Compare the confidence level of the first target type with the confidence level of the second target type. If the absolute value of their difference is greater than or equal to a preset threshold, then use the target type corresponding to the first-ranked confidence level as the detection result;

[0028] If the absolute value of their difference is less than the preset threshold, then obtain the latitude and longitude information of the location where the target is located;

[0029] Determine the target smart phone based on the latitude and longitude information;

[0030] Send a photo-taking request message to the target smart phone;

[0031] Receive the image obtained by the smart phone taking a photo of the low-altitude area where the target is located;

[0032] Identify the low, slow, and small targets in the image to determine the third target type;

[0033] Compare the first target type, the second target type, and the third target type;

[0034] If the first target type is the same as the third target type, then use the first target type as the final detection result;

[0035] If the second target type is the same as the third target type, then use the second target type as the final detection result;

[0036] If the first target type, the second target type, and the third target type are all different, then use the first target type as the final detection result.

[0037] Furthermore, the steps of performing target detection based on the target pulse compression echo signal further include:

[0038] Determine the speed, position, shape, and size of the target based on the target pulse compression echo signal;

[0039] Input a preset target recognition model based on the shape and size of the target, and output a first target type and a second target type; wherein, the first target type is the target type corresponding to the maximum confidence level, and the second target type is the target type with the second-largest confidence level;

[0040] Compare the confidence level of the first target type with the confidence level of the second target type. If the absolute value of their difference is greater than or equal to a preset threshold, then use the target type corresponding to the first-ranked confidence level as the detection result;

[0041] If the absolute value of the difference is less than a preset threshold, obtain the latitude and longitude information of the location where the target is located;

[0042] Determine the target smart phone based on the latitude and longitude information;

[0043] Determine the target intelligent monitoring system based on the latitude and longitude information;

[0044] Control the intelligent monitoring system to capture the low-altitude area of the location where the target is located to obtain a low-altitude area image;

[0045] Identify the low, slow, and small targets in the low-altitude area image to determine the third target type;

[0046] Compare the first target type, the second target type, and the third target type;

[0047] If the first target type is the same as the third target type, use the first target type as the final detection result;

[0048] If the second target type is the same as the third target type, use the second target type as the final detection result;

[0049] If the first target type, the second target type, and the third target type are all different, use the first target type as the final detection result.

[0050] Further, before the step of obtaining the pulse compression echo signal of the low, slow, and small radar, the method further includes:

[0051] Obtain the echo signal received by the low, slow, and small radar;

[0052] Mix the echo signal with the transmitted signal to generate an intermediate frequency signal;

[0053] Perform low-pass filtering on the intermediate frequency signal to obtain a low-pass filtered intermediate frequency signal;

[0054] Determine whether an acceleration signal is detected;

[0055] If not detected, sample and digitize the low-pass filtered intermediate frequency signal to obtain a sampled signal;

[0056] Process the sampled signal using a digital matched filter to obtain a pulse compression echo signal;

[0057] If detected, perform adaptive filtering, spectral subtraction processing, sparse coding, and reconstruction on the acceleration signal and the low-pass filtered intermediate frequency signal in sequence to obtain an intermediate frequency signal with motion interference removed;

[0058] Sample and digitize the intermediate frequency signal with motion interference removed to obtain a sampled signal;

[0059] Process the sampled signal using a digital matched filter to obtain a pulse compression echo signal.

[0060] In one embodiment, the step of determining whether an acceleration signal is detected includes:

[0061] Install a three-axis acceleration sensor on the low, slow, and small radar;

[0062] Determine whether an acceleration signal is detected through the three-axis acceleration sensor.

[0063] In a second aspect, an embodiment of the present application provides a low, slow, and small radar target detection device, and the device includes:

[0064] An acquisition module for acquiring the pulse compression echo signal of the low, slow, and small radar;

[0065] A judgment module for judging whether the echo intensity of each range gate of the pulse compression echo signal is greater than the threshold value of the corresponding range gate of the clutter map; wherein, the clutter map is a dynamic clutter map;

[0066] An elimination module for eliminating the corresponding range gate if it is less, and using the remaining range gates as target range gates;

[0067] A low-order fitting module for respectively performing low-order fitting on the real part and the imaginary part of the azimuth sampling signal of each target range gate to obtain a real part fitting signal and an imaginary part fitting signal;

[0068] A subtraction module for subtracting the corresponding real part fitting signal and imaginary part fitting signal from the azimuth sampling signal in the time domain for each target range gate to obtain a target pulse compression echo signal;

[0069] A target detection module for performing target detection based on the target pulse compression echo signal.

[0070] In a third aspect, an embodiment of the present application provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the low, slow, and small radar target detection method described in any one of the above are implemented.

[0071] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the low, slow, and small radar target detection method described in any one of the above are implemented.

[0072] The low, slow, and small radar target detection method provided by the embodiments of the present application determines whether the echo intensity of each range gate of the pulse compression echo signal is greater than the threshold value of the corresponding range gate of the dynamic clutter map. By means of the threshold value, it can be determined which range gates contain targets, and the range gates that do not contain targets can be removed, which can greatly reduce the subsequent calculation amount and improve the calculation speed. When using the dynamic clutter map, since the dynamic clutter map adjusts the threshold value according to real-time environmental information, it can more accurately distinguish the range gates that only contain clutter and the range gates that contain targets, improving the detection accuracy of low, slow, and small targets in complex environments. The present invention does not need to distinguish between strong ground clutter range gates and non-strong ground clutter range gates, and the operation is simple. The real part and the imaginary part of the azimuth sampling signal of each target range gate are respectively subjected to low-order fitting to obtain a real part fitting signal and an imaginary part fitting signal. For each target range gate, in the time domain, its azimuth sampling signal is subtracted from the corresponding real part fitting signal and imaginary part to remove interfering clutter, making the target signal more prominent and pure, and then the detection accuracy of low, slow, and small targets based on the target pulse compression echo signal is higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions of the present application, the drawings required for the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0074] Figure 1 is a schematic flowchart of the low, slow, and small radar target detection method provided by the embodiments of the present application;

[0075] Figure 2 is a schematic structural diagram of the low, slow, and small radar target detection device provided by the embodiments of the present application;

[0076] Figure 3 is a schematic block diagram of the structure of the computer device provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0077] In order to make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0078] Those skilled in the art can understand that, unless specifically stated otherwise, the singular forms "a", "an", "the above" and "the" used herein may also include the plural forms. It should be further understood that the term "including" used in the specification of this application means the presence of features, integers, steps, operations, elements, modules and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, components and / or their groups. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The phrase "and / or" used herein includes all or any module and all combinations of one or more associated listed items.

[0079] Those skilled in the art can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as the general understanding of those of ordinary skill in the art to which this application belongs. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless specifically defined as here.

[0080] Please refer to Figure 1 , an embodiment of this application provides a method for detecting low, slow and small radar targets, and the method includes:

[0081] S1. Obtain the pulse compression echo signal of the low, slow and small radar.

[0082] In step S1, the pulse compression echo signal is a signal obtained by performing pulse compression processing on the echo signal received by the low, slow and small radar. Pulse compression can improve the detection range and range resolution.

[0083] S2. For each range gate of the pulse compression echo signal, determine whether its echo intensity is greater than the threshold value of the corresponding range gate of the clutter map; wherein, the clutter map is a dynamic clutter map.

[0084] In step S2, the echo signal of the radar system is collected by the ADC. During the conversion process, the sampling rate discretizes the echo signal. For an ADC with a sampling rate of fs, its sampling time is Ts = 1 / fs. Corresponding to the radar system, the electromagnetic wave travels a certain distance within Ts, and this distance is called the range gate. The clutter map is a distribution map of clutter intensity within the radar's power range stored in the memory. The clutter map is stored in the form of range-azimuth units. The dynamic clutter map is a clutter map that can be automatically corrected and updated, and can continuously adapt to the changing environment. The construction and update methods of the clutter map are prior arts and will not be elaborated here. When the echo intensity of the pulse compression echo signal of the target range gate is greater than the threshold value of the corresponding range gate in the clutter map, it indicates that a target appears in this range gate; otherwise, no target appears in this range gate. The threshold values of each range gate are respectively equal to the mean value of the clutter intensity stored in each azimuth-range gate in the clutter map multiplied by a preset threshold factor.

[0085] S3. If it is less, then eliminate the corresponding range gate, and use the remaining range gates as the target range gates.

[0086] S4. Respectively perform low-order fitting on the real part and the imaginary part of the azimuth sampling signal of each said target range gate to obtain a real-part fitting signal and an imaginary-part fitting signal.

[0087] S5. For each said target range gate, in the time domain, subtract its corresponding real-part fitting signal and imaginary-part fitting signal from the azimuth sampling signal to obtain a target pulse compression echo signal;

[0088] S6. Perform target detection based on the said target pulse compression echo signal.

[0089] In steps S4, S5, and S6, respectively perform low-order fitting on the real part and the imaginary part of the azimuth sampling signal of each said target range gate, with the order being from 1 to 6, to obtain a real-part fitting signal and an imaginary-part fitting signal. For each said target range gate, in the time domain, subtract its corresponding real-part fitting signal and imaginary part from the azimuth sampling signal to remove the interfering clutter, making the target signal more prominent and pure, and thus the detection accuracy for low, slow, and small targets based on the said target pulse compression echo signal is higher.

[0090] The low, slow, and small radar target detection method provided by the embodiments of the present application determines, for each range gate of the pulse compression echo signal, whether its echo intensity is greater than the threshold value of the corresponding range gate of the dynamic clutter map. By means of the threshold value, it can be determined which range gates include targets, and the range gates that do not include targets can be removed, which can greatly reduce the subsequent calculation amount and improve the calculation speed. By using the dynamic clutter map, since the dynamic clutter map adjusts the threshold value according to real-time environmental information, it can more accurately distinguish the range gates that only include clutter and the range gates that include targets, and improve the detection accuracy of low, slow, and small targets in a complex environment. The present invention does not need to distinguish between strong ground clutter range gates and non-strong ground clutter range gates, and the operation is simple. The real part and the imaginary part of the azimuth sampling signal of each target range gate are respectively subjected to low-order fitting to obtain a real part fitting signal and an imaginary part fitting signal. For each target range gate, in the time domain, its azimuth sampling signal is subtracted from the corresponding real part fitting signal and imaginary part to remove interference clutter, making the target signal more prominent and pure, and further improving the detection accuracy of low, slow, and small targets based on the target pulse compression echo signal.

[0091] In one embodiment, the step of performing target detection based on the target pulse compression echo signal includes:

[0092] Determining the speed, position, shape, and size of the target based on the target pulse compression echo signal;

[0093] Inputting the shape and size of the target into a preset target recognition model and outputting a first target type and a second target type; wherein, the first target type is the target type corresponding to the maximum confidence level, and the second target type is the target type with the second largest confidence level;

[0094] Comparing the confidence level of the first target type with the confidence level of the second target type. If the absolute value of their difference is greater than or equal to a preset threshold, then the target type corresponding to the confidence level ranked first is used as the detection result;

[0095] If the absolute value of their difference is less than the preset threshold, then obtain the longitude and latitude information of the location where the target is located;

[0096] Determining the target smart phone based on the longitude and latitude information;

[0097] Sending a photo-taking request message to the target smart phone;

[0098] Receiving the image obtained by the smart phone taking a picture of the low-altitude area where the target is located;

[0099] Identifying the low, slow, and small targets in the image and determining a third target type;

[0100] Comparing the first target type, the second target type with the third target type;

[0101] If the first target type is the same as the third target type, then use the first target type as the final detection result;

[0102] If the second target type is the same as the third target type, then use the second target type as the final detection result;

[0103] If the first target type, the second target type and the third target type are all different, then use the first target type as the final detection result.

[0104] In the embodiments of the present application, the target recognition model is obtained by training a neural network, and its training data includes the shape and size of the target, as well as the corresponding target type labels. In the embodiments of the present application, the confidence difference between the first target type and the second target type is calculated, and different processing strategies are adopted according to the size of the confidence difference. When the confidence difference is large, directly use the target type with the highest confidence as the detection result, ensuring the detection efficiency when the target features are obvious; when the difference is small, introduce the image taken by the smart phone for further confirmation, making full use of external resources and improving the accuracy of target recognition in complex situations. This decision-making method that combines multi-source information makes full use of the advantages of various information, verifies and confirms the target type from multiple angles, improves the reliability and accuracy of the final detection result, and can better adapt to complex and changeable actual application scenarios.

[0105] In one embodiment, the steps of performing target detection based on the target pulse compression echo signal include:

[0106] Determine the speed, position, shape and size of the target based on the target pulse compression echo signal;

[0107] Input the shape and size of the target into a preset target recognition model, and output a first target type and a second target type; wherein, the first target type is the target type corresponding to the maximum confidence, and the second target type is the target type with the second highest confidence;

[0108] Compare the confidence of the first target type with the confidence of the second target type. If the absolute value of their difference is greater than or equal to a preset threshold, then use the target type corresponding to the highest confidence as the detection result;

[0109] If the absolute value of their difference is less than the preset threshold, then obtain the longitude and latitude information of each second target from the gate;

[0110] Determine the target smart phone based on the longitude and latitude information;

[0111] Send a request for inputting a low, slow and small target type and a photo-taking request to the target smart phone;

[0112] Receive the low, slow, and small target type input by the smart phone and the image obtained by photographing the low-altitude area where the target is located.

[0113] Compare the first target type and the second target type with the low, slow, and small target type.

[0114] If the first target type is the same as the low, slow, and small target type, use the first target type as the final detection result.

[0115] If the second target type is the same as the low, slow, and small target type, use the second target type as the final detection result.

[0116] If the first target type, the second target type, and the low, slow, and small target type are all different, use the first target type as the final detection result.

[0117] Compared with the previous embodiment, the present application sends a low, slow, and small target type input request to the target smart phone, which does not require image recognition and is simpler. At the same time, receiving the image obtained by the smart phone by photographing the low-altitude area where the target is located facilitates manual verification and traceability.

[0118] In one embodiment, the step of performing target detection based on the target pulse compression echo signal further includes:

[0119] Determine the speed, position, shape, and size of the target based on the target pulse compression echo signal.

[0120] Input the shape and size of the target into a preset target recognition model, and output a first target type and a second target type; wherein, the first target type is the target type corresponding to the maximum confidence, and the second target type is the target type with the second largest confidence.

[0121] Compare the confidence of the first target type and the confidence of the second target type. If the absolute value of their difference is greater than or equal to a preset threshold, use the target type corresponding to the first ranked confidence as the detection result.

[0122] If the absolute value of their difference is less than the preset threshold, obtain the longitude and latitude information of the location where the target is located.

[0123] Determine the target smart phone based on the longitude and latitude information.

[0124] Determine the target intelligent monitoring system based on the longitude and latitude information.

[0125] Control the intelligent monitoring system to photograph the low-altitude area where the target is located to obtain a low-altitude area image.

[0126] Identify the low, slow, and small targets in the low-altitude area image and determine the third target type;

[0127] Compare the first target type, the second target type, and the third target type;

[0128] If the first target type is the same as the third target type, use the first target type as the final detection result;

[0129] If the second target type is the same as the third target type, use the second target type as the final detection result;

[0130] If the first target type, the second target type, and the third target type are all different, use the first target type as the final detection result.

[0131] In the embodiments of the present application, the target recognition model is obtained by training a neural network, and its training data includes the shape and size of the target, as well as the corresponding target type labels. In the embodiments of the present application, the confidence difference between the first target type and the second target type is calculated, and different processing strategies are adopted according to the size of the confidence difference. When the confidence difference is large, the target type with the highest confidence is directly used as the detection result, ensuring the detection efficiency when the target features are obvious; when the difference is small, the image taken by the smartphone is introduced for further confirmation, making full use of external resources and improving the accuracy of target recognition in complex situations. This decision-making method that combines multi-source information makes full use of the advantages of various information, verifies and confirms the target type from multiple angles, improves the reliability and accuracy of the final detection result, and can better adapt to complex and changeable actual application scenarios.

[0132] In one embodiment, before the step of acquiring the pulse compression echo signal of the low, slow, and small radar, the method further includes:

[0133] Acquire the echo signal received by the low, slow, and small radar;

[0134] Mix the echo signal with the transmitted signal to generate an intermediate frequency signal;

[0135] Perform low-pass filtering on the intermediate frequency signal to obtain the low-pass filtered intermediate frequency signal;

[0136] Determine whether an acceleration signal is detected;

[0137] If not detected, sample and digitize the low-pass filtered intermediate frequency signal to obtain a sampled signal;

[0138] Process the sampled signal using a digital matched filter to obtain a pulse compression echo signal;

[0139] If detected, the acceleration signal and the intermediate frequency signal after low-pass filtering are used to perform adaptive filtering, spectral subtraction processing, sparse coding and reconstruction in sequence to obtain an intermediate frequency signal with motion interference removed;

[0140] Sampling and digitizing the intermediate frequency signal from which motion interference has been removed to obtain a sampled signal;

[0141] The sampled signal is processed using a digital matched filter to obtain a pulse compression echo signal.

[0142] In one embodiment, the step of determining whether an acceleration signal is detected includes:

[0143] Installing a three-axis acceleration sensor on the low and slow small radar;

[0144] It is determined whether an acceleration signal is detected by the three-axis acceleration sensor.

[0145] In an embodiment of the present application, a three-axis acceleration sensor is installed on the low, slow and small radar, so that when an acceleration signal is detected, the acceleration signal can be used for adaptive filtering, and then spectral subtraction processing, sparse coding and reconstruction can be performed to obtain an intermediate frequency signal with motion interference removed; adaptive filtering can effectively locate the frequency of motion interference, and preliminarily suppress the motion interference through spectral subtraction. Finally, sparse coding and reconstruction are used to suppress the main components other than the radar echo signal, thereby improving the suppression component of motion interference, and the motion interference effect is better, that is, it can suppress the interference signal introduced by radar vibration and shaking.

[0146] In one embodiment, the step of performing adaptive filtering using the acceleration signal and the intermediate frequency signal after low-pass filtering to obtain the intermediate frequency signal after adaptive filtering includes:

[0147] Assume that the intermediate frequency signal after low-pass filtering is x, the acceleration signal is a, the weight vector of the acceleration signal is b, whose dimension is equal to the number of channels of the acceleration signal, e is the error signal of the adaptive filter, μ adap is the step size for updating the adaptive filter weights, and ε is a very small positive number. When processing the i=1, ..., nth sample points of the signal to be analyzed, the error signal and weight of the adaptive filter are calculated using formula (1) and formula (2):

[0148] e(i) = x(i) - b T (i)a(i) (1)

[0149] b(i+1)=βb(i)+2μ adap / (ε+a T (i)a(i))a(i)e(i) (2)

[0150] After the adaptive filtering is completed, the error signal e of the adaptive filter is used as the intermediate frequency signal after adaptive filtering.

[0151] As Figure 2 shown, an embodiment of the present application further provides a low, slow, and small radar target detection device, and the device includes:

[0152] An acquisition module 1, configured to acquire the pulse compression echo signal of the low, slow, and small radar;

[0153] A judgment module 2, configured to judge, for each range gate of the pulse compression echo signal, whether its echo intensity is greater than the threshold value of the corresponding range gate of the clutter map; wherein, the clutter map is a dynamic clutter map;

[0154] A rejection module 3, configured to, if it is less, reject the corresponding range gate, and use the remaining range gates as target range gates;

[0155] A low-order fitting module 4, configured to perform low-order fitting on the real part and the imaginary part of the azimuth sampling signal of each of the target range gates respectively, to obtain a real part fitting signal and an imaginary part fitting signal;

[0156] A subtraction module 5, configured to, for each of the target range gates, subtract the corresponding real part fitting signal and imaginary part fitting signal from its azimuth sampling signal in the time domain, to obtain a target pulse compression echo signal;

[0157] A target detection module 6, configured to perform target detection based on the target pulse compression echo signal.

[0158] It should be noted that the above low, slow, and small radar target detection methods are all applicable to the low, slow, and small radar target detection system of the present invention. Therefore, the embodiments of the present invention will not be elaborated herein.

[0159] Referring to Figure 3 , an embodiment of the present application further provides a computer device, and the internal structure of the computer device may be as Figure 3As shown. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. Among them, the processor designed for the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating device, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data such as a method for detecting low, slow, and small radar targets. The network interface of the computer device is used to communicate with an external terminal via a network connection. Further, the above computer device may also be provided with an input device, a display screen, etc. The above computer program, when executed by the processor, implements a method for detecting low, slow, and small radar targets. Those skilled in the art can understand, Figure 3 The structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied.

[0160] An embodiment of this application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a method for detecting low, slow, and small radar targets.

[0161] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to the memory, storage, database, or other media provided in this application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchl ink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0162] It should be noted that in this text, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent in such a process, device, article or method. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, device, article or method comprising such element.

[0163] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A low, slow and small radar target detection method, characterized in that: The method comprises: Get the pulse pressure echo signal of the slow and small radar; For each range gate of the pulse pressure echo signal, determine whether its echo intensity is greater than a threshold value of the range gate corresponding to the clutter map; wherein the clutter map is a dynamic clutter map; If it is less than, the corresponding range gate is eliminated and the remaining range gate is used as the target range gate; Performing low-order fitting on the real part and the imaginary part of the azimuth sampling signal of each target range gate to obtain a real part fitting signal and an imaginary part fitting signal; For each of the target range gates, in the time domain, the corresponding real part fitting signal and imaginary part fitting signal are subtracted from the azimuth sampling signal to obtain the target pulse pressure echo signal; Target detection is performed based on the target pulse pressure echo signal.

2. The low, slow and small radar target detection method according to claim 1 is characterized in that: The threshold value of each range gate is equal to the average value of the clutter intensity stored in each direction-range gate in the clutter map multiplied by the preset threshold factor.

3. The low, slow and small radar target detection method according to claim 1 is characterized in that: The step of performing target detection based on the target pulse pressure echo signal comprises: Determine the speed, position, shape and size of the target based on the target pulse pressure echo signal; Inputting a preset target recognition model based on the shape and size of the target, and outputting a first target type and a second target type; wherein the first target type is the target type corresponding to the highest confidence level, and the second target type is the target type corresponding to the second highest confidence level; Compare the confidence of the first target type with the confidence of the second target type, and if the absolute value of the difference between them is greater than or equal to a preset threshold, take the target type corresponding to the confidence ranked first as the detection result; If the absolute value of the difference is less than a preset threshold, obtaining the longitude and latitude information of each of the second target range gates; Determine the target smartphone based on the latitude and longitude information; Sending a low, slow, small target type input request and a photo taking request to the target smartphone; Receiving the type of a low, slow, small target input by the smartphone and an image captured of a low-altitude area where the target is located; comparing the first target type, the second target type and the low, slow and small target type; If the first target type is the same as the low, slow and small target type, the first target type is used as the final detection result; If the second target type is the same as the low, slow and small target type, the second target type is used as the final detection result; If the first target type, the second target type and the low, slow and small target type are all different, the first target type is used as the final detection result.

4. The low, slow and small radar target detection method according to claim 1 is characterized in that: The step of performing target detection based on the target pulse pressure echo signal comprises: Determine the speed, position, shape and size of the target based on the target pulse pressure echo signal; Inputting a preset target recognition model based on the shape and size of the target, and outputting a first target type and a second target type; wherein the first target type is the target type corresponding to the highest confidence level, and the second target type is the target type corresponding to the second highest confidence level; Compare the confidence of the first target type with the confidence of the second target type, and if the absolute value of the difference between them is greater than or equal to a preset threshold, take the target type corresponding to the confidence ranked first as the detection result; If the absolute value of the difference is less than a preset threshold, the longitude and latitude information of the target location is obtained; Determine the target smartphone based on the latitude and longitude information; Sending a photo request message to the target smartphone; Receiving an image captured by the smart phone of a low-altitude area where the target is located; Identifying a low, slow, and small target in the image to determine a third target type; comparing the first target type, the second target type, and the third target type; If the first target type is the same as the third target type, taking the first target type as the final detection result; If the second target type is the same as the third target type, taking the second target type as the final detection result; If the first target type, the second target type and the third target type are all different, the first target type is taken as the final detection result.

5. The low, slow and small radar target detection method according to claim 1 is characterized in that: The step of performing target detection based on the target pulse pressure echo signal also includes: Determine the speed, position, shape and size of the target based on the target pulse pressure echo signal; Inputting a preset target recognition model based on the shape and size of the target, and outputting a first target type and a second target type; wherein the first target type is the target type corresponding to the highest confidence level, and the second target type is the target type corresponding to the second highest confidence level; Compare the confidence of the first target type with the confidence of the second target type, and if the absolute value of the difference between them is greater than or equal to a preset threshold, take the target type corresponding to the confidence ranked first as the detection result; If the absolute value of the difference is less than a preset threshold, the longitude and latitude information of the target location is obtained; Determine the target smartphone based on the latitude and longitude information; Determine a target intelligent monitoring system based on the latitude and longitude information; Controlling the intelligent monitoring system to shoot a low-altitude area where the target is located to obtain an image of the low-altitude area; Identifying a low, slow, small target in the low-altitude area image to determine a third target type; comparing the first target type, the second target type, and the third target type; If the first target type is the same as the third target type, taking the first target type as the final detection result; If the second target type is the same as the third target type, taking the second target type as the final detection result; If the first target type, the second target type and the third target type are all different, the first target type is taken as the final detection result.

6. The low, slow and small radar target detection method according to claim 1 is characterized in that: Before the step of obtaining the pulse pressure echo signal of the low, slow and small radar, the method further includes: Get the echo signal received by the low, slow and small radar; Mixing the echo signal with the transmission signal to generate an intermediate frequency signal; Performing low-pass filtering on the intermediate frequency signal to obtain an intermediate frequency signal after low-pass filtering; Determine whether an acceleration signal is detected; If it cannot be detected, sampling and digitizing the intermediate frequency signal after the low-pass filtering to obtain a sampled signal; Processing the sampled signal using a digital matched filter to obtain a pulse pressure echo signal; If detected, the acceleration signal and the intermediate frequency signal after low-pass filtering are used to perform adaptive filtering, spectral subtraction processing, sparse coding and reconstruction in sequence to obtain an intermediate frequency signal with motion interference removed; Sampling and digitizing the intermediate frequency signal from which motion interference has been removed to obtain a sampled signal; The sampled signal is processed using a digital matched filter to obtain a pulse compression echo signal.

7. The low, slow and small radar target detection method according to claim 6 is characterized in that: The steps of determining whether an acceleration signal is detected include: Installing a three-axis acceleration sensor on the low and slow small radar; It is determined whether an acceleration signal is detected by the three-axis acceleration sensor.

8. A low, slow and small radar target detection device, characterized in that: The device comprises: The acquisition module is used to acquire the pulse pressure echo signal of the low, slow and small radar; A judgment module, used for judging, for each range gate of the pulse pressure echo signal, whether its echo intensity is greater than a threshold value of the range gate corresponding to the clutter map; wherein the clutter map is a dynamic clutter map; The elimination module is used to eliminate the corresponding range gate if it is less than , and take the remaining range gate as the target range gate; A low-order fitting module, used for performing low-order fitting on the real part and the imaginary part of the azimuth sampling signal of each target range gate, respectively, to obtain a real part fitting signal and an imaginary part fitting signal; A subtraction module is used for, for each target range gate, subtracting the corresponding real part fitting signal and imaginary part fitting signal from its azimuth sampling signal in the time domain to obtain a target pulse pressure echo signal; The target detection module is used to perform target detection based on the target pulse pressure echo signal.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the low, slow and small radar target detection method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the low, slow and small radar target detection method as described in any one of claims 1 to 7 are implemented.

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