Clutter map constant false alarm target detection method and system, storage medium and computing device

Abstract

The invention relates to the field of radar target detection, and discloses a clutter map constant false alarm target detection method and system, a storage medium and computing equipment, and the method comprises the steps: carrying out the signal processing of radar sampling data, obtaining a distance-Doppler spectrum, and constructing an initial clutter map; calculating a threshold factor according to a preset false alarm rate, multiplying the threshold factor by the clutter map to obtain a target detection threshold, and performing target detection on the distance-Doppler spectrum of the current frame on the distance-Doppler spectrum to obtain a target detection result; and updating the clutter map according to a target detection result, and carrying out target detection of the next frame of distance-Doppler spectrum by adopting the updated clutter map. The method can effectively improve the detection probability of the expansion target reflection point.

Description

Technical Field

[0001] The present invention relates to the technical field of radar target detection, and in particular to a clutter image constant false alarm target detection method, system, storage medium and computing device. Background Art

[0002] In radar signal processing, electromagnetic echo signals often contain multiple interference factors, necessitating accurate differentiation between target signals and clutter signals, including receiver noise. Target detection is typically performed on the signal spectrum. Commonly used signal spectra include range spectrum, angle spectrum, Doppler spectrum, range-Doppler spectrum, and range-angle spectrum. The Constant False Alarm Rate (CFAR) algorithm is a commonly used target detection algorithm because it can adaptively adjust the detection threshold based on clutter.

[0003] Commonly used CFAR algorithms are space-based CFAR algorithms. These algorithms estimate the clutter power by extracting cells adjacent to the unit to be detected in a single-frame signal spectrum, using this to dynamically set the detection threshold. These algorithms have the advantage of simple computational logic and have been widely used in engineering practice. However, when applied to extended target detection, they face the problem of low detection rates of target reflection points. This is due to energy masking: cells adjacent to the unit to be detected may contain other target reflection points, which can lead to an overestimation of the clutter power estimate, and thus a higher detection threshold. Weaker target reflection signals may be mistakenly identified as clutter.

[0004] Another type of CFAR algorithm is time-based CFAR, also known as clutter map CFAR. This type of algorithm uses historical sampled data from the unit to be detected to estimate clutter power. The algorithm calculates a threshold factor based on a preset false alarm rate and multiplies the clutter power by the threshold factor to obtain the detection threshold. Since the algorithm no longer uses neighboring units to estimate clutter power, it effectively avoids energy masking. However, current research on clutter map CFAR is largely at the theoretical derivation and simulation verification stage, lacking practical deployment solutions. Clutter map CFAR also faces the challenge of system startup, where no clutter map is available. Therefore, a simple and effective scheme for constructing an initial clutter map remains to be developed.

[0005] Currently, clutter map CFAR lacks a closed-form solution for the threshold factor. Existing techniques require Monte Carlo simulation to establish a mapping between false alarm rate and threshold factor. This method of determining the threshold factor through simulation requires extensive simulation to redefine the corresponding threshold factor after adjusting the false alarm rate, making it difficult to flexibly adjust the false alarm rate. Furthermore, slow-moving targets may appear in the environment. If a target repeatedly appears in the same resolution cell, estimating clutter power using historical sampling data from the cell to be detected may incorrectly use samples taken when the target was present, resulting in a high detection threshold and reduced algorithm performance. Summary of the Invention

[0006] In view of the above problems, the purpose of the present invention is to provide a clutter pattern constant false alarm target detection method, system, storage medium and computing device, which can effectively improve the probability of detecting extended target reflection points.

[0007] To achieve the above-mentioned object, in a first aspect, the present invention adopts a technical solution: a clutter map constant false alarm target detection method, comprising: performing signal processing on radar sampling data to obtain a range-Doppler spectrum to construct an initial clutter map; calculating a threshold factor according to a preset false alarm rate, multiplying the threshold factor by the clutter map to obtain a target detection threshold, performing target detection on the range-Doppler spectrum of a current frame on the range-Doppler spectrum to obtain a target detection result; updating the clutter map according to the target detection result, and using the updated clutter map to perform target detection on the range-Doppler spectrum of a next frame.

[0008] Furthermore, the radar sampling data is processed to obtain the range-Doppler spectrum, including:

[0009] Perform FFT on the sampled data of each chirp along the fast time dimension to obtain the range spectrum of the target;

[0010] Perform static elimination on the distance spectrum to remove signals reflected by static objects in the environment;

[0011] Perform FFT on the data after static elimination along the slow time dimension to obtain the range-Doppler spectrum of the target.

[0012] Furthermore, one or more frames of range-Doppler spectrum are used as input to construct an initial clutter map, including:

[0013] Determine whether a clutter map already exists. If so, calculate the target detection threshold. If not, accumulate one or several frames to obtain the range-Doppler spectrum and proceed to the next step.

[0014] The accumulated range-Doppler spectrum is used to construct the initial clutter map.

[0015] Furthermore, the initial clutter map is constructed using one of the following two methods or a combination of the two methods:

[0016] Method 1: If there is no target in the environment, the accumulated range-Doppler spectrum is directly averaged by resolution unit, and the average result is used as the initial clutter map;

[0017] Method 2: Without making any assumptions about whether there are targets in the environment, one or more frames of data are collected from the environment. A target detection threshold is set to perform target detection on the collected data, and the collected data is divided into resolution units with targets and resolution units without targets. The initial clutter power is estimated using the resolution units without targets to construct an initial clutter map.

[0018] Furthermore, the target detection threshold adopts a lower global threshold as the target detection threshold; or, adopts a space-based constant false alarm detection algorithm and sets a higher false alarm rate as the target detection threshold.

[0019] Furthermore, the target detection threshold is obtained by multiplying the threshold factor with the clutter map, and target detection is performed on the current frame range-Doppler spectrum on the range-Doppler spectrum to obtain the target detection result, including:

[0020] The target detection threshold of each resolution unit on the range-Doppler spectrum is the value of the corresponding resolution unit on the clutter map multiplied by the threshold factor;

[0021] The target detection threshold is used to detect the target in each resolution unit on the range-Doppler spectrum: if the power value of the resolution unit on the range-Doppler spectrum is greater than the corresponding target detection threshold, the resolution unit is determined to have a target; otherwise, the resolution unit is determined to have no target.

[0022] After completing target detection for each resolution unit on the range-Doppler spectrum, the target detection result is output.

[0023] Furthermore, the clutter map is updated according to the target detection result, and the updated clutter map is used to perform target detection in the next frame of range-Doppler spectrum, including:

[0024] For the resolution unit that detects the existence of a target, the clutter power stored in the corresponding resolution unit in the clutter map remains unchanged;

[0025] For a resolution unit that detects that no target exists, the power value of the resolution unit in the current range-Doppler spectrum is used to update the clutter power stored in the corresponding resolution unit in the clutter map.

[0026] In a second aspect, the present invention adopts a technical solution: a clutter map constant false alarm target detection system, which includes: a clutter map construction module, which performs signal processing on radar sampling data to obtain a range-Doppler spectrum to construct an initial clutter map; a target detection module, which calculates a threshold factor according to a preset false alarm rate, obtains a target detection threshold by multiplying the threshold factor by the clutter map, and performs target detection on the current frame range-Doppler spectrum on the range-Doppler spectrum to obtain a target detection result; and an update module, which updates the clutter map according to the target detection result, and uses the updated clutter map to perform target detection on the next frame range-Doppler spectrum.

[0027] In a third aspect, the technical solution adopted by the present invention is: a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes any one of the above methods.

[0028] In a fourth aspect, the technical solution adopted by the present invention is: a computing device, comprising: one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the above methods.

[0029] The present invention has the following advantages due to the adoption of the above technical solution:

[0030] 1. The present invention solves the energy masking problem encountered in extended target detection and effectively improves the detection rate of extended target reflection points.

[0031] 2. The present invention proposes two initial clutter map construction schemes, which solve the problem of no clutter map when the algorithm starts, so that the algorithm can be deployed in actual scenarios.

[0032] 3. The strategy proposed in this invention for updating the clutter map based on target detection results can effectively suppress the problem of inflated clutter power estimation caused by slow targets continuously staying in the same resolution unit, thereby significantly improving the adaptability of the target detection algorithm to targets with different moving speeds.

[0033] 4. The algorithm proposed in this invention provides a closed-form analytical solution to the threshold factor, allowing technicians to dynamically adjust the detection threshold according to the preset false alarm rate, significantly enhancing the flexibility and controllability of the detection system parameter configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is an overall flow chart of the clutter image constant false alarm target detection method according to an embodiment of the present invention;

[0035] Figure 2This is a detailed flowchart of the clutter image constant false alarm target detection method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] To address the problem in the prior art where a high detection threshold causes weaker target reflection signals to be mistakenly identified as clutter, resulting in reduced algorithm performance, the present invention provides a clutter map constant false alarm target detection method, system, storage medium, and computing device that can be deployed in practice, improve the probability of detecting extended target reflection points, and more completely characterize information such as the shape and posture of the extended target. The present invention comprises: constructing an initial clutter map using a radar signal spectrum as input; calculating a threshold factor based on a preset false alarm rate; calculating a target detection threshold using the clutter map and the threshold factor, and performing target detection on the signal spectrum; and updating the clutter map based on the target detection results. The present invention provides an initial clutter map construction scheme, a closed-form solution for the threshold factor, and a clutter map update strategy, enabling the practical deployment of a clutter map constant false alarm target detection algorithm and significantly improving the detection probability of extended target reflection points.

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of the present invention.

[0038] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0039] In one embodiment of the present invention, a method for detecting a target with constant false alarm rate (CFAR) is provided. Figure 1 、 Figure 2 As shown, the method includes the following steps:

[0040] 1) Perform signal processing on radar sampling data to obtain the range-Doppler spectrum to construct the initial clutter map;

[0041] In this embodiment, a frequency-modulated continuous wave (FMCW) millimeter-wave radar emits multiple chirp signals into the environment within a single frame. By analyzing changes in the chirp signal's frequency and phase, the radar can infer information such as the distance, velocity, and angle of targets in the environment. The data collected by the radar has two dimensions: a fast-time dimension, corresponding to a single chirp sample, which contains target distance information; and a slow-time dimension, consisting of radar frames composed of a fixed number of chirps, which contains information about the distance changes between moving targets and different chirps.

[0042] 2) Calculate the threshold factor based on the preset false alarm rate, multiply the threshold factor by the clutter map to obtain the target detection threshold, and perform target detection on the current frame range-Doppler spectrum on the range-Doppler spectrum to obtain the target detection result;

[0043] 3) Update the clutter map according to the target detection results, and use the updated clutter map to perform target detection in the next frame of range-Doppler spectrum.

[0044] In the above step 1), the radar samples the target's echo signal and performs signal processing on the sampled data to obtain a range-Doppler spectrum, which includes the following steps:

[0045] 1.1.1) Perform an FFT (Fast Fourier Transform) on the sampled data of each chirp along the fast time dimension to obtain the target's range spectrum. Each resolution unit in the range spectrum is called a range unit.

[0046] 1.1.2) Perform static elimination on the range spectrum to eliminate the signals reflected by static objects in the environment and highlight the reflected signals of dynamic targets;

[0047] Specifically, the static elimination operation is not limited to the following implementation: for each range unit, firstly calculate the mean along the slow time dimension, and then subtract the mean from the data of the corresponding range unit.

[0048] 1.1.3) Perform FFT on the static-eliminated data along the slow time dimension to obtain the target's range-Doppler spectrum.

[0049] In this embodiment, after obtaining the range-Doppler spectrum, one or more frames of range-Doppler spectrum are used as input to construct an initial clutter map. Specifically, the following steps are included:

[0050] 1.2.1) Determine whether a clutter map already exists. If so, proceed to step 2); if not, accumulate one or several frames of the range-Doppler spectrum obtained in the previous steps and proceed to the next step.

[0051] 1.2.2) Use the accumulated range-Doppler spectrum to construct the initial clutter map.

[0052] In this embodiment, the clutter map and the signal spectrum to be detected have the same number of resolution cells and physical meaning. The signal spectrum to be detected can be a range-Doppler spectrum, a range spectrum, an angle spectrum, a Doppler spectrum, a range-angle spectrum, etc. These signal spectra are all composed of multiple resolution cells. The clutter map and the range-Doppler spectrum have the same dimensions. Each resolution cell in the clutter map records the estimated value of the clutter power of the corresponding resolution cell in the range-Doppler spectrum.

[0053] Specifically, the initial clutter map is constructed using one of the following two methods or a combination of the two methods:

[0054] Method 1: If there is no target in the environment, the accumulated range-Doppler spectrum can be directly averaged by resolution unit, and the average result is used as the initial clutter map.

[0055] Method 2: Targets may exist in the environment, so no assumptions are made about their presence. A target detection threshold is set and target detection is performed on the collected data. The collected data is divided into resolution cells with and without targets. Resolution cells without targets are used to estimate the initial clutter power and construct the initial clutter map.

[0056] In this embodiment, the target detection threshold may be a lower global threshold as the target detection threshold, or a space-based constant false alarm detection algorithm may be used to set a higher false alarm rate as the target detection threshold.

[0057] Specifically, a lower global threshold is set as the target detection threshold. Specifically, when the set global threshold does not exceed a set multiple of the average power of the entire range-Doppler spectrum (for example, 5 times), it is considered a lower global threshold and is used as the target detection threshold.

[0058] A higher false alarm rate is set as the target detection threshold. Specifically, when the threshold factor calculated from the false alarm rate does not exceed 5, the set false alarm rate is considered high. The target detection threshold is then calculated based on this threshold factor. The higher the false alarm rate, the lower the calculated threshold factor. Since the final target detection threshold is the threshold factor multiplied by the noise power, the lower the threshold factor, the lower the target detection threshold.

[0059] The specific implementation of the above method 2 is as follows:

[0060] (1) Before constructing the initial clutter map, target detection is performed on the range-Doppler spectrum to exclude resolution units where targets may exist.

[0061] For example, a lower global threshold is used to detect targets on the range-Doppler spectrum; or a space-based CFAR, such as CA-CFAR, is used to set a higher false alarm rate to detect targets on the range-Doppler spectrum, thereby excluding all resolution units where targets may exist.

[0062] (2) For a resolution unit without a target, the data of multiple frames of the resolution unit are averaged as the clutter power estimate of the resolution unit and recorded in the corresponding resolution unit of the clutter map;

[0063] (3) For a resolution cell with a target, the clutter power estimation value of the adjacent resolution cell without a target is used as the clutter power estimation value of the resolution cell and recorded in the corresponding resolution cell in the clutter map.

[0064] In step 2), the clutter map is multiplied by the threshold factor to obtain the detection threshold, so the threshold factor needs to be calculated based on the preset false alarm rate. However, there is currently no closed-form solution for the threshold factor with relatively small error in the clutter map CFAR. Existing solutions generally obtain approximate solutions for the threshold factor through simulation, which makes it difficult to flexibly adjust the threshold factor according to the false alarm rate during actual deployment. For this reason, the present invention adopts a closed-form solution for the threshold factor, which is specifically as follows:

[0065] When the power of the resolution unit satisfies the exponential distribution and the clutter map is updated using the exponential weighted average method, the false alarm rate expression of the clutter map CFAR is:

[0066]

[0067] Where p fa is the false alarm rate, α is the threshold factor, and ω is the attenuation factor when updating the clutter map.

[0068] Generally, the threshold factor is obtained by inversely solving the preset false alarm rate. However, the expression of the false alarm rate of the clutter map CFAR is a transcendental equation and a closed-form solution cannot be obtained. In order to obtain a closed-form solution, this embodiment calculates the false alarm rate p fa Scale as follows:

[0069]

[0070] Thus, the upper bound of the false alarm rate is obtained. If this upper bound is set equal to the preset false alarm rate, then the actual false alarm rate must be less than the preset false alarm rate. Therefore, in this embodiment, the upper bound is set equal to the preset false alarm rate, and the closed-form solution of the threshold factor can be obtained as:

[0071]

[0072] Where α is the threshold factor, ω is the attenuation factor used to update the clutter map, and p fais the preset false alarm rate.

[0073] Comparing the threshold factor calculated using the above formula with the threshold factor obtained by simulation using the first 100 million terms of the formula, the maximum error between the two is 0.07%, indicating that the threshold factor calculated using the above formula is a close approximation of the actual threshold factor.

[0074] Given a preset false alarm rate, the threshold factor can be directly calculated according to the above formula, which allows technicians to flexibly adjust the false alarm rate.

[0075] The above step 2) calculates the target detection threshold and uses the threshold to perform target detection on the range-Doppler spectrum, including the following steps:

[0076] 2.1) Calculate the target detection threshold: The target detection threshold of each resolution unit on the range-Doppler spectrum is the value of the corresponding resolution unit on the clutter map multiplied by the threshold factor.

[0077] 2.2) Use the obtained target detection threshold to perform target detection on each resolution unit on the range-Doppler spectrum: if the power value of the resolution unit on the range-Doppler spectrum is greater than the corresponding target detection threshold, the resolution unit is determined to have a target; otherwise, the resolution unit is determined to have no target.

[0078] Specifically, each resolution unit of the range-Doppler spectrum is traversed; the power value of the resolution unit is taken out and compared with the calculated target detection threshold of the resolution unit; if the power value of the resolution unit is greater than the target detection threshold, then a target exists in the resolution unit; otherwise, no target exists in the resolution unit.

[0079] 2.3) After completing target detection for each resolution unit on the range-Doppler spectrum, output the target detection result.

[0080] Among them, the target detection results can be used in various perception applications, including but not limited to: target existence detection, point cloud generation, target tracking, etc.

[0081] In step 3), the clutter map is updated according to the target detection result to perform target detection in the next frame of range-Doppler spectrum, including the following steps:

[0082] 3.1) For the resolution unit that detects the presence of a target, the clutter power stored in the corresponding resolution unit in the clutter map remains unchanged;

[0083] 3.2) For a resolution unit that detects the absence of a target, the power value of the resolution unit in the current range-Doppler spectrum is used to update the clutter power stored in the corresponding resolution unit in the clutter map;

[0084] Specifically, the update process uses the following formula:

[0085]

[0086] Where, is the updated power value of the corresponding resolution unit of the clutter map, ω is the attenuation factor, is the power value of the clutter map corresponding to the resolution unit before updating, x n is the power value of the corresponding resolution unit in the range-Doppler spectrum.

[0087] In this embodiment, for the next frame of signal spectrum to be detected, the updated clutter map is used to perform target detection, and the clutter map is further updated using the above steps according to the result of the target detection.

[0088] In one embodiment of the present invention, a clutter image constant false alarm target detection system is provided, comprising:

[0089] The clutter map construction module performs signal processing on radar sampling data to obtain the range-Doppler spectrum to construct the initial clutter map;

[0090] The target detection module calculates a threshold factor based on a preset false alarm rate, multiplies the threshold factor by the clutter map to obtain a target detection threshold, and performs target detection on the current frame range-Doppler spectrum on the range-Doppler spectrum to obtain a target detection result;

[0091] The update module updates the clutter map according to the target detection results and uses the updated clutter map to perform target detection in the next frame of range-Doppler spectrum.

[0092] In the above embodiment, signal processing is performed on the radar sampling data to obtain the range-Doppler spectrum, including:

[0093] Perform FFT on the sampled data of each chirp along the fast time dimension to obtain the range spectrum of the target;

[0094] Perform static elimination on the distance spectrum to remove signals reflected by static objects in the environment;

[0095] Perform FFT on the data after static elimination along the slow time dimension to obtain the range-Doppler spectrum of the target.

[0096] In the above embodiment, one or more frames of range-Doppler spectra are used as input to construct an initial clutter map, including:

[0097] Determine whether a clutter map already exists. If so, calculate the target detection threshold. If not, accumulate one or several frames to obtain the range-Doppler spectrum and proceed to the next step.

[0098] The accumulated range-Doppler spectrum is used to construct the initial clutter map.

[0099] In the above embodiment, the initial clutter map is constructed using one of the following two methods or a combination of the two methods:

[0100] Method 1: If there is no target in the environment, the accumulated range-Doppler spectrum is directly averaged by resolution unit, and the average result is used as the initial clutter map;

[0101] Method 2: Without making any assumptions about whether there are targets in the environment, one or more frames of data are collected from the environment. A target detection threshold is set to perform target detection on the collected data, and the collected data is divided into resolution units with targets and resolution units without targets. The initial clutter power is estimated using the resolution units without targets to construct an initial clutter map.

[0102] In the above embodiment, the target detection threshold adopts a lower global threshold as the target detection threshold; or adopts a space-based constant false alarm detection algorithm by setting a higher false alarm rate as the target detection threshold.

[0103] In the above embodiment, the target detection threshold is obtained by multiplying the threshold factor by the clutter map, and target detection is performed on the range-Doppler spectrum of the current frame to obtain the target detection result, including:

[0104] The target detection threshold of each resolution unit on the range-Doppler spectrum is the value of the corresponding resolution unit on the clutter map multiplied by the threshold factor;

[0105] The target detection threshold is used to detect the target in each resolution unit on the range-Doppler spectrum: if the power value of the resolution unit on the range-Doppler spectrum is greater than the corresponding target detection threshold, the resolution unit is determined to have a target; otherwise, the resolution unit is determined to have no target.

[0106] After completing target detection for each resolution unit on the range-Doppler spectrum, the target detection result is output.

[0107] In the above embodiment, updating the clutter map according to the target detection result and using the updated clutter map to perform target detection in the next frame of range-Doppler spectrum includes:

[0108] For the resolution unit that detects the existence of a target, the clutter power stored in the corresponding resolution unit in the clutter map remains unchanged;

[0109] For a resolution unit that detects that no target exists, the power value of the resolution unit in the current range-Doppler spectrum is used to update the clutter power stored in the corresponding resolution unit in the clutter map.

[0110] The system provided in this embodiment is used to execute the above-mentioned method embodiments. Please refer to the above-mentioned embodiments for specific processes and detailed contents, which will not be repeated here.

[0111] In one embodiment of the present invention, a computing device is provided. The computing device may be a terminal and may include: a processor, a communications interface, a memory, a display screen, and an input device. The processor, communications interface, and memory communicate with each other via a communications bus. The processor is configured to provide computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. When executed by the processor, the computer program implements the methods described in the above embodiments. The internal memory provides an environment for the operating system and computer program in the non-volatile storage medium to run. The communications interface is configured to communicate with an external terminal via wired or wireless communication. The wireless communication may be achieved via Wi-Fi, a network management service provider, NFC (near field communication), or other technologies. The display screen may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen layer covering the display screen, or may be buttons, a trackball, or a touchpad provided on the computing device housing, or may be an external keyboard, touchpad, or mouse. The processor may invoke logic instructions stored in the memory.

[0112] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0113] In one embodiment of the present invention, a computer program product is provided, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided by the above-mentioned method embodiments.

[0114] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided. The non-transitory computer-readable storage medium stores server instructions. The computer instructions enable a computer to execute the methods provided in the above embodiments.

[0115] The above embodiment provides a computer-readable storage medium, whose implementation principle and technical effects are similar to those of the above method embodiment, and will not be repeated here.

[0116] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0117] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A clutter image constant false alarm target detection method, characterized in that: include: Perform signal processing on radar sampling data to obtain the range-Doppler spectrum to construct the initial clutter map; The threshold factor is calculated based on the preset false alarm rate, and the target detection threshold is obtained by multiplying the threshold factor with the clutter map. Target detection is then performed on the range-Doppler spectrum of the current frame to obtain the target detection result. The clutter map is updated according to the target detection results, and the updated clutter map is used to perform target detection in the next frame of range-Doppler spectrum.

2. The clutter image constant false alarm target detection method according to claim 1, wherein: Perform signal processing on the radar sampling data to obtain the range-Doppler spectrum, including: Perform FFT on the sampled data of each chirp along the fast time dimension to obtain the range spectrum of the target; Perform static elimination on the distance spectrum to remove signals reflected by static objects in the environment; Perform FFT on the data after static elimination along the slow time dimension to obtain the range-Doppler spectrum of the target.

3. The clutter image constant false alarm target detection method according to claim 1, wherein: Using one or more frames of range-Doppler spectra as input, an initial clutter map is constructed, including: Determine whether a clutter map already exists. If so, calculate the target detection threshold. If not, accumulate one or several frames to obtain the range-Doppler spectrum and proceed to the next step. The accumulated range-Doppler spectrum is used to construct the initial clutter map.

4. The clutter image constant false alarm target detection method according to claim 3, wherein: The initial clutter map is constructed using one of the following two methods or a combination of the two methods: Method 1: If there is no target in the environment, the accumulated range-Doppler spectrum is directly averaged by resolution unit, and the average result is used as the initial clutter map; Method 2: Without making any assumptions about whether there is a target in the environment, collect one or more frames of data in the environment; Set the target detection threshold to perform target detection on the collected data, and divide the collected data into resolution units with target presence and resolution units without target presence; The initial clutter power is estimated using the resolution cells without targets and the initial clutter map is constructed.

5. The clutter image constant false alarm target detection method according to claim 4, wherein: The target detection threshold adopts a lower global threshold as the target detection threshold; or adopts a space-based constant false alarm detection algorithm and sets a higher false alarm rate as the target detection threshold.

6. The clutter image constant false alarm target detection method according to claim 1, wherein: The target detection threshold is obtained by multiplying the threshold factor with the clutter map, and target detection is performed on the current frame range-Doppler spectrum on the range-Doppler spectrum to obtain the target detection results, including: The target detection threshold of each resolution unit on the range-Doppler spectrum is the value of the corresponding resolution unit on the clutter map multiplied by the threshold factor; The target detection threshold is used to detect the target in each resolution unit on the range-Doppler spectrum: if the power value of the resolution unit on the range-Doppler spectrum is greater than the corresponding target detection threshold, the resolution unit is determined to have a target; otherwise, the resolution unit is determined to have no target. After completing target detection for each resolution unit on the range-Doppler spectrum, the target detection result is output.

7. The clutter image constant false alarm target detection method according to claim 1, wherein: The clutter map is updated based on the target detection results, and the updated clutter map is used to perform target detection in the next frame of range-Doppler spectrum, including: For the resolution unit that detects the existence of a target, the clutter power stored in the corresponding resolution unit in the clutter map remains unchanged; For a resolution unit that detects that no target exists, the power value of the resolution unit in the current range-Doppler spectrum is used to update the clutter power stored in the corresponding resolution unit in the clutter map.

8. A clutter pattern constant false alarm target detection system, characterized in that: include: The clutter map construction module performs signal processing on radar sampling data to obtain the range-Doppler spectrum to construct the initial clutter map; The target detection module calculates a threshold factor based on a preset false alarm rate, multiplies the threshold factor by the clutter map to obtain a target detection threshold, and performs target detection on the current frame range-Doppler spectrum on the range-Doppler spectrum to obtain a target detection result; The update module updates the clutter map according to the target detection results and uses the updated clutter map to perform target detection in the next frame of range-Doppler spectrum.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any one of the methods of claims 1 to 7 .

10. A computing device, characterized in that include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any one of the methods according to claims 1 to 7.

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