A radar signal processing method and device, electronic equipment and storage medium

Abstract

This application proposes a radar signal processing method, apparatus, electronic device, and storage medium, belonging to the field of radar information processing technology. The method includes: acquiring channel data and optimizing the channel data; using a first filter to suppress clutter in the optimized channel data to display moving targets; using a second filter to further filter out clutter from the clutter-suppressed channel data to obtain target detection data; and detecting the target detection data based on a constant false alarm rate to obtain radar-detected targets. The apparatus includes: a data acquisition module, a data optimization module, a first filtering module, a second filtering module, and a target detection module. This application achieves hardware and software decoupling, significantly improving the real-time performance of the radar signal processing system, while also improving anti-interference capability and signal-to-noise ratio.

Description

Technical Field

[0001] This application belongs to the field of radar information processing technology, specifically relating to a radar signal processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] The radar signal processing system is an indispensable core component of radar, determining its performance. It primarily utilizes various algorithms to perform clutter and interference suppression, target detection, and information extraction on the radar signals received by the receiver. For example, the most basic function of a Doppler radar is to acquire the target's range, azimuth, and velocity. Currently, the hardware architecture of mainstream radar signal processing systems uses a Field-Programmable Gate Array (FPGA) combined with a Digital Signal Processing Unit (DSP). Based on this, algorithms such as pulse compression, moving target indication / detection, and constant false alarm rate (CFAR) are developed to implement signal processing functions.

[0003] The current mainstream radar signal processing system's FPGA combined with DSP hardware architecture has some unavoidable drawbacks. For example, the hardware and software coupling is too strong. Once the radar system needs to be upgraded or modified, the hardware and software of the DSP and FPGA must be redesigned. This strong coupling seriously affects the radar upgrade and modification. The lack of computing resources is also a weakness. Using FPGA and DSP to complete various algorithms in the radar signal system places a huge burden on the chips themselves. Often, due to insufficient computing resources, some functions of the radar signal processing system must be weakened or reduced, thus affecting radar performance.

[0004] Due to limitations in computing resources, traditional radar signal processing rarely involves the development of anti-jamming functions, and moving target display and moving target detection functions often require choosing one or the other. This will impose certain constraints on radar performance. Summary of the Invention

[0005] Based on the above-mentioned technical deficiencies, this application provides a radar signal processing method, apparatus, electronic device, and storage medium.

[0006] In a first aspect, this application proposes a radar signal processing method, comprising the following steps:

[0007] Acquire channel data and optimize the channel data, which includes multiple periodically repeating pulse radar signals;

[0008] The first filter is used to suppress clutter in the optimized channel data in order to display moving targets;

[0009] The second filter is used to further filter out clutter from the channel data after clutter suppression, so as to obtain the target data to be detected.

[0010] Based on a constant false alarm rate, the target data to be detected is detected to obtain the radar-detected target.

[0011] The optimization of the channel data includes:

[0012] The channel data is subjected to anti-interference processing;

[0013] Narrow pulse suppression is applied to the channel data after anti-interference processing;

[0014] Pulse compression is performed on the channel data after narrow pulse suppression;

[0015] Anti-asynchronous interference processing is applied to the channel data after pulse compression.

[0016] The channel data includes: main channel data and auxiliary channel data; the anti-interference processing of the channel data includes the following steps:

[0017] The pulse radar signals at the same time corresponding to the main channel data and the auxiliary channel data are weighted and summed.

[0018] The interference signal of the main channel data is eliminated based on the weighted summation data to obtain the anti-interference processed channel data.

[0019] The process of narrow pulse suppression on the channel data after anti-interference processing includes the following steps:

[0020] Obtain the distance between two adjacent pulses and determine whether the distance between the two adjacent pulses is greater than a preset threshold;

[0021] If yes, the two pulses are different pulses; if no, the two pulses are the same pulse.

[0022] The pulse compression of the channel data after narrow pulse suppression includes the following steps: using nonlinear phase modulation of the signal to perform pulse compression on the channel data after narrow pulse suppression.

[0023] The compressed channel data contains multiple pulses of the target signal; the anti-asynchronous interference processing of the compressed channel data includes the following steps: determining whether the same target signal appears continuously in multiple pulses in the compressed channel data; if it does not appear continuously in multiple pulses, the target signal is an interference signal; and removing the interference signal.

[0024] The first filter utilizes the Doppler frequency difference between clutter and the moving target for filtering.

[0025] The process of detecting the target data based on a constant false alarm rate to obtain the radar-detected target includes the following steps:

[0026] The noise and interference levels in the target data to be detected are estimated, and a threshold is set based on the estimated noise and interference levels.

[0027] The threshold is compared with the target data to be detected to determine whether there is a radar-detectable target.

[0028] Secondly, this application proposes a radar signal processing device, comprising: a data acquisition module, a data optimization module, a first filtering module, a second filtering module, and a target detection module;

[0029] The data acquisition module, data optimization module, first filtering module, second filtering module, and target detection module are connected in sequence.

[0030] The data acquisition and optimization module is used to acquire channel data;

[0031] The data optimization module is used to optimize the channel data;

[0032] The second filtering module is used to suppress clutter in the optimized channel data using the first filter in order to display moving targets;

[0033] The second filtering module is used to further filter out clutter from the channel data after clutter suppression using the second filter, so as to obtain the target data to be detected;

[0034] The target detection module is used to detect the target data based on a constant false alarm rate to obtain the radar-detected target.

[0035] The data optimization module includes: an anti-interference processing unit, a narrow pulse suppression unit, a pulse compression unit, and an anti-asynchronous interference processing unit;

[0036] The anti-interference processing unit, the narrow pulse suppression unit, the pulse compression unit, and the anti-asynchronous interference processing unit are connected in sequence.

[0037] The anti-interference processing unit is used to perform anti-interference processing on the channel data;

[0038] The narrow pulse suppression unit is used to suppress narrow pulses in the channel data after anti-interference processing.

[0039] The pulse compression unit is used to perform pulse compression on the channel data after narrow pulse suppression.

[0040] The anti-asynchronous interference processing unit is used to perform anti-asynchronous interference processing on the channel data after pulse compression.

[0041] Thirdly, this application proposes an electronic device comprising: one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the radar signal processing method as described above.

[0042] Fourthly, this application proposes a storage medium storing executable instructions that, when executed, cause a machine to perform the radar signal processing method described above.

[0043] Beneficial technical effects:

[0044] This application proposes a radar signal processing method, apparatus, electronic device, and storage medium, which can achieve the following technical effects:

[0045] 1. Decoupling of hardware and software enables flexible modification and upgrading of the radar.

[0046] To address the shortcomings and technical requirements of existing technologies, this application provides a multi-threaded radar signal processing method and apparatus based on a CPU architecture. This improves the real-time performance of radar signal system processing, reduces secondary development costs, facilitates the upgrading and modification of radar signal processing functions, and overcomes the limitation of fixed functions in digital radar systems, making upgrades difficult.

[0047] 2. Multi-threaded design significantly improves the real-time performance of the radar signal processing system.

[0048] In radar signal processing, there is a concept of a "frame," which contains multiple PRI (Primary Information Level) data points. To improve the signal-to-noise ratio (SNR) during radar signal processing, coherent accumulation is commonly used. Accumulating multiple PRI data points means processing a large amount of data, posing a significant challenge to the real-time performance requirements of signal processing. Multithreaded methods allocate a separate thread to each PRI data point for individual processing. Using this method, multiple PRI data points within a single frame can be processed simultaneously, significantly improving the real-time performance of the radar signal processing system.

[0049] 3. Reconstruct the signal processing flow to improve anti-interference capability.

[0050] This application restructures the traditional signal processing flow. Under the restructured architecture of this application, even general radars can acquire anti-jamming capabilities. This application processes baseband IQ signals using signal processing methods including sidelobe concealment (SLC), narrow pulse suppression (NPS), pulse compression (PC), anti-asynchronous interference (ASYN), moving target indication (MTI), moving target detection (MTD), and constant false alarm rate system.

[0051] 4. Develop MTI cascaded MTD using multi-threading to improve computational efficiency and signal-to-noise ratio.

[0052] The MTI-cascaded MTD approach used in this application improves the detection performance of moving targets and increases the signal-to-noise ratio (SNR) gain in the radar system. In this method, an MTI filter is first used to suppress ground clutter. Due to the characteristics of ground clutter, its clutter spectrum is concentrated at zero frequency and integer multiples of the radar pulse repetition frequency, with a small range of expansion. The MTI filter suppresses echoes resembling targets generated by ground clutter, while minimizing or eliminating the loss of moving target echoes. The MTD filter uses a Doppler filter bank, with each filter's passband covering a certain frequency range. From the output of each filter, it is possible to determine whether there are moving targets within that frequency range and estimate the target's velocity range. Since the MTD in this application is an FFT-type filter, it has no dips at zero frequency and integer multiples of the PRF. Therefore, combining it with the MTI algorithm not only improves the SNR but also compensates for the inherent limitations of FFT filters. Attached Figure Description

[0053] Figure 1 This is a flowchart of a radar signal processing method according to an embodiment of this application;

[0054] Figure 2 This is an optimized channel data flow diagram of an embodiment of this application;

[0055] Figure 3 This is a flowchart illustrating the detection process for target data in an embodiment of this application.

[0056] Figure 4 This is a schematic block diagram of a radar signal processing device according to an embodiment of this application;

[0057] Figure 5 This is a block diagram illustrating the internal workings of the data optimization module in an embodiment of this application.

[0058] Figure 6 This is the optimal integration flowchart for an embodiment of this application;

[0059] Figure 7 This is a schematic diagram of the main channel data acquisition in this embodiment;

[0060] Figure 8 This is a schematic diagram of the main channel acquiring data with interference in this embodiment;

[0061] Figure 9 This is a schematic diagram of the data after anti-interference processing in this embodiment;

[0062] Figure 10 This is a schematic diagram of the data after narrow pulse suppression processing in this embodiment;

[0063] Figure 11 This is a schematic diagram of the data after pulse compression processing in this embodiment;

[0064] Figure 12 This is a schematic diagram of the data after anti-asynchronous interference processing in this embodiment;

[0065] Figure 13 This is a schematic diagram of the same data position in five consecutive pulses in this embodiment;

[0066] Figure 14 This is a schematic diagram of the MTD filtering result of the data after anti-asynchronous interference processing in this embodiment;

[0067] Figure 15 This is a schematic diagram of the MTI cascaded MTD filtering result in this embodiment. Detailed Implementation

[0068] The present disclosure will be further described below with reference to the embodiments shown in the accompanying drawings.

[0069] Traditional digital radar systems are developed using FPGA+DSP architecture for signal processing. This approach results in rigid hardware-software coupling and lacks a clear front-end / back-end "cross-section." Front-end signal preprocessing, such as analog-to-digital conversion and digital down-conversion, is typically handled by the FPGA. This means that adding or modifying radar functionality often requires a complete hardware and software re-architecting. The general-purpose computing blade utilized in this application creates a front-end / back-end "cross-section." The FPGA in the radar performs signal preprocessing, transmitting the digital baseband signal to the computing blade using a specific transmission method. The CPU-based signal processing system within the blade performs the corresponding signal processing tasks independently of the front-end. The only connection between the two is the interaction between digital signals and commands, allowing for extensive secondary development of the system beyond this "cross-section" without requiring a complete hardware re-architecting.

[0070] This application presents a novel radar signal processing method using a CPU as the processing core. With the development of radar models, radars have become more open, standardized, universal, and modular. Modern radar signal processing generally deals with digital signals. After the radar echo is received by the receiver, it undergoes down-conversion, AD conversion, and other steps to be converted into a digital baseband signal, which is then processed. This application not only utilizes a multi-core CPU as the processing core but also improves the traditional radar signal processing flow at the algorithm level. It incorporates anti-asynchronous interference, narrow pulse suppression, and sidelobe cancellation to resist various types of interference. Furthermore, it integrates an MTI-cascaded MTD filter to further improve the radar signal-to-noise ratio. In traditional radar architectures, due to resource constraints, it is difficult to complete the development of such complex functions while ensuring real-time requirements.

[0071] In radar signal processing, there is a concept of a "frame," which contains multiple PRI (Pulse Repetition Interval) data points. To improve the signal-to-noise ratio (SNR) during radar signal processing, coherent accumulation is commonly used. Accumulating multiple PRI data points means processing a large amount of data, posing a significant challenge to the real-time performance requirements of signal processing. In multi-threaded methods, each PRI data point is assigned to a separate thread for processing. This method allows multiple PRI data points within a frame to be processed simultaneously, significantly improving the real-time performance of the radar signal processing system. Different allocation methods for PRI data can be used in different embodiments. For example, if a frame contains 128 PRI data points, it can be processed by a single thread of the processor, or by a single thread for each PRI data point. One process can handle the main channel data, and another process can handle the auxiliary channel data. The allocation of each process to specific threads and the corresponding data is determined by the computer's internal default settings. All allocation methods fall within the scope of this application.

[0072] Firstly, this application proposes a radar signal processing method, such as... Figure 1 As shown, it includes the following steps:

[0073] Step S1: Acquire channel data; optimize the channel data, which includes multiple periodically repeating pulse radar signals;

[0074] The optimization of the channel data, such as Figure 2 As shown, it includes:

[0075] Step S1.1: Perform anti-interference processing on the channel data;

[0076] The channel data includes: main channel data and auxiliary channel data; the anti-interference processing of the channel data includes the following steps:

[0077] The pulse radar signals at the same time corresponding to the main channel data and the auxiliary channel data are weighted and summed.

[0078] The interference signal of the main channel data is eliminated based on the weighted summation data to obtain the anti-interference processed channel data.

[0079] Step S1.2: Perform narrow pulse suppression on the channel data after anti-interference processing, including the following steps:

[0080] Obtain the distance between two adjacent pulses and determine whether the distance between the two adjacent pulses is greater than a preset threshold;

[0081] If yes, the two pulses are different pulses; if no, the two pulses are the same pulse.

[0082] Step S1.3: Perform pulse compression on the channel data after narrow pulse suppression, including the following steps: use nonlinear phase modulation of the signal to perform pulse compression on the channel data after narrow pulse suppression.

[0083] Step S1.4: Perform anti-asynchronous interference processing on the channel data after pulse compression.

[0084] The compressed channel data contains multiple pulses of the target signal; the anti-asynchronous interference processing of the compressed channel data includes the following steps: determining whether the same target signal appears continuously in multiple pulses in the compressed channel data; if it does not appear continuously in multiple pulses, the target signal is an interference signal; and removing the interference signal.

[0085] Step S2: The first filter is used to suppress clutter in the optimized channel data to display the moving target; the first filter uses the Doppler frequency difference between the clutter and the moving target for filtering. In this embodiment, the first filter is an MTI filter.

[0086] Step S3: Use the second filter to further filter out clutter from the channel data after suppressing clutter, and obtain the target data to be detected; in this embodiment, the second filter is an MTD filter.

[0087] Step S4: Based on a constant false alarm rate, the target data to be detected is processed to obtain the radar-detected target, such as... Figure 3 As shown, it includes the following steps:

[0088] Step S4.1: Estimate the noise and interference levels in the target data to be detected, and set a threshold based on the estimated noise and interference levels;

[0089] Step S4.2: Compare the threshold with the target detection data to determine whether there is a radar detection target.

[0090] Secondly, this application proposes a radar signal processing device, such as... Figure 4 As shown, it includes: a data acquisition module, a data optimization module, a first filtering module, a second filtering module, and a target detection module;

[0091] The data acquisition module, data optimization module, first filtering module, second filtering module, and target detection module are connected in sequence.

[0092] The data acquisition and optimization module is used to acquire channel data;

[0093] The data optimization module is used to optimize the channel data;

[0094] The second filtering module is used to suppress clutter in the optimized channel data using the first filter in order to display moving targets;

[0095] The second filtering module is used to further filter out clutter from the channel data after clutter suppression using the second filter, so as to obtain the target data to be detected;

[0096] The target detection module is used to detect the target data based on a constant false alarm rate to obtain the radar-detected target.

[0097] The data optimization module is as follows: Figure 5 As shown, it includes: an anti-interference processing unit, a narrow pulse suppression unit, a pulse compression unit, and an anti-asynchronous interference processing unit;

[0098] The anti-interference processing unit, the narrow pulse suppression unit, the pulse compression unit, and the anti-asynchronous interference processing unit are connected in sequence.

[0099] The anti-interference processing unit is used to perform anti-interference processing on the channel data;

[0100] The narrow pulse suppression unit is used to suppress narrow pulses in the channel data after anti-interference processing.

[0101] The pulse compression unit is used to perform pulse compression on the channel data after narrow pulse suppression.

[0102] The anti-asynchronous interference processing unit is used to perform anti-asynchronous interference processing on the channel data after pulse compression.

[0103] In one embodiment of the present invention:

[0104] This application uses the cVPX-D2100 system general-purpose computing blade as the processor for the radar signal processing method. This blade is based on an Intel D-2183IT processor, configured with 16 / 32 threads, and utilizes communication methods such as fiber optics or RapidIO for data transmission and digital signal processing. This approach creates a clear separation between the back-end processing system and the radar front-end (including signal preprocessing). Upgrading the back-end processing system only involves software refactoring, without any changes to the front-end hardware architecture, truly achieving hardware-software decoupling.

[0105] The processor supports up to 16 cores / 32 threads, which means that the signal processing system can process 32 pulse repetition interval (PRI) data at the same time. In this embodiment, each PRI data is assigned to a thread for separate processing. The hardware parameters and software environment are shown in Table 1.

[0106] Table 1 Hardware Parameters and Software Environment

[0107]

[0108] Due to its powerful real-time data processing capabilities, this embodiment employs an optimal integration process for radar signal processing, as follows: Figure 6 As shown:

[0109] Step S110: Obtain main channel data;

[0110] The receiver receives data from the main channel and auxiliary channels, which undergo analog-to-digital conversion (AD) and digital down-conversion (DDC) to transform the radio frequency (RF) signal into a baseband signal. Based on the RF signal, Matlab is used to perform time-domain flipping and conjugation operations to calculate the pulse compression coefficient. This coefficient is saved in the Intel processor's memory for subsequent pulse compression algorithm calls. The MTI coefficient is also saved in memory. In this embodiment, the radar echo RF signal acquired by the main channel is as follows: Figure 7 As shown, the main channel receives data with interference from... Figure 8 It can be seen that the received signal is very chaotic, making it impossible to distinguish the location of the target signal. A distinct frequency-modulated signal was also received near the 40,000 sampling point. Figure 8 This can be seen as a typical active interference signal.

[0111] Step S111: Determine if there is data in the main channel; if there is, proceed to step S130; if not, proceed to step S110 and continue to acquire main channel data.

[0112] Step S120: Acquire auxiliary channel data;

[0113] Step S121: Determine whether there is data in the auxiliary channel; if there is, proceed to step S130; if not, proceed to step S120 and continue to acquire auxiliary channel data.

[0114] Step S130: Store data according to the sequence numbers of the main channel and auxiliary channel;

[0115] Step S140: Determine whether the main channel data and the auxiliary channel data are synchronized; if yes, proceed to step S150; if no, proceed to step S130 and continue to store data according to the sequence number of the main channel and the auxiliary channel.

[0116] Step S150: Perform anti-interference processing, including the following procedures:

[0117] The anti-interference processing is called sidelobe cancellation (SLC). In this application, the baseband signal is transmitted to the processor via optical fiber, and an active interference suppression operation is first performed on the baseband signal. Because the sidelobes of the radar receiving antenna are very wide, interference signals can easily enter through the sidelobes. Although the sidelobe gain is very low, when the radar is in a strong active interference environment, the interference signal may overwhelm the target signal, causing the radar to fail to detect the target. The anti-interference processing utilizes the auxiliary channel data transmitted from the auxiliary antenna, which is weighted and summed with the main channel data, and then subtracted from the interference signal of the main channel data received by the main antenna. This minimizes the main channel interference output power, thereby achieving the purpose of interference cancellation. After the active interference is cancelled, the radar echo is processed by subtracting the auxiliary channel data acquired by the auxiliary antenna from the main channel data acquired by the main antenna, thus achieving the purpose of interference cancellation. Figure 9 As can be seen, after the SLC algorithm, the interference signal completely disappears, leaving behind a clean signal. Figure 9 Other interferences still exist, requiring more signal processing to obtain the desired target information.

[0118] Step S160: Perform Narrow Pulse Suppression (NPS), which includes the following process:

[0119] After the main channel data has undergone anti-interference processing, noise and other factors may still cause amplitude fluctuations within the pulse, making some distance units within the pulse undetectable. Narrow pulse suppression is performed to prevent pulse splitting; only pulses with a spacing greater than a certain threshold dT are considered two pulses, otherwise they are considered the same pulse. (Comparison) Figure 10 and Figure 9 It can be observed that, due to factors such as noise, many narrow pulses split off near the main pulse in the echo. The presence of these narrow pulses affects the judgment of the target signal. After the Narrow Pulse Suppression (NPS) algorithm, the split narrow pulses are completely eliminated.

[0120] Step S170: Perform pulse compression (PC), which includes the following process: compress the data after narrow pulse suppression to improve range resolution. To meet both detection range and range resolution requirements, radar needs to utilize nonlinear phase modulation of the signal, such as linear frequency modulation. Pulse compression allows the radar to transmit wide pulses while maintaining good range resolution. Figure 11As can be seen, even after removing some interference signals using SLC and NPS algorithms, five pulse signals still appear in the pulse compression. The purpose of pulse compression is to compress wide pulses into narrow pulses to improve radar range resolution. This indicates that other signal processing methods are needed to verify that these are five real targets. The data after narrow pulse suppression is subjected to a Fourier transform, and the pulse compression coefficients are also subjected to a Fourier transform. The results of the two Fourier transforms are multiplied to obtain the product. An inverse Fourier transform is then performed on this product to obtain the pulse-compressed channel data.

[0121] Step S180: Perform anti-nonsynchronous interference processing (ASYN), including the following procedures:

[0122] The principle of anti-asynchronous jamming is whether the same target appears consecutively in multiple pulses. If it appears consecutively, it is considered a genuine target echo; otherwise, it is considered a jamming signal. According to radar parameters, the duration of a PRI (Primary Response Time) is often in the millisecond or even microsecond range, which can be approximated as the target not moving and should be in the same position within multiple consecutive echoes. In this application, it is defined that a target is considered a genuine target only if it appears consecutively at the same position 3 or more times in 5 consecutive pulses; otherwise, it is considered asynchronous jamming. (Comparison) Figure 12 , 11 It can be observed that after anti-asynchronous suppression (NPS), the pulses around sampling point 20,000 have disappeared. From Figure 13 It can be observed that the pulse was not in the same position in any of the five consecutive echoes. Based on the target's moving speed and the duration of a single radar pulse, if it were a real target, it should be in the same position for several consecutive pulses, because a moving target cannot undergo a significant change in spatial position within a minute or even nanosecond interval.

[0123] Step S190: Perform MTI filtering, including the following process:

[0124] The MTI (Multi-Target Interference) filtering principle utilizes a clutter suppression filter to suppress various types of clutter, improving the signal-to-clutter ratio (SNR) of the radar signal and facilitating moving target detection. Taking ground clutter as an example, the clutter spectrum is typically concentrated at DC (Doppler frequency 0) and integer multiples of the radar pulse repetition frequency. The MTI filter leverages the Doppler frequency difference between the clutter and the moving target, positioning its notch at the clutter frequency to filter the clutter. In this application, the MTI is cascaded with an MTD (Multi-Target Interference Device) to further improve the signal-to-noise ratio. The MTI coefficients, typically a 1×p matrix, are predetermined. P cycles of pulse radar signals after anti-asynchronous interference processing are accumulated. The accumulated result is multiplied by the MTI coefficients to obtain the MTI-filtered result, thus suppressing ground clutter.

[0125] Step S200: Perform MTD filtering, including the following process:

[0126] Unlike traditional signal processing methods, this application cascades MTD and MTI, rather than choosing one algorithm over the other. Although both MTI and MTD utilize filters to remove clutter based on the target's frequency, considering their different focuses, this application cascades them. The MTI-filtered signal is then used for MTD target detection, eliminating some of the clutter's influence on the MTD algorithm and improving the output signal-to-noise ratio.

[0127] This application derives a formula for calculating the signal-to-noise ratio (SNR) gain of an MTI cascaded with an MTD, where the gain does not exceed the SNR gain of the MTD alone. This demonstrates that the cascaded signal is not distorted and effectively filters out noise. The derivation process is as follows:

[0128] The binomial coefficient 3-pulse MTI of the received signal is:

[0129] y(k) = x(k) - 2x(k+1) + x(k+2)

[0130] k = 0, 1, ..., K-1

[0131] Where k is the kth pulse, K is the total number of pulses, x(k) is the kth pulse signal, corresponding to the kth pulse signal after anti-asynchronous interference processing in the processing flow of this embodiment, and y(k) is the output signal after MTI filtering.

[0132] Windowed DFT processing is applied to the MTI output:

[0133]

[0134] Where m is the m-th MTD filter, and w(k) is the weight.

[0135] The SNR gain of the m-th MTD filter can be obtained as:

[0136]

[0137] Where β is the β-th MTD filter;

[0138] Similarly, the binomial coefficients for the gain of a cascaded MTD with 2-pulse MTI and 4-pulse MTI are derived, and finally, the gain I of a general cascaded MTD with p-pulse MTI is summarized. p (m) is:

[0139]

[0140] Where p is the number of MTI pulses. When p > 1, it is MTI cascaded with MTD. When p = 1, it is equivalent to MTD only.

[0141] The above equation shows that (1) in the MTI passband, cascading MTDs with MTI does not change the maximum SNR gain of each MTD filter, that is, when β = m, H(p, β) = 1, therefore I p (m)=I1(m).

[0142] (2) The range of β is For β≠m, H(p,β)≠1, therefore, I p (m)≠I1(m).

[0143] In this embodiment, Figure 14 To directly apply MTD filtering to the results of anti-asynchronous interference, it can be seen from the figure that many interference signals appear near the Doppler 0 channel, with low noise and high noise. Figure 15 For the same anti-asynchronous interference result, first apply an MTI filter to filter it once, and then apply an MTD filter. (Comparison) Figure 14 , Figure 15 It is evident that the MTI-cascaded MTD configuration makes the radar echo "cleaner," filtering out many unnecessary clutter signals and improving the signal-to-noise ratio of the radar signal.

[0144] Step S210: Perform target detection to obtain the radar-detected target, including the following process:

[0145] The target detection process involves processing data through a Constant False Alarm Rate (CFAR) system. The purpose of the CFAR system is to maintain a constant false alarm rate during signal detection under interference, preventing the radar system from becoming overloaded due to excessive false alarms. The basic CFAR process involves estimating the noise and interference levels within the cell to be detected, setting a threshold based on the estimate, and then comparing this threshold with the data of the detection cell to determine the presence of a target. Finally, the data is transmitted to the data processing system for operations such as point track convergence.

[0146] Thirdly, this application proposes an electronic device, including: one or more processors and a memory, wherein the memory stores instructions, and the processor is a multi-core multi-threaded processor, wherein when the instructions are executed by the one or more processors, the one or more processors perform the radar signal processing method as described above.

[0147] Fourthly, this application proposes a storage medium storing executable instructions that, when executed, cause a machine to perform the radar signal processing method described above.

[0148] In the various embodiments of the present invention, the functional units can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0149] Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or 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 to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0150] The aforementioned storage media include: flash memory, hard disk, multimedia card, card-type memory (e.g., SD (Secure Digital Memory Card) or DX (Memory Data Register, MDR) memory, random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, APP (Application) application store, and other media capable of storing program verification codes, on which computer programs are stored. When the computer programs are executed by the processor, they can implement the various steps of the aforementioned radar signal processing method.

[0151] The various embodiments in this disclosure are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0152] The scope of protection of this disclosure is not limited to the embodiments described above. Obviously, those skilled in the art can make various modifications and variations to this disclosure without departing from its scope and spirit. If such modifications and variations fall within the scope of the claims of this disclosure and their equivalents, then the intent of this disclosure also includes such modifications and variations.

Claims

1. A radar signal processing method, characterized in that, The method, applied to a computing blade, wherein the computing blade is equipped with a signal processing system based on a multi-core CPU, includes the following steps: Acquire channel data and optimize the channel data, wherein the channel data includes multiple periodically repeating pulse radar signals; wherein each frame of data in the channel data is processed by a single thread, and each frame of data contains multiple periodically repeating pulse radar signals; or each periodically repeating pulse radar signal in each frame of data is processed by a single thread; the optimization of the channel data includes: The channel data is subjected to anti-interference processing; Narrow pulse suppression is applied to the channel data after anti-interference processing; Pulse compression is performed on the channel data after narrow pulse suppression; Anti-asynchronous interference processing is performed on the channel data after pulse compression; The first filter is used to suppress ground clutter in the optimized channel data in order to display moving targets; The second filter is used to further filter out clutter from the channel data after clutter suppression, so as to obtain the target data to be detected. Based on a constant false alarm rate, the target data to be detected is detected to obtain the radar-detected target.

2. The radar signal processing method according to claim 1, characterized in that, The channel data includes: main channel data and auxiliary channel data; the anti-interference processing of the channel data includes the following steps: The pulse radar signals at the same time corresponding to the main channel data and the auxiliary channel data are weighted and summed. The interference signal of the main channel data is eliminated based on the weighted summation data to obtain the anti-interference processed channel data.

3. The radar signal processing method according to claim 1, characterized in that, The process of narrow pulse suppression on the channel data after anti-interference processing includes the following steps: Obtain the distance between two adjacent pulses and determine whether the distance between the two adjacent pulses is greater than a preset threshold; If yes, the two pulses are different pulses; if no, the two pulses are the same pulse.

4. The radar signal processing method according to claim 1, characterized in that, The pulse compression of the channel data after narrow pulse suppression includes the following steps: Pulse compression is performed on the channel data after narrow pulse suppression using nonlinear phase modulation of the signal.

5. The radar signal processing method according to claim 1, characterized in that, The compressed channel data contains multiple pulses of the target signal; the anti-asynchronous interference processing of the compressed channel data includes the following steps: determining whether the same target signal appears consecutively in multiple pulses in the compressed channel data; If the target signal does not appear consecutively in multiple pulses, then the target signal is an interference signal; remove the interference signal.

6. The radar signal processing method according to claim 1, characterized in that, The first filter utilizes the Doppler frequency difference between clutter and the moving target for filtering.

7. The radar signal processing method according to claim 1, characterized in that, The process of detecting the target data based on a constant false alarm rate to obtain the radar-detected target includes the following steps: The noise and interference levels in the target data to be detected are estimated, and a threshold is set based on the estimated noise and interference levels. The threshold is compared with the target data to be detected to determine whether there is a radar-detectable target.

8. A radar signal processing device, characterized in that, The radar signal processing device is applied to a computing blade, wherein the computing blade is equipped with a signal processing system with a multi-core CPU as the processing core. The radar signal processing device includes: a data acquisition module, a data optimization module, a first filtering module, a second filtering module, and a target detection module. The data acquisition module, data optimization module, first filtering module, second filtering module, and target detection module are connected in sequence. The data acquisition and optimization module is used to acquire channel data; The data optimization module is used to optimize the channel data; wherein each frame of data in the channel data is processed by a thread, and each frame of data contains multiple periodically repeating pulse radar signals; or each periodically repeating pulse radar signal in each frame of data is processed by a thread; the data optimization module includes: an anti-interference processing unit, a narrow pulse suppression unit, a pulse compression unit, and an anti-asynchronous interference processing unit. The anti-interference processing unit, the narrow pulse suppression unit, the pulse compression unit, and the anti-asynchronous interference processing unit are connected in sequence. The anti-interference processing unit is used to perform anti-interference processing on the channel data; The narrow pulse suppression unit is used to suppress narrow pulses in the channel data after anti-interference processing. The pulse compression unit is used to perform pulse compression on the channel data after narrow pulse suppression. The anti-asynchronous interference processing unit is used to perform anti-asynchronous interference processing on the channel data after pulse compression. The second filtering module is used to suppress ground clutter in the optimized channel data using the first filter in order to display moving targets; The second filtering module is used to further filter out clutter from the channel data after clutter suppression using the second filter, so as to obtain the target data to be detected; The target detection module is used to detect the target data based on a constant false alarm rate to obtain the radar-detected target.

9. An electronic device, characterized in that, include: One or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform the radar signal processing method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, It stores executable instructions that, when executed, cause the machine to perform the radar signal processing method as described in any one of claims 1 to 7.

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