A method and system for radar signal processing

Abstract

This application provides a method and system for radar signal processing, including pulse compression processing of echo signals and moving target detection. The method uses Fast Fourier Transform (FFT) for pulse compression processing and moving target detection instead of traditional filter banks, which greatly reduces the computational load of convolution. Furthermore, the use of FFT simplifies the hardware implementation structure and improves the feasibility and real-time performance of hardware implementation.

Description

Technical Field

[0001] This application belongs to the field of radar detection technology, and in particular relates to a method and system for radar signal processing. Background Technology

[0002] Radar identifies targets and obtains information such as their range and speed by transmitting signals and processing the received echo signals. However, in real-world environments, various clutter such as rain, snow, atmospheric conditions, and ground features constantly exist around targets. This clutter hinders the radar's accurate target identification, easily leading to misjudgments and missed detections, resulting in radar detection failures.

[0003] When processing radar echo signals, matched filters are typically used to compress multiple sets of echo signals into narrow pulses. This pulse compression with matched filters also introduces Doppler shift, and the output of linear frequency modulated (LFM) signals from matched filters can introduce high sidelobes, potentially masking weak targets. Weighting is often used in the time domain, while windowing is primarily used in the frequency domain. However, regardless of the method, the main lobe will widen, making the system less sensitive. After pulse compression, moving target detection is performed. When clutter and moving target echoes are displayed simultaneously on the radar screen, target observation becomes very difficult. If the target is in a strong clutter background, weak targets will be submerged and difficult to detect. Target echoes and clutter are difficult to distinguish in the time domain, thus achieving clutter cancellation. The main purpose of radar is to separate weak target signals from noise and improve the signal-to-noise ratio. To extract the signal, it must be amplified. However, noise is also amplified at the same time, because they always coexist. Furthermore, the amplifier circuit itself also has noise. After amplification, the ratio of signal to noise becomes smaller, which is even more detrimental to extracting useful echo signals.

[0004] Two major problems radar faces when detecting targets are false alarms and missed detections. The main reasons are: 1. Radar is affected by strong clutter and interference when detecting targets; 2. When receiving target signals, radar also receives noise, clutter, and interference signals, which are random and their strength changes constantly.

[0005] Constant False Alarm Rate (CFAR) is an abbreviation for Constant False Alarm Rate. In radar signal detection, when the intensity of external interference changes, the radar can automatically adjust its sensitivity to maintain a constant false alarm probability; this characteristic is called the constant CFAR characteristic. CFAR detection is a crucial component of automatic radar target detection. As the first step in extracting targets from the radar, it forms the basis for further target identification. The false alarm rate refers to the probability that a reconnaissance device misidentifies noise or other interference signals as threatening radiation sources per unit of time. CFAR detection demonstrates the stability and reliability of the detection algorithm.

[0006] Constant false alarm rate (CFAR) detection of radar signals requires that the false alarm probability remain constant. This is mainly because in radar signal detection, the optimal detection of the signal usually adopts the Neyman-Pearson criterion, which is to maximize the probability of correct detection while maintaining a constant false alarm probability.

[0007] Currently, commonly used CFAR algorithms include Cell Average CFAR (CA-CFAR), Small Select CFAR Detector (SO-CFAR), Large Select CFAR Detector (GO-CFAR), and Ordered CFAR Detector (OS-CFAR). However, these algorithms only show slightly better detection performance in specific clutter types, and are not ideal in other environments. Cell Average CFAR and Large Select CFAR detectors show a decrease in multi-target detection probability, while Small Select CFAR detectors show a decrease in detection probability in homogeneous environments.

[0008] When using the CFAR algorithm for target detection, traditional CFAR detectors are mostly based on exponential or Rayleigh distributions. However, in radar target detection, the environment is highly variable, and environmental clutter often does not strictly conform to Rayleigh or exponential distributions. If a fixed traditional CFAR detector is still used, the poor model matching of the background clutter will prevent the detector from maintaining a constant false alarm rate. CA-CFAR performs poorly when detecting multiple targets. This is partly because CA-CFAR estimates the noise power level based on the mean of a reference cell. The presence of multiple targets inflates the overall mean, resulting in an excessively large threshold T, causing some targets with lower signal energy to be missed. Furthermore, if two targets are very close, the threshold near these targets will also increase, covering targets with lower energy and causing missed detections. Since actual millimeter-wave radar detection data contains various background clutter, primarily non-uniform clutter, a radar system using only a single CFAR detection algorithm cannot achieve the performance required for millimeter-wave radar detection, thus exhibiting numerous shortcomings. Summary of the Invention

[0009] To address at least one of the aforementioned problems, embodiments of this application provide a radar signal processing method and system, including at least a portion of echo signal pulse compression processing and moving target detection. By determining the power of clutter background within the echo signal window, different constant false alarm rate (CFAR) processing methods are adopted in single-target and multi-target scenarios to achieve clutter filtering and suppression, and further improve the ability to locate and identify radar targets.

[0010] The first aspect of this application provides a method for radar signal processing, including:

[0011] Determine the sequence of matched filter parameters based on at least one radar transmitted signal;

[0012] Based on the matched filter parameter sequence and the radar echo signal sequence, and combined with Fourier transform, a two-dimensional matrix for pulse compression is generated.

[0013] The difference between the signal value of the i-th row in the n-th column of the two-dimensional matrix and the signal value of the i-th row in the (n-1)-th column is used as the signal value of the i-th row in the n-th column to generate a canceled target detection matrix; n and i are both positive integers greater than 1;

[0014] The range and velocity information of the radar-detected target are determined based on the aforementioned counteracting target detection matrix.

[0015] In an optional embodiment, generating a two-dimensional matrix for pulse compression based on the matched filter parameter sequence and the radar echo signal sequence, combined with Fourier transform, includes:

[0016] The matched filter parameter sequence is zero-padded to obtain a first sequence, and the acquired radar echo signal is zero-padded to obtain a second sequence. The lengths of the first and second sequences are greater than the length of the radar echo signal and are both 2. m m is a positive integer greater than 1;

[0017] Based on the first sequence and the second sequence, and combined with Fourier transform, a two-dimensional matrix of pulse compression is obtained.

[0018] In an optional embodiment, obtaining the two-dimensional matrix of pulse compression based on the first sequence and the second sequence, combined with Fourier transform, includes:

[0019] The first sequence and the second sequence are subjected to Fourier transform using a linear modulation signal to obtain the Fourier transform sequences of the two sequences.

[0020] Perform a dot product operation on the Fourier transform sequences of the two sequences to obtain the dot product result;

[0021] The dot product result is coherently demodulated to form a two-dimensional matrix.

[0022] In an optional embodiment, determining the range and velocity information of the radar-detected target based on the canceling target detection matrix includes:

[0023] For the aforementioned target detection matrix, perform a Fast Fourier Transform (FFT) on one row of the sequence to obtain the distance of the echo target;

[0024] For the aforementioned target detection matrix, Fast Fourier Transform (FFT) is performed on points in the same order with different pulse periods column by column to calculate the velocity information of the echo target.

[0025] In an optional embodiment, it further includes:

[0026] The modulus of the Fourier transform-derived anti-movement target detection matrix is ​​calculated to obtain the modulus result.

[0027] Based on the modulus results, select the corresponding algorithm to calculate the gate limit value;

[0028] If the echo signal value is greater than the threshold value, the target is determined to exist.

[0029] In an optional embodiment, based on the modulus result, different algorithms are selected to calculate the gate limit value, including:

[0030] According to the preset algorithm structure, the data of the canceling target detection matrix is ​​divided into a leading edge window, a trailing edge window, and a full window;

[0031] Compare the ratio of the statistical means of the leading edge window and the trailing edge window to determine whether the data type is a multi-target region;

[0032] If it is a multi-target region, the corresponding algorithm is selected to calculate the gate threshold value based on the relationship between the preset mean threshold ratio and the statistical mean ratio of the leading edge window and the trailing edge window. The preset mean threshold ratio is determined based on the statistically uniform and non-uniform environmental background clutter power.

[0033] In an optional embodiment, determining the appropriate algorithm to calculate the threshold value based on the relationship between the preset mean threshold ratio and the statistical mean ratio of the leading edge window and the trailing edge window includes:

[0034] If the preset mean threshold ratio is greater than the statistical mean ratio of the leading edge window and the trailing edge window, and the reciprocal of the preset mean threshold ratio is less than the statistical mean ratio of the leading edge window and the trailing edge window, the mean CFAR algorithm is selected.

[0035] If the preset mean threshold ratio is less than the statistical mean ratio of the leading edge window and the trailing edge window, or if the reciprocal of the preset mean threshold ratio is greater than the statistical mean ratio of the leading edge window and the trailing edge window, the ordered statistical CFAR algorithm is selected.

[0036] A second aspect of this application provides a radar signal processing system, including:

[0037] The matched filter parameter sequence generation module determines the matched filter parameter sequence based on at least one radar transmitted signal.

[0038] The two-dimensional matrix generation module generates a pulse-compressed two-dimensional matrix based on the matched filter parameter sequence and the radar echo signal sequence, combined with Fourier transform.

[0039] For the motion cancellation target detection matrix generation module, the difference between the signal value of the i-th row of the n-th column and the signal value of the i-th row of the (n-1)-th column of the two-dimensional matrix is ​​used as the signal value of the i-th row of the n-th column to generate the motion cancellation target detection matrix; n and i are both positive integers greater than 1;

[0040] The calculation module determines the range and velocity information of the radar-detected target based on the canceled target detection matrix.

[0041] Another aspect of this application provides a terminal device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described above.

[0042] Another aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.

[0043] The beneficial effects of this application embodiment compared with the prior art are as follows: This application proposes a method for processing linear frequency modulated radar signals, including pulse compression processing of echo signals and moving target detection. Pulse compression processing and moving target detection are performed by Fast Fourier Transform (FFT) instead of traditional filter banks, which greatly reduces the amount of computation of convolution. Furthermore, the FFT makes the hardware implementation structure simpler, improving the feasibility and real-time performance of hardware implementation. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is the radar echo signal processing flow described in the embodiments of the present invention;

[0046] Figure 2 This is a block diagram of the moving target detection algorithm described in an embodiment of the present invention;

[0047] Figure 3 This is a schematic diagram of the traditional CFAR algorithm described in an embodiment of the present invention;

[0048] Figure 4 The image shows the MATLAB detection of a single target using the adaptive CAOS-CFAR method described in this embodiment of the invention.

[0049] Figure 5The image shows the MATLAB detection of multiple targets using the adaptive CAOS-CFAR method described in this embodiment of the invention.

[0050] Figure 6 This is a flowchart of the improved radar echo signal processing described in an embodiment of the present invention;

[0051] Figure 7 This is a schematic flowchart illustrating the method provided in one embodiment of this application;

[0052] Figure 8 This is a schematic diagram of the radar signal processing system provided in the embodiments of this application;

[0053] Figure 9 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. Detailed Implementation

[0054] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0055] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0056] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0057] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0058] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0059] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0060] The methods, apparatus, terminal devices, storage media, and computer programs provided in this application will now be described in detail with reference to the accompanying drawings.

[0061] Currently, when processing radar echo signals, matched filters are typically used to compress multiple sets of echo signals into narrow pulses. This pulse compression with matched filters also introduces Doppler shift, and the output of linear frequency modulated (LFM) signals from matched filters can introduce high sidelobes, potentially masking weak targets. Weighting is often used in the time domain, while windowing is primarily used in the frequency domain. However, regardless of the method, the main lobe widens, making the system less sensitive. After pulse compression, moving target detection is performed. When clutter and moving target echoes are displayed simultaneously on the radar screen, target observation becomes very difficult. If the target is in a strong clutter background, weak targets are submerged and difficult to detect. Target echoes and clutter are difficult to distinguish in the time domain, thus achieving clutter cancellation. The main purpose of radar is to separate weak target signals from noise and improve the signal-to-noise ratio. To extract the signal, it must be amplified. However, noise is also amplified at the same time, because they always coexist. Furthermore, the amplifier circuit itself also has noise. After amplification, the ratio of signal to noise becomes smaller, which is even more detrimental to extracting useful echo signals.

[0062] Two major problems radar faces when detecting targets are false alarms and missed detections. The main reasons are: 1. Radar is affected by strong clutter and interference when detecting targets; 2. When receiving target signals, radar also receives noise, clutter, and interference signals, which are random and their strength changes constantly.

[0063] Constant False Alarm Rate (CFAR) is an abbreviation for Constant False Alarm Rate. In radar signal detection, when the intensity of external interference changes, the radar can automatically adjust its sensitivity to maintain a constant false alarm probability; this characteristic is called the constant CFAR characteristic. CFAR detection is a crucial component of automatic radar target detection. As the first step in extracting targets from the radar, it forms the basis for further target identification. The false alarm rate refers to the probability that a reconnaissance device misidentifies noise or other interference signals as threatening radiation sources per unit of time. CFAR detection demonstrates the stability and reliability of the detection algorithm.

[0064] Constant false alarm rate (CFAR) detection of radar signals requires that the false alarm probability remain constant. This is mainly because in radar signal detection, the optimal detection of the signal usually adopts the Neyman-Pearson criterion, which is to maximize the probability of correct detection while maintaining a constant false alarm probability.

[0065] Currently, commonly used CFAR algorithms include Cell Average CFAR (CA-CFAR), Small Select CFAR Detector (SO-CFAR), Large Select CFAR Detector (GO-CFAR), and Ordered CFAR Detector (OS-CFAR). However, these algorithms only show slightly better detection performance in specific clutter types, and are not ideal in other environments. Cell Average CFAR and Large Select CFAR detectors show a decrease in multi-target detection probability, while Small Select CFAR detectors show a decrease in detection probability in homogeneous environments.

[0066] When using the CFAR algorithm for target detection, traditional CFAR detectors are mostly based on exponential or Rayleigh distributions. However, in radar target detection, the environment is highly variable, and environmental clutter often does not strictly conform to Rayleigh or exponential distributions. If a fixed traditional CFAR detector is still used, the poor model matching of the background clutter will prevent the detector from maintaining a constant false alarm rate. CA-CFAR performs poorly when detecting multiple targets. This is partly because CA-CFAR estimates the noise power level based on the mean of a reference cell. The presence of multiple targets inflates the overall mean, resulting in an excessively large threshold T, causing some targets with lower signal energy to be missed. Furthermore, if two targets are very close, the threshold near these targets will also increase, covering targets with lower energy and causing missed detections. Since actual millimeter-wave radar detection data contains various background clutter, primarily non-uniform clutter, a radar system using only a single CFAR detection algorithm cannot achieve the performance required for millimeter-wave radar detection, thus exhibiting numerous shortcomings.

[0067] To address at least one of the aforementioned problems, embodiments of this application provide a radar signal processing method and system, including at least a portion of echo signal pulse compression processing and moving target detection. By determining the power of clutter background within the echo signal window, different constant false alarm rate (CFAR) processing methods are adopted in single-target and multi-target scenarios to achieve clutter filtering and suppression, and further improve the ability to locate and identify radar targets.

[0068] Figure 7 The first aspect of this application illustrates a method for radar signal processing, comprising:

[0069] S1: Determine the sequence of matched filter parameters based on at least one radar transmitted signal;

[0070] S2: Based on the matched filter parameter sequence and the radar echo signal sequence, and combined with Fourier transform, generate a two-dimensional matrix for pulse compression;

[0071] S3: The difference between the signal value of the i-th row of the n-th column and the signal value of the i-th row of the (n-1)-th column of the two-dimensional matrix is ​​used as the signal value of the i-th row of the n-th column to generate a canceled target detection matrix; n and i are both positive integers greater than 1;

[0072] S4: Determine the range and velocity information of the radar-detected target based on the aforementioned counteracting target detection matrix.

[0073] This application proposes a method for processing linear frequency modulated radar signals, including pulse compression processing of echo signals and moving target detection. The method replaces the traditional filter bank with Fast Fourier Transform (FFT) for pulse compression processing and moving target detection, which greatly reduces the computational load of convolution. Furthermore, the use of FFT simplifies the hardware implementation structure and improves the feasibility and real-time performance of hardware implementation.

[0074] In an optional embodiment, generating a two-dimensional matrix for pulse compression based on the matched filter parameter sequence and the radar echo signal sequence, combined with Fourier transform, includes:

[0075] The matched filter parameter sequence is zero-padded to obtain a first sequence, and the acquired radar echo signal is zero-padded to obtain a second sequence. The lengths of the first and second sequences are greater than the length of the radar echo signal and are both 2. m m is a positive integer greater than 1;

[0076] Based on the first sequence and the second sequence, and combined with Fourier transform, a two-dimensional matrix of pulse compression is obtained.

[0077] In an optional embodiment, obtaining the two-dimensional matrix of pulse compression based on the first sequence and the second sequence, combined with Fourier transform, includes:

[0078] The first sequence and the second sequence are subjected to Fourier transform using a linear modulation signal to obtain the Fourier transform sequences of the two sequences.

[0079] Perform a dot product operation on the Fourier transform sequences of the two sequences to obtain the dot product result;

[0080] The dot product result is coherently demodulated to form a two-dimensional matrix.

[0081] In an optional embodiment, determining the range and velocity information of the radar-detected target based on the canceling target detection matrix includes:

[0082] For the aforementioned target detection matrix, perform a Fast Fourier Transform (FFT) on one row of the sequence to obtain the distance of the echo target;

[0083] For the aforementioned target detection matrix, Fast Fourier Transform (FFT) is performed on points in the same order with different pulse periods column by column to calculate the velocity information of the echo target.

[0084] In an optional embodiment, it further includes:

[0085] The modulus of the Fourier transform-derived anti-movement target detection matrix is ​​calculated to obtain the modulus result.

[0086] Based on the modulus results, select the corresponding algorithm to calculate the gate limit value;

[0087] If the echo signal value is greater than the threshold value, the target is determined to exist.

[0088] In an optional embodiment, based on the modulus result, different algorithms are selected to calculate the gate limit value, including:

[0089] According to the preset algorithm structure, the data of the canceling target detection matrix is ​​divided into a leading edge window, a trailing edge window, and a full window;

[0090] Compare the ratio of the statistical means of the leading edge window and the trailing edge window to determine whether the data type is a multi-target region;

[0091] If it is a multi-target region, the corresponding algorithm is selected to calculate the gate threshold value based on the relationship between the preset mean threshold ratio and the statistical mean ratio of the leading edge window and the trailing edge window. The preset mean threshold ratio is determined based on the statistically uniform and non-uniform environmental background clutter power.

[0092] In an optional embodiment, determining the appropriate algorithm to calculate the threshold value based on the relationship between the preset mean threshold ratio and the statistical mean ratio of the leading edge window and the trailing edge window includes:

[0093] If the preset mean threshold ratio is greater than the statistical mean ratio of the leading edge window and the trailing edge window, and the reciprocal of the preset mean threshold ratio is less than the statistical mean ratio of the leading edge window and the trailing edge window, the mean CFAR algorithm is selected.

[0094] If the preset mean threshold ratio is less than the statistical mean ratio of the leading edge window and the trailing edge window, or if the reciprocal of the preset mean threshold ratio is greater than the statistical mean ratio of the leading edge window and the trailing edge window, the ordered statistical CFAR algorithm is selected.

[0095] The following example uses simulated data. First, we simulate the echo signal data of a linear frequency modulated radar carrying range and velocity information for multiple targets using MATLAB.

[0096] Figure 1 This is a flowchart of radar echo signal processing. The radar echo signal processing flow includes ADC sampling, digital pulse compression, moving target detection, and constant false alarm rate (CFAR) detection. These algorithms are used to extract the number of targets and their range and velocity parameters.

[0097] In this embodiment, the complex expression of the linear frequency modulated signal is:

[0098]

[0099] Where T is the pulse width, K = BW / T is called the frequency modulation slope, and BW is the signal bandwidth. rect() is the rectangular window function, and u(t) is the complex envelope.

[0100]

[0101] To perform pulse compression processing, it is necessary to generate matched filter coefficients with the same parameters as the transmitted signal, maintaining the same bandwidth Br, sampling rate Fs, pulse repetition period Tr, and pulse width Tp as the transmitted signal. In other words, the matched filter parameter sequence is determined based on at least one radar transmitted signal.

[0102] The result of FFT processing in the frequency domain is discrete. Discretization in the frequency domain leads to periodization in the time domain. According to the frequency domain sampling theorem, if the periodic continuous spectrum of a finite-time-width sequence x(n) is uniformly sampled, and the number of sampling points N in one period is greater than or at least equal to the finite-time-width of x(n), it is possible to recover the original periodic continuous spectrum from the spectral sample points X(k) without distortion.

[0103] Assuming the sequence of echo signals x(n) to be processed (i.e., the sequence of radar echo signals) has a length of M, and the sequence of matched filter coefficients h(n) (i.e., the sequence of matched filter parameters) has a length of N, pulse compression in the frequency domain requires zero-padding of the sequences. This is because during convolution, the length of the resulting sequence is M + N - 1. Therefore, to process the signal in the frequency domain, the length of the processed sequence must be greater than or equal to M + N - 1 before performing pulse compression on the two sequences. If it is less than M, aliasing will occur.

[0104] The matched filter coefficients and echo signals are padded with zeros to a length that is a power of 2 greater than the echo signal sequence. This zero-padding process is applied to the matched filter parameter sequence to obtain a first sequence. Similarly, the acquired radar echo signals are padded with zeros to obtain a second sequence. The lengths of both the first and second sequences are greater than the length of the radar echo signal and are equal to 2. m m is a positive integer greater than 1.

[0105] The two sets of sequences are then subjected to Fast Fourier Transform (FFT) respectively. The results of the FFT of the two sets of sequences are multiplied by a dot product and then subjected to IFFT again to obtain the pulse compression result, which is the two-dimensional matrix of pulse compression.

[0106] y(n)=IFFT[X(k)·H(k)]=IFFT[FFT[x(n)]·FFT[h(n)]]

[0107] A two-dimensional data matrix is ​​formed from baseband data obtained by coherent demodulation of M consecutive pulse echoes within a CPI. Each column of the two-dimensional data matrix corresponds to continuous sampling of a pulse echo, i.e., continuous range cells. Each element in the column is a complex number representing the real and imaginary (I and Q) components of a range cell. Therefore, each row of the two-dimensional data matrix represents a series of pulse measurements for the same range cell. Specifically, the first and second sequences are subjected to Fourier transforms using a linearly modulated signal to obtain Fourier transform sequences of the two sequences; a dot product operation is performed on the Fourier transform sequences of the two sequences to obtain the dot product result; and the dot product result is coherently demodulated to form a two-dimensional matrix.

[0108] The clutter cancellation MTI function is achieved by subtracting the (n-1)th sequence from the nth sequence row by row. After digital cancellation processing, low-speed and stationary targets are suppressed.

[0109] Figure 2The lateral delay unit shown can constitute an MTI (Mean Transmission Interval), which suppresses low-speed and stationary targets after digital cancellation processing. The longitudinal direction can be viewed as a mathematically weighted summation of each range unit. As shown in the figure, N pulse compression values ​​within the same range unit are delayed, with delay periods ranging from 1 to N; then, a weighted summation is performed, with weights ranging from W1 to Wn. The weight Wn at each end represents... Figure 2 .

[0110] For the obtained two-dimensional matrix, the distance dimension information has been calculated in the one-dimensional direction by Fast Fourier Transform (FFT). Then, Fast Fourier Transform (FFT) is performed on the MTI results column by column on the points in the same order of different pulse periods to calculate the velocity information of the echo target.

[0111] The modulus of the two-dimensional matrix data after velocity-dimensional FFT is calculated and fed into the constant false alarm rate (CFAR) module. CFAR determines the presence of a target. A threshold value is calculated based on different algorithms; if the echo signal value is greater than the threshold value, the target is considered to exist; otherwise, it is considered not to exist. An appropriate number of protection units and reference units are selected.

[0112] In CFAR, detection requires a specified distance cell, often referred to as the "test cell" (CUT). The noise power is derived from the neighboring distance cells. The detection threshold is T, expressed as follows:

[0113] T = a·Pn

[0114] In the formula, Pn represents the noise power estimate, and a is a scaling factor, or threshold factor. From the formula, we can see that the threshold calculation corresponds to the data; by choosing a suitable threshold factor, the false alarm probability can be kept constant.

[0115] Assuming a single pulse data point passes through the detector, and no pulses arrive, the threshold factor can be expressed as:

[0116] a=N*P -1 / N-1

[0117] In the formula, P represents the false alarm rate.

[0118] like Figure 3 This is a block diagram illustrating the principle of the traditional CFAR algorithm. Since CA-CFAR and OS-CFAR share the same structure—namely, the data under test unit, protection unit, reference unit, decision unit, and front and rear sliding window structures—they can be used as a shared module for both CA-CFAR and OS-CFAR.

[0119] Figure 6 The improved signal processing flowchart shows that when performing constant false alarm rate (CFAR) detection on the signal under test, the process first follows... Figure 3 In the traditional algorithm structure, the data is divided into a leading-edge window, a trailing-edge window, and a full window. Then, by comparing the statistical mean ratio SMR of the leading-edge and trailing-edge windows, it is determined whether the data type is a multi-target area. The definition of the mean SMR is the ratio of the means of the two windows before and after.

[0120]

[0121] When there are targets or other types of clutter in the window, the value of SMR increases or decreases sharply. When there are more targets and they appear in a certain window simultaneously, the change is more剧烈. The one-dimensional data is cyclically detected in the two-dimensional matrix in turn. A mean threshold ratio KMR is set, and KMR is obtained from the statistical uniform and non-uniform environmental background clutter power.

[0122] When KMR - 1 < SMR < KMR, the background clutter is uniform, and at this time the system determines to select the mean CFAR algorithm for processing;

[0123] When SMR < KMR - 1 or SMR > KMR, the background clutter is non-uniform, and the "shadowing effect" of the target may occur. At this time, the system determines to use the ordered statistical CFAR algorithm for processing.

[0124] In this example, the false alarm rate is set to 10e-6, the number of reference units is 32, the number of protection units is 2, and the threshold factor is calculated through the formula. Figure 4 , Figure 5 It is the matlab simulation diagram for single-target and multi-target detection using adaptive CAOS-CFAR constant false alarm. It suppresses clutter and extracts the target, meeting the system requirements.

[0125] It can be seen that this application has the following technical effects:

[0126] 1. Pulse compression processing and moving target detection are carried out through the fast Fourier transform FFT instead of the traditional filter bank, greatly reducing the computational amount of convolution, and the structure for hardware implementation is relatively simple through FFT, improving the feasibility and real-time performance of hardware implementation.

[0127] 2. Adaptive constant false alarm detection using different CFAR algorithms according to different clutter environmental backgrounds. The CA-CFAR detector is selected in a uniform clutter environment, and the OS-CFAR detector is selected in a non-uniform environment. On the one hand, it can effectively maintain the detection performance of the radar under single-target and multi-target conditions, and on the other hand, it can greatly reduce resource consumption and better meet the requirements of high-performance radar detection.

[0128] 3. Adaptive constant false alarm rate (CFAR) detection is employed to enhance the radar's target detection capability. By studying the difference in background clutter power values ​​between uniform and non-uniform environments, a mean-threshold ratio is determined. The actual mean is compared with this mean-threshold ratio to select a suitable CFAR algorithm. This approach effectively maintains the radar's detection performance under single-target and multi-target conditions while significantly reducing resource consumption, thus better meeting the requirements for high-performance radar detection.

[0129] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0130] Corresponding to the method described in the above embodiments, Figure 8 The diagram shows a structural block diagram of the system provided in the embodiments of this application. For ease of explanation, only the parts related to the embodiments of this application are shown, such as... Figure 8 As shown, a radar signal processing system is characterized by comprising:

[0131] The matched filter parameter sequence generation module determines the matched filter parameter sequence based on at least one radar transmitted signal.

[0132] The two-dimensional matrix generation module generates a pulse-compressed two-dimensional matrix based on the matched filter parameter sequence and the radar echo signal sequence, combined with Fourier transform.

[0133] For the motion cancellation target detection matrix generation module, the difference between the signal value of the i-th row of the n-th column and the signal value of the i-th row of the (n-1)-th column of the two-dimensional matrix is ​​used as the signal value of the i-th row of the n-th column to generate the motion cancellation target detection matrix; n and i are both positive integers greater than 1;

[0134] The calculation module determines the range and velocity information of the radar-detected target based on the canceled target detection matrix.

[0135] This application proposes a method for processing linear frequency modulated radar signals, including pulse compression processing of echo signals and moving target detection. The method replaces the traditional filter bank with Fast Fourier Transform (FFT) for pulse compression processing and moving target detection, which greatly reduces the computational load of convolution. Furthermore, the use of FFT simplifies the hardware implementation structure and improves the feasibility and real-time performance of hardware implementation.

[0136] To implement the above embodiments, this application also proposes a terminal device.

[0137] Figure 9 This is a schematic diagram of the structure of a terminal device according to an embodiment of this application.

[0138] like Figure 9As shown, the terminal device 600 includes:

[0139] The system includes a memory 610 and at least one processor 620, and a bus 630 connecting the different components (including the memory 610 and the processor 620). The memory 610 stores a computer program, which, when executed by the processor 620, implements the method described in the embodiments of this application.

[0140] Bus 630 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0141] Terminal device 600 typically includes various electronically readable media. These media can be any available media that can be accessed by terminal device 600, including volatile and non-volatile media, removable and non-removable media.

[0142] Memory 610 may also include computer system readable media in the form of volatile memory, such as random access memory (RAM) 640 and / or cache memory 650. Terminal device 600 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 660 can be used to read and write non-removable, non-volatile magnetic media (…). Figure 9 Not shown; usually referred to as a "hard drive"). Although Figure 9 As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 630 via one or more data media interfaces. Memory 610 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0143] A program / utility 680 having a set (at least one) of program modules 670 may be stored in, for example, memory 610. Such program modules 670 include—but are not limited to—an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 670 typically perform the functions and / or methods described in the embodiments of this application.

[0144] Terminal device 600 can also communicate with one or more external devices 690 (e.g., keyboard, pointing device, display 691, etc.), one or more devices that enable a user to interact with terminal device 600, and / or any device that enables terminal device 600 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 692. Furthermore, terminal device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 693. As shown, network adapter 693 communicates with other modules of terminal device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with terminal device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0145] The processor 620 executes various functional applications and data processing by running programs stored in the memory 610.

[0146] It should be noted that the implementation process and technical principles of the terminal device in this embodiment are explained in the foregoing description of the method in the embodiment of this application, and will not be repeated here.

[0147] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0148] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0149] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0150] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0151] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0152] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0153] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0154] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method of radar signal processing, characterized by, include: Determine the sequence of matched filter parameters based on at least one radar transmitted signal; Based on the matched filter parameter sequence and the radar echo signal sequence, and combined with Fourier transform, a two-dimensional matrix for pulse compression is generated. The difference between the signal value of the i-th row in the n-th column of the two-dimensional matrix and the signal value of the i-th row in the (n-1)-th column is used as the signal value of the i-th row in the n-th column to generate a canceled target detection matrix; n and i are both positive integers greater than 1; The range and velocity information of the radar-detected target are determined based on the aforementioned target detection matrix. The modulus of the Fourier transform-derived anti-movement target detection matrix is ​​calculated to obtain the modulus result. Based on the modulus results, select the corresponding algorithm to calculate the gate limit value; If the echo signal value is greater than the threshold value, the target is determined to exist; The step of generating a two-dimensional matrix for pulse compression based on the matched filter parameter sequence and the radar echo signal sequence, combined with Fourier transform, includes: The matched filter parameter sequence is zero-padded to obtain a first sequence, and the acquired radar echo signal is zero-padded to obtain a second sequence. The lengths of the first and second sequences are greater than the length of the radar echo signal and are both 2. m m is a positive integer greater than 1; Based on the first sequence and the second sequence, and combined with Fourier transform, a two-dimensional matrix of pulse compression is obtained; The step of obtaining a two-dimensional matrix for pulse compression based on the first sequence and the second sequence, combined with Fourier transform, includes: The first sequence and the second sequence are subjected to Fourier transform using a linear modulation signal to obtain the Fourier transform sequences of the two sequences. Perform a dot product operation on the Fourier transform sequences of the two sequences to obtain the dot product result; The dot product result is coherently demodulated to form a two-dimensional matrix; The step of combining the modulus results and selecting different algorithms to calculate the gate limit value includes: According to the preset algorithm structure, the data of the canceling target detection matrix is ​​divided into a leading edge window, a trailing edge window, and a full window; Compare the ratio of the statistical means of the leading edge window and the trailing edge window to determine whether the data type is a multi-target region; If it is a multi-target region, the corresponding algorithm is selected to calculate the gate threshold value based on the relationship between the preset mean threshold ratio and the statistical mean ratio of the leading edge window and the trailing edge window. The preset mean threshold ratio is determined based on the statistically uniform and non-uniform environmental background clutter power.

2. The method of radar signal processing according to claim 1, characterized in that, The step of determining the range and velocity information of the radar-detected target based on the anti-motion target detection matrix includes: For the aforementioned target detection matrix, perform a Fast Fourier Transform (FFT) on one row of the sequence to obtain the distance of the echo target; For the aforementioned target detection matrix, Fast Fourier Transform (FFT) is performed on points in the same order with different pulse periods column by column to calculate the velocity information of the echo target.

3. The method of radar signal processing according to claim 1, characterized in that, The step of determining the appropriate algorithm to calculate the threshold value based on the relationship between the preset mean threshold ratio and the statistical mean ratio of the leading edge window and the trailing edge window includes: If the preset mean threshold ratio is greater than the statistical mean ratio of the leading edge window and the trailing edge window, and the reciprocal of the preset mean threshold ratio is less than the statistical mean ratio of the leading edge window and the trailing edge window, the mean CFAR algorithm is selected. If the preset mean threshold ratio is less than the statistical mean ratio of the leading edge window and the trailing edge window, or if the reciprocal of the preset mean threshold ratio is greater than the statistical mean ratio of the leading edge window and the trailing edge window, the ordered statistical CFAR algorithm is selected.

4. A radar signal processing system, characterized in that, A method for implementing radar signal processing according to any one of claims 1-3; The radar signal processing system includes: A matched filter parameter sequence generation module is used to determine a matched filter parameter sequence based on at least one radar transmitted signal; The two-dimensional matrix generation module is used to generate a pulse compression two-dimensional matrix based on the matched filter parameter sequence and the radar echo signal sequence, combined with Fourier transform. The target detection matrix generation module is used to take the difference between the signal value of the i-th row of the n-th column and the signal value of the i-th row of the (n-1)-th column of the two-dimensional matrix and use it as the signal value of the i-th row of the n-th column to generate the target detection matrix; n and i are both positive integers greater than 1; The calculation module is used to determine the range and velocity information of the radar-detected target based on the canceling target detection matrix.

5. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-3.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-3.

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