Passive interference suppression method for radar data

Abstract

The invention belongs to the field of radar signal processing, and particularly relates to a passive interference suppression method for radar data. The objective of the invention is to solve the problem of low target identification accuracy when the radar is passively interfered. The invention provides a passive interference suppression method for radar data, and the method comprises the steps: 1, obtaining to-be-suppressed radar data; step 2, carrying out data preprocessing on the obtained radar data to be suppressed to obtain preprocessed radar data; 3, carrying out the extraction processing of the preprocessed radar data, and obtaining passive interference features; and step 4, according to the extracted passive interference features, passive interference removal processing is performed on the radar data to be suppressed, and radar data after passive interference information removal are obtained. The problem that the target identification accuracy is low when the radar is passively interfered is solved.

Description

Technical Field

[0001] This invention belongs to the field of radar signal processing, and particularly relates to a passive interference suppression method for radar data. Background Technology

[0002] Radar plays an important role in fields such as environmental monitoring and marine target surveillance.

[0003] Existing radars include pulse radar and SAR radar. Pulse radar transmits short radio frequency pulses and receives the echo signals reflected back from the target to achieve functions such as target detection, ranging, velocity measurement and tracking.

[0004] SAR radar uses synthetic aperture technology to generate high-resolution radar images by utilizing the relative motion between the radar and the target, enabling functions such as target detection, imaging, and identification.

[0005] In practical applications, radar may be subject to various passive interferences, including: corner jamming and chaff jamming;

[0006] Corner interference is usually caused by corner reflectors. For example, the most common application of corner reflectors in daily life is the yellow reflective plastic light at the rear of a bicycle. In fact, the plastic light itself cannot emit light, but when a light shines on it at night, it can emit light. The reason is that the plastic light is a corner reflector, which reflects the light back in the original direction, thus ensuring that it will not be hit by a car behind it because it cannot see it at night.

[0007] These corner reflectors are located within the same resolution cell as the real target, causing the pulse radar's measurement angle to point towards their center of mass. Since the radar echo from the corner reflectors is typically stronger than the echo from the real target, it also causes a deviation in the pulse radar's detection results, resulting in the radar's tracking point deviating from the real target.

[0008] Chaff jamming is another common passive jamming method. Chaff jamming is generated by large clouds of chaff in the air. These chaff clouds can be confused with real targets in radar images, leading to deviations in radar detection results and affecting the accuracy of target identification. This results in low detection accuracy in existing radar signal processing technologies. Summary of the Invention

[0009] The purpose of this invention is to address the problem of low target identification accuracy in radar systems subjected to passive interference. A passive interference suppression method for radar data is provided, comprising:

[0010] Step 1: Acquire the radar data to be suppressed;

[0011] Step 2: Perform data preprocessing on the acquired radar data to be suppressed to obtain preprocessed radar data;

[0012] Step 3: Extract and process the preprocessed radar data to obtain passive interference characteristics;

[0013] Step 4: Based on the extracted passive interference features, perform passive interference removal processing on the radar data to be suppressed to obtain radar data with passive interference removed.

[0014] Furthermore, the radar data to be suppressed in step one includes Z-frame radar data, where Z is a positive integer;

[0015] Each frame of radar data contains M pulse data; M is a positive integer.

[0016] The radar data includes: pulse radar data or SAR radar data;

[0017] The SAR imaging data to be suppressed is range pulse compressed pulse data, which is range pulse compressed pulse data obtained from the SAR system; the value of M ranges from five to ten.

[0018] In synthetic aperture radar (SAR) systems, pulse compression is a well-known signal processing technique. Pulse compression is used to improve the range resolution of the radar.

[0019] In simple terms, it involves transmitting a wideband modulated pulse signal and then compressing the received echo signal at the receiving end to achieve higher resolution in the range direction (i.e., the radial direction from which the radar reaches the target).

[0020] For example, traditional radar pulse signals have low range resolution due to their large width. By employing pulse compression technology, the echo signal is compressed in the range direction without changing the pulse energy, much like compressing a wide "signal cluster" into a narrow "signal spike." This allows for more precise differentiation of different targets at relatively close range. This processing method is well-known in the field.

[0021] In each scan cycle, a monopulse radar transmits and receives a series of pulse signals, which form the basic framework of the radar data. Each frame of data typically contains five to ten pulses, which carry key information such as the target's position, velocity, and shape.

[0022] Therefore, both pulse radar data and SAR radar data consist of multiple frames of radar data;

[0023] Here, a "frame" is similar to the concept of an image or a unit of data. In SAR systems, data is processed and transmitted in frames. Just like shooting video, which is composed of frames, data in a SAR system is also acquired and processed in frames.

[0024] Each frame of SAR imaging data contains M pulse data, meaning that in one frame (i.e., one complete image acquisition process), the SAR radar emits multiple pulse signals to scan an area. The data returned by these pulse signals is recorded, and each frame of image data is actually composed of data returned by multiple pulses. The same applies to pulse radar;

[0025] In step two, the acquired radar data to be suppressed is preprocessed to obtain preprocessed radar data; the specific process is as follows:

[0026] Step 21: Set the sliding window algorithm parameters.

[0027] The sliding window algorithm parameters include: determining the window size and window spacing of the sliding window;

[0028] The window interval is C pulse widths, where C is a positive integer;

[0029] The window size and window spacing of the sliding window are obtained according to the resolution of the radar system and the target characteristics. The initial window width is set to N pulse widths, where N is a positive integer.

[0030] This invention determines the optimal window size by comparing data processing results (such as target detection accuracy and computation time) under different window sizes through experiments. The parameters are as follows:

[0031] For radar signals with a resolution of 3m, and for obstructed targets with a size of 200-300 meters, a resolution of 20×20 pixels is recommended.

[0032] Regarding the window size, if it is too large, the target will be suppressed even if it is obscured by the foil strips; if it is too small, the computational load will increase, and the foil strip suppression rate will decrease.

[0033] Smaller window size increases computational load, but does not necessarily improve suppression effectiveness.

[0034] Step 22: Use a sliding window algorithm to process all frames of radar data in the radar data to be suppressed, and obtain all frames of radar data after sliding window processing;

[0035] Steps 2 and 3: Use all the radar data frames processed by the sliding window as preprocessed radar data.

[0036] The beneficial effects of this invention are as follows:

[0037] (1) By deeply exploring the statistical characteristics of signals, this invention successfully constructs a complete chaff interference suppression mechanism. This mechanism first utilizes advanced techniques such as sliding window processing, kurtosis characteristic statistics, and non-circularity characteristic statistics to conduct a comprehensive characteristic analysis of SAR imaging data, accurately capturing abnormal features in the signal. Based on this, through innovative comprehensive statistical characteristic weighted processing, this invention achieves effective suppression of chaff information. This breakthrough not only significantly reduces the impact of chaff interference on target detection but also greatly improves the performance of the SAR imaging system in complex environments, enabling SAR to capture target information more accurately and providing solid data support for subsequent signal processing and decision-making.

[0038] (2) Building upon the successful suppression of chaff interference, this invention further introduces peak point count detection technology. This technology achieves high-precision target signal identification through a series of sophisticated operations, including upper envelope extraction, maximum point localization, peak point count statistics, and threshold determination, on the processed radar signal data. In particular, through an innovative weighting strategy, this invention can comprehensively consider the number, significance, and stability of peak points, thereby accurately screening potential target locations. The introduction of this technology not only significantly improves the accuracy of target detection but also greatly enhances the stability of detection, enabling the SAR system to maintain excellent performance in various complex environments. Attached Figure Description

[0039] Figure 1 This is a flowchart of a chaff interference suppression method for radar data according to the present invention;

[0040] Figure 2 This is a flowchart of the SAR radar data range and azimuth sliding window processing of the present invention;

[0041] Figure 3 This is a flowchart of the characteristic statistical suppression foil of the present invention;

[0042] Figure 4 This is a flowchart of a method for suppressing corner reflection interference in radar data according to the present invention;

[0043] Figure 5 This is a flowchart of the pulse radar data range sliding window processing of the present invention;

[0044] Figure 6 This is a flowchart of the characteristic statistical suppression angle reflection of the present invention;

[0045] Figure 7 This is a flowchart of the peak point number suppression angle reflection process of the present invention. Detailed Implementation

[0046] Specific implementation method one: Combining Figures 1-7 This invention is described.

[0047] Step 1: Acquire the radar data to be suppressed;

[0048] Step 2: Perform data preprocessing on the acquired radar data to be suppressed to obtain preprocessed radar data;

[0049] Step 3: Extract and process the preprocessed radar data to obtain passive interference characteristics;

[0050] Step 4: Based on the extracted passive interference features, perform passive interference removal processing on the radar data to be suppressed to obtain radar data after removing passive interference information.

[0051] This invention distinguishes between interference from real targets and chaff clouds by analyzing the statistical characteristics of radar echoes. Specifically, because the statistical characteristics of the echoes from real targets and chaff clouds differ, their Gaussian properties and signal circularity exhibit significant differences. Therefore, by analyzing the statistical characteristics of the signals and assigning weights, the echoes from chaff clouds can be suppressed, effectively suppressing chaff interference and accurately measuring real targets, thereby improving the radar's detection and tracking performance.

[0052] Specific Implementation Method Two: The difference between this implementation method and Specific Implementation Method One is that...

[0053] The radar data to be suppressed in step one includes Z-frame radar data, where Z is a positive integer;

[0054] Each frame of radar data contains M pulse data; M is a positive integer.

[0055] The radar data includes: pulse radar data or SAR radar data;

[0056] The SAR imaging data to be suppressed is range pulse compressed pulse data, which is range pulse compressed pulse data obtained from the SAR system; the value of M ranges from five to ten.

[0057] In synthetic aperture radar (SAR) systems, pulse compression is a well-known signal processing technique. Pulse compression is used to improve the range resolution of the radar.

[0058] In simple terms, it involves transmitting a wideband modulated pulse signal and then compressing the received echo signal at the receiving end to achieve higher resolution in the range direction (i.e., the radial direction from which the radar reaches the target).

[0059] For example, traditional radar pulse signals have low range resolution due to their large width. By employing pulse compression technology, the echo signal is compressed in the range direction without changing the pulse energy, much like compressing a wide "signal cluster" into a narrow "signal spike." This allows for more precise differentiation of different targets at relatively close range. This processing method is well-known in the field.

[0060] In each scan cycle, a monopulse radar transmits and receives a series of pulse signals, which form the basic framework of the radar data. Each frame of data typically contains five to ten pulses, which carry key information such as the target's position, velocity, and shape.

[0061] Therefore, both pulse radar data and SAR radar data consist of multiple frames of radar data;

[0062] Here, a "frame" is similar to the concept of an image or a unit of data. In SAR systems, data is processed and transmitted in frames. Just like shooting video, which is composed of frames, data in a SAR system is also acquired and processed in frames.

[0063] Each frame of SAR imaging data contains M pulse data, meaning that in one frame (i.e., one complete image acquisition process), the SAR radar emits multiple pulse signals to scan an area. The data returned by these pulse signals is recorded, and each frame of image data is actually composed of data returned by multiple pulses. The same applies to pulse radar;

[0064] In step two, the acquired radar data to be suppressed is preprocessed to obtain preprocessed radar data; the specific process is as follows:

[0065] Step 21: Set the sliding window algorithm parameters.

[0066] The sliding window algorithm parameters include: determining the window size and window spacing of the sliding window;

[0067] The window interval is C pulse widths, where C is a positive integer;

[0068] The window size and window spacing of the sliding window are obtained according to the resolution of the radar system and the target characteristics. The initial window width is set to N pulse widths, where N is a positive integer.

[0069] This invention determines the optimal window size by comparing data processing results (such as target detection accuracy and computation time) under different window sizes through experiments. The parameters are as follows:

[0070] For radar signals with a resolution of 3m, and for obstructed targets with a size of 200-300 meters, a resolution of 20×20 pixels is recommended.

[0071] Regarding the window size, if it is too large, the target will be suppressed even if it is obscured by the foil strips; if it is too small, the computational load will increase, and the foil strip suppression rate will decrease.

[0072] Smaller window size increases computational load, but does not necessarily improve suppression effectiveness.

[0073] Step 22: Use a sliding window algorithm to process all frames of radar data in the radar data to be suppressed, and obtain all frames of radar data after sliding window processing;

[0074] Steps 2 and 3: Use all frames of radar data after sliding window processing as the preprocessed radar data.

[0075] The other steps and parameters are the same as in Specific Implementation Method 1.

[0076] Specific Implementation Method Three: The difference between this implementation method and Specific Implementation Method One is that...

[0077] In step two, a sliding window algorithm is used to process all frames of radar data in the radar data to be suppressed, resulting in all frames of radar data after sliding window processing. The specific process is as follows:

[0078] A sliding window is used to slide across a frame of radar data, and a data segment of one window is extracted at each sliding window interval; the sliding direction includes: along the range direction and the azimuth direction.

[0079] When the radar is a SAR radar, the sliding direction is along the range and azimuth directions;

[0080] When the radar is a pulse radar, the sliding direction is only along the range direction;

[0081] Pulse radar does have azimuth, but it measures azimuth by pointing the antenna beam in space. However, its azimuth resolution is limited by the physical size of the antenna and can only meet the needs of "target localization" (such as knowing which direction the target is in). It cannot achieve "high-resolution imaging" (such as seeing vehicles and buildings on the ground) through "synthetic aperture" technology like SAR radar.

[0082] Finally, after the sliding window covers the entire radar data, complex signal data of B windows are obtained; B is a positive integer.

[0083] The obtained complex signal data from the B windows are combined into a single frame of radar data after sliding window processing.

[0084] A frame of radar data after sliding window processing includes B window data segments, the number of which depends on the window size and window interval. Each data segment obtained by sliding one window interval is stored as an independent processing unit for subsequent statistical characteristic analysis.

[0085] The other steps and parameters are the same as in one of the specific implementation methods one or two.

[0086] Specific Implementation Method Four: This implementation method differs from Specific Implementation Methods One through Four in that...

[0087] The passive interference features in step three include: kurtosis features and non-circularity features.

[0088] In step three, the preprocessed radar data is extracted and processed to obtain passive interference characteristics. The specific process is as follows:

[0089] Step 31: Calculate the kurtosis value of each window in the preprocessed radar data to form the kurtosis feature;

[0090] Step 32: Calculate the non-circularity value of each window in the preprocessed radar data to form the non-circularity feature.

[0091] The other steps and parameters are the same as those in one of the specific implementation methods one to three.

[0092] Specific Implementation Method Five: The difference between this implementation method and Specific Implementation Methods One to Four is that...

[0093] In step three, the kurtosis value of the complex signal data in the b-th window of the preprocessed z-th frame radar data is calculated. The specific process is as follows:

[0094] When the radar is a SAR radar, the formula for calculating the kurtosis value is expressed as:

[0095]

[0096] In the formula, This represents the complex signal data extracted from the b-th window of the z-th frame of the SAR radar; Let x represent the statistical mean, y represent the distance variable, and y represent the orientation variable.

[0097] When the radar is a pulse radar, the formula for calculating the kurtosis value is expressed as:

[0098]

[0099] in, This represents the complex signal data extracted from the b-th window of the z-th frame of the pulse radar; t represents the time variable.

[0100] Kurtosis, a statistical measure describing the shape of a data distribution, is used to measure how sharp the data distribution is relative to a normal distribution. For real-valued signals, kurtosis can be obtained by calculating the ratio of the fourth central moment of the signal to the square of its variance.

[0101] However, for complex signals, traditional kurtosis calculation methods are no longer applicable due to the independence of their real and imaginary parts and their correlation. Since the radar data acquired in this invention are all complex signals, this invention uses the above calculation method to calculate kurtosis.

[0102] In practical applications, various optimization strategies can be adopted to improve the calculation efficiency and accuracy of kurtosis values.

[0103] For example, reducing redundant calculations by pre-calculating the mean and variance of the signal; accelerating the calculation of higher-order moments using algorithms such as Fast Fourier Transform (FFT); and improving data processing speed through parallel computing technology. All of the above calculation methods for improving computational efficiency and accuracy are within the scope of protection of this invention.

[0104] The other steps and parameters are the same as those in one of the specific implementation methods one to four.

[0105] Specific Implementation Method Six: The difference between this implementation method and Specific Implementation Methods One to Five is that...

[0106] In step three, the non-circularity value of the complex signal data in the b-th window of the z-th frame in the preprocessed radar data is calculated; the specific process is as follows:

[0107] Step 321: Perform mean-removal processing on the complex signal data of the b-th window in the preprocessed z-th frame radar data to obtain the mean-removed complex signal data;

[0108] Step 322: Based on the mean-removed complex signal data obtained in Step 321, calculate the signal conjugate symmetric component and the signal anticonjugate symmetric component of the complex signal data of the b-th window in the preprocessed z-th frame radar data;

[0109] Step 323: Based on the conjugate and anti-conjugate symmetric components of the signal in the b-th window of the preprocessed z-th frame radar data, calculate the non-circular coefficient of the b-th window in the preprocessed z-th frame radar data.

[0110] Other steps and parameters are the same as in any of the specific implementation methods one to five.

[0111] Specific Implementation Method Seven: The difference between this implementation method and Specific Implementation Methods One through Six is ​​that...

[0112] In step 321, the mean-removed complex signal data of the b-th window in the preprocessed z-th frame radar data is subjected to mean-removed processing to obtain the mean-removed complex signal data. The specific process is as follows:

[0113] Step 3211: Calculate the mean value of the complex signal data in the b-th window of the preprocessed z-th frame of radar data. ,

[0114] Step 3212: Subtract the mean from each data point in the signal within the window. The mean-removed signal is obtained; the specific process is as follows:

[0115] When the radar is a SAR radar, the signal after removing the mean from the b-th window of the z-th frame in the radar data is represented as follows: This can be expressed as a formula:

[0116] ;

[0117] When the radar is a pulse radar, the signal after removing the mean from the b-th window of the z-th frame in the data is represented as follows: ; expressed as a formula:

[0118]

[0119] The specific process for calculating the conjugate symmetric component and anticonjugate symmetric component of the signal in the b-th window of the z-th frame based on the mean-removed signal obtained in step 321 is as follows:

[0120] When the radar is a SAR radar, the signal conjugate symmetric component and the signal anticonjugate symmetric component in the b-th window of the z-th frame.

[0121] The calculation formula is expressed as follows:

[0122] ;

[0123] ;

[0124] This represents the conjugate symmetric component of the signal in the b-th window of the z-th frame of the SAR radar. This represents the anticonjugate symmetric component of the signal in the b-th window of the z-th frame of the SAR radar. express The conjugate signal;

[0125] When the radar is a pulse radar, the signal conjugate symmetric component and the signal anticonjugate symmetric component in the b-th window of the z-th frame.

[0126] The calculation formula is expressed as follows:

[0127] ;

[0128] ;

[0129] This represents the conjugate symmetric component of the signal in the b-th window of the z-th frame of the pulse radar. This represents the anticonjugate symmetric component of the signal in the b-th window of the z-th frame of the pulse radar. express The conjugate signal;

[0130] In step three, the non-circular coefficient of the single-pulse radar signal after mean removal in the b-th window of the z-th frame is calculated based on the conjugate and anti-conjugate symmetric components of the signal. The specific process is as follows:

[0131] When the radar is a SAR radar, the formula for calculating the non-circular coefficient of the mean-free monopulse radar signal in the b-th window of the z-th frame is as follows:

[0132]

[0133] in Let represent the non-circularity of the monopulse radar signal in the b-th window of the z-th frame. The value of the non-circularity ranges from [0,1]. Indicates a non-circular phase;

[0134] When the radar is a pulse radar, the formula for calculating the non-circular coefficient of the single-pulse radar signal after removing the mean in the b-th window of the z-th frame is as follows:

[0135]

[0136] Noncircularity, as a statistic, reflects the non-uniformity of a signal's distribution in the complex plane. For a complex random signal with zero mean... Generally speaking, based on whether their first and second moments possess rotational invariance, signals can be classified into circular signals and non-circular signals.

[0137]

[0138] For any zero-mean signal It is always true; it applies to any signal. It is also always true; if The establishment means Therefore, if So It is a circular signal; otherwise, it is a non-circular signal.

[0139] However, monopulse radar signals are not zero-mean. The target information, noise, and system errors contained in the monopulse radar signal will all cause the signal mean to be non-zero.

[0140] Therefore, traditional methods for calculating non-circularity have limitations when processing monopulse radar signals and require appropriate adjustments to adapt to application scenarios. Non-circularity is typically defined as the second-order statistical property of a complex signal, i.e., the ratio between the conjugate symmetric component and the anti-conjugate symmetric component of the signal. For zero-mean complex signals, this ratio accurately reflects the signal's non-circularity. However, for non-zero-mean monopulse radar signals, preprocessing is necessary to eliminate the influence of the mean on the calculation results. Specifically,

[0141] First, the signal is subjected to mean-reduction processing, which involves calculating the mean of the signal and subtracting the mean from each data point to obtain a signal with zero mean.

[0142] Then, the conjugate symmetric component and the anticonjugate symmetric component are calculated based on the mean-removed signal.

[0143] when hour, Indicates a circular signal; when hour, Indicates a non-circular signal, specifically, when hour, Represents a linear signal

[0144] The other steps and parameters are the same as those in any of the specific implementation methods one to six.

[0145] Specific Implementation Method Eight: The difference between this implementation method and Specific Implementation Methods One to Seven is that...

[0146] In step four, based on the extracted passive interference features, the radar data to be suppressed is subjected to passive interference removal processing to obtain radar data after passive interference removal; the specific process is as follows:

[0147] Step 41: Summarize the kurtosis and non-circularity values ​​in each window to form a global statistical characteristic distribution, and generate a joint feature vector based on the extracted passive interference features;

[0148] Step 42: Construct a weighting strategy and apply it to the joint feature vector to obtain a weighted joint feature vector; the weighting strategy is expressed by the formula:

[0149] ,

[0150] This represents the weighted joint feature.

[0151] Points with high kurtosis and abnormal non-circularity are assigned higher weights, while points with moderate kurtosis and normal non-circularity are assigned lower weights.

[0152] The filtering strategies in step four-two include: foil interference filtering strategy or corner reflection interference filtering strategy;

[0153] Step 43: Perform passive interference removal processing on the radar data to be suppressed according to the filtering strategy to obtain radar data after removing passive interference information. The specific process is as follows:

[0154] A chaff interference filtering strategy is applied to remove chaff interference from the radar data to be suppressed, resulting in radar data with chaff interference removed.

[0155] An angle reflection interference filtering strategy is applied to perform preliminary angle reflection interference removal processing on the radar data to be suppressed, resulting in radar data with preliminary angle reflection interference removal information; a signal peak point detection method is then applied to process the radar data with preliminary angle reflection interference removal information, resulting in radar data with angle reflection interference removed.

[0156] The other steps and parameters are the same as those in any of the specific implementation methods one to seven.

[0157] Specific Implementation Method Nine: The difference between this implementation method and Specific Implementation Methods One through Eight is that...

[0158] The foil interference filtering strategy is expressed as follows:

[0159]

[0160] In the formula, This represents the average joint characteristic of the entire region. t1 represents the maximum joint feature of the entire region, t2 represents the first threshold, and t2 represents the second threshold. This means that both conditions need to be met. This indicates that at least one of the conditions must be met.

[0161] t1 represents the first threshold, and t2 represents the second threshold. These values ​​can be set manually or estimated based on experimental training data samples. These thresholds are used to determine whether the signal is interfered with by foil or affected by background noise, and the degree of influence. The first and second thresholds in this invention are obtained experimentally, and the specific process is expressed by the following formula:

[0162]

[0163] in This represents the average joint characteristics of the target region. The average joint characteristics represented; Represents the maximum joint feature of the target region. The largest joint feature represented,

[0164] Energy attenuation is a signal processing technique for "identifying interference or background". Its core is to eliminate the impact of interference on target detection by significantly reducing signal energy.

[0165] Here, "energy" usually refers to the power or square of the radar echo signal (energy is positively correlated with power / amplitude). For example, if the original signal energy is E, the remaining energy after 90% attenuation is 0.1E.

[0166] Attenuating the energy of interfering signals (chaff or background) is equivalent to reducing their "presence" in the detection system, preventing them from overwhelming the real target signal. The higher the attenuation level, the less impact the interfering signal has on subsequent target detection (such as threshold judgment and target tracking); this is a processing procedure well-known to those skilled in the art.

[0167] The corner reflection interference filtering strategy is expressed as follows:

[0168]

[0169] In the formula, This represents the third threshold. This represents the fourth threshold;

[0170] The specific values ​​of the third and fourth thresholds can be set manually or estimated based on experimental training data samples. These thresholds are used to determine whether the signal is affected by corner reflection interference or background noise, and the degree of influence.

[0171] The third and fourth thresholds in this invention are obtained experimentally, and the specific process is expressed by the following formula:

[0172]

[0173] in The maximum combined characteristic of a ship target. Mean joint characteristics representing ship targets; ship; The average joint feature represented, The largest joint feature represented;

[0174] Compared to the combined characteristics of ship targets, corner reflectors have a sharp amplitude distribution and a high kurtosis value (K>4.2) due to strong specular reflection, while the kurtosis value of ship targets is more similar to the background (K<2.8).

[0175] The phase distribution of corner reflectors exhibits high symmetry, and their non-circularity is also sharper (C>0.65), which is 2-3 times higher than the circularity value (C<0.25) of ship targets.

[0176] The other steps and parameters are the same as in any of the specific implementation methods one through eight.

[0177] Specific Implementation Method Ten: The difference between this implementation method and Specific Implementation Methods One through Nine is that...

[0178] The applied signal peak point detection method processes the radar data after preliminary removal of corner reflection interference information to obtain radar data after removing corner reflection interference information; the specific process is as follows:

[0179] A1: Extract the upper envelope from the radar signal data after angle inversion suppression to obtain the upper envelope signal;

[0180] This effectively isolates and highlights potential target areas, providing a clear signal profile for subsequent steps.

[0181] A2: Based on the acquired upper envelope signal, a local extremum detection algorithm is used to process the signal and identify all local maxima points that satisfy specific conditions. These points represent the highest points of signal intensity within a local range and are a direct reflection of the potential target signal.

[0182] A3: Setting a reasonable threshold and neighborhood window size can further filter out spurious maxima caused by noise, ensuring the accuracy of subsequent analysis.

[0183] For each defined maximum point, define a small interval centered on that point (i.e., the size of the neighborhood window), and count the number N of extreme points (including the original maximum point itself and other significant peaks nearby) that exceed a preset threshold within that interval.

[0184] The selection of this threshold needs to take into account the signal-to-noise ratio, clutter distribution characteristics, and expected strength of the target signal to ensure that noise can be effectively filtered while retaining the target information.

[0185] A4: To further improve the accuracy of target detection, the relative intensity of each maximum point is calculated. The relative intensity of the i-th maximum point is expressed by the formula:

[0186]

[0187] in, Let N be the amplitude of the i-th maximum point, N be the number of extreme points in the neighborhood window that exceed the preset threshold, and the denominator be the average amplitude of all maximum points, reflecting the significance of the target scattering center.

[0188] A5: Assign a weighted score to each maximum point based on the number of peak points and relative intensity:

[0189] The weighted score of the i-th maximum point is expressed by the formula:

[0190]

[0191] Where n is the number of peak points exceeding the threshold in the current interval (e.g., the typical value for ships is higher than 10, and the number of angular reflections is lower than 4). The empirical weights are typically 0.6 / 0.4.

[0192] A6: Based on the weighted score ranking, select the range coordinates of the P maximum points with the highest scores as the ship target positions to obtain radar data after removing angular reflection interference information.

[0193] This strategy not only considers the number of peak points, but also the salience and stability of the peak points, thereby enhancing the robustness of target detection.

[0194] Finally, the proposed detection method was validated and optimized using extensive experimental data and simulated scenarios. Based on the validation results, the upper envelope extraction parameters, the maximum point localization strategy, the peak point count threshold, and the weighting strategy were continuously adjusted to ensure that the method exhibits excellent detection performance under different environmental conditions and target types.

[0195] In the field of radar signal processing, especially in target detection under complex environments, angle reflection suppression technology serves as an effective preprocessing method, significantly enhancing target signals and reducing interference caused by multipath reflection (i.e., angle reflection). However, even after angle reflection suppression, clutter interference and incompletely eliminated angle reflection effects may still remain in the data, posing a significant challenge to the target detection process. To address this challenge, this invention proposes an innovative detection method based on the number of signal peak points to accurately identify target information from data shrouded in complex interference.

[0196] Although corner reflectors cause significant interference in signal amplitude, they differ from ships in the number of peak values. Corner reflector arrays typically have three to four peak values, while ships have more than four. This is because polyhedral corner reflectors are composed of multiple trihedral angles, resulting in a simple and highly symmetrical structure. The scattering mechanism of a trihedral angle determines that it has only one strong scattering point. The number of strong scattering points in an array corner reflector is determined by the number of angle reflectors themselves, further reducing the number of peak values ​​in the angle reflector region in the processed data. Ships, on the other hand, are complex targets, with their range direction formed by the superposition of echoes from multiple scattering centers. Therefore, the number of peak values ​​in the angle reflector region is further reduced in the processed data. Thus, further detection is performed on the processed data based on the number of peak values.

[0197] The other steps and parameters are the same as those in any of the specific implementation methods one to nine.

[0198] Specific Implementation Method Eleven: This implementation method is a SAR chaff interference suppression device based on the statistical characteristics of complex domain echoes. The device includes a processor and a memory. It should be understood that this includes any device including a processor and a memory described in this invention. The device may also include other units and modules that perform display, interaction, processing, control, and other functions through signals or instructions.

[0199] The memory stores at least one instruction, which is loaded and executed by the processor to implement the passive interference suppression method for radar data.

[0200] Those skilled in the art will understand that at least one stored instruction constitutes a computer program product corresponding to a method or system. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0201] This application is described with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products according to embodiments of this application, and can also be used with corresponding devices. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0202] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0203] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0204] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0205] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0206] The above description is merely of preferred embodiments of the present invention. It should be understood that the present invention is not limited to the specific embodiments described above. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent substitutions, and improvements made to the above embodiments without departing from the scope of the present invention, based on the technical essence of the present invention, and within the spirit and principles of the present invention, shall still fall within the protection scope of the present invention.

Claims

1. A passive interference suppression method for radar data, characterized in that, include: Step 1: Acquire the radar data to be suppressed; Step 2: Perform data preprocessing on the acquired radar data to be suppressed to obtain preprocessed radar data; Step 3: Perform feature extraction processing on the preprocessed radar data to obtain passive jamming features; Step 4: Based on the obtained passive interference characteristics, perform passive interference removal processing on the radar data to be suppressed to obtain radar data after removing passive interference information.

2. The passive interference suppression method for radar data according to claim 1, characterized in that, The radar data to be suppressed in step one includes: Z-frame radar data, where Z is a positive integer; Each frame of radar data contains M pulse data; M is a positive integer. The radar data includes: pulse radar data or SAR radar data; In step two, the acquired radar data to be suppressed is preprocessed to obtain preprocessed radar data; the specific process is as follows: Step 21: Set the sliding window algorithm parameters. The sliding window algorithm parameters include: the window size and window spacing of the sliding window; The window interval is set to C pulse widths, where C is a positive integer; Step 22: Use a sliding window algorithm to process all frames of radar data in the radar data to be suppressed, and obtain all frames of radar data after sliding window processing; All frames of radar data after sliding window processing are used as preprocessed radar data; Specifically, the sliding window algorithm is used to process a frame of radar data in the radar data to be suppressed, resulting in a processed frame of radar data. The specific process is as follows: Use a sliding window to slide over a frame of radar data, and extract a data segment of a window every time you slide a window. After the sliding window covers the entire radar data, the complex signal data of B windows is finally obtained; B is a positive integer. The obtained complex signal data from the B windows are combined into a single frame of radar data after sliding window processing.

3. The passive interference suppression method for radar data according to claim 2, characterized in that, The sliding direction of the sliding window on a certain frame of radar data includes: along the range direction and the azimuth direction; When the radar is a SAR radar, the sliding direction is along the range and azimuth directions; When the radar is a pulse radar, the sliding direction is only along the range direction.

4. The passive interference suppression method for radar data according to claim 3, characterized in that, In step three, feature extraction processing is performed on the preprocessed radar data to obtain passive interference features. The specific process is as follows: the passive interference features include kurtosis features and non-circularity features. Step 31: Calculate the kurtosis value of each window in the preprocessed radar data to form the kurtosis feature; Wherein, the kurtosis value of the complex signal data of the b-th window in the preprocessed z-th frame of radar data is represented as ; Step 32: Calculate the non-circularity value of each window in the preprocessed radar data to form a non-circularity feature; The non-circularity value of the complex signal data in the b-th window of the z-th frame in the preprocessed radar data is represented as: .

5. A passive interference suppression method for radar data according to claim 4, characterized in that, In step three, the kurtosis value of the complex signal data in the b-th window of the preprocessed z-th frame radar data is calculated. The specific process is as follows: When the radar is a SAR radar, the formula for calculating the kurtosis value is expressed as: In the formula, This represents the complex signal data extracted from the b-th window of the z-th frame of the SAR radar; Let x represent the statistical mean, y represent the distance variable, and y represent the orientation variable. When the radar is a pulse radar, the formula for calculating the kurtosis value is expressed as: in, This represents the complex signal data extracted from the b-th window of the z-th frame of the pulse radar; t represents the time variable.

6. The passive interference suppression method for radar data according to claim 5, characterized in that, In step three, the non-circularity value of the complex signal data in the b-th window of the z-th frame in the preprocessed radar data is calculated. ; The specific process is as follows: Step 321: Perform mean-removal processing on the complex signal data of the b-th window in the preprocessed z-th frame radar data to obtain the mean-removed complex signal data; Step 322: Based on the mean-removed complex signal data obtained in Step 321, calculate the signal conjugate symmetric component and the signal anticonjugate symmetric component of the complex signal data of the b-th window in the preprocessed z-th frame radar data; Step 323: Based on the conjugate symmetric component and anti-conjugate symmetric component of the signal in the b-th window of the preprocessed z-th frame radar data, calculate the non-circular coefficient of the b-th window in the preprocessed z-th frame radar data.

7. A passive interference suppression method for radar data according to claim 6, characterized in that, In step 321, the mean-removed complex signal data of the b-th window in the preprocessed z-th frame radar data is subjected to mean-removed processing to obtain the mean-removed complex signal data. The specific process is as follows: Step 3211: Calculate the mean value of the complex signal data in the b-th window of the preprocessed z-th frame of radar data. , Step 3212: Subtract the mean from each data point in the signal within the window. The mean-removed signal is obtained; the specific process is as follows: When the radar is a SAR radar, the signal after removing the mean from the b-th window of the z-th frame in the radar data is represented as follows: This can be expressed as a formula: ； When the radar is a pulse radar, the signal after removing the mean from the b-th window of the z-th frame in the data is represented as follows: ; expressed as a formula: The specific process for calculating the conjugate symmetric component and anticonjugate symmetric component of the signal in the b-th window of the z-th frame based on the mean-removed signal obtained in step 321 is as follows: When the radar is a SAR radar, the signal conjugate symmetric component and the signal anticonjugate symmetric component in the b-th window of the z-th frame. The calculation formula is expressed as follows: ； ； This represents the conjugate symmetric component of the signal in the b-th window of the z-th frame of the SAR radar. This represents the anticonjugate symmetric component of the signal in the b-th window of the z-th frame of the SAR radar. express The conjugate signal; When the radar is a pulse radar, the signal conjugate symmetric component and the signal anticonjugate symmetric component in the b-th window of the z-th frame. The calculation formula is expressed as follows: ； ； This represents the conjugate symmetric component of the signal in the b-th window of the z-th frame of the pulse radar. This represents the anticonjugate symmetric component of the signal in the b-th window of the z-th frame of the pulse radar. express The conjugate signal; In step three, the non-circular coefficient of the single-pulse radar signal after mean removal in the b-th window of the z-th frame is calculated based on the conjugate and anti-conjugate symmetric components of the signal. The specific process is as follows: When the radar is a SAR radar, the formula for calculating the non-circular coefficient of the mean-free monopulse radar signal in the b-th window of the z-th frame is as follows: in Let represent the non-circularity of the monopulse radar signal in the b-th window of the z-th frame. The value of the non-circularity ranges from [0,1]. Indicates a non-circular phase; When the radar is a pulse radar, the formula for calculating the non-circular coefficient of the single-pulse radar signal after removing the mean in the b-th window of the z-th frame is as follows: 。 8. A passive interference suppression method for radar data according to claim 7, characterized in that, In step four, based on the extracted passive interference features, the radar data to be suppressed is subjected to passive interference removal processing to obtain radar data after removing passive interference information. The specific process is as follows: Step 41: Generate a joint feature vector based on the extracted passive interference features; apply a weighting strategy to the joint feature vector to obtain a weighted joint feature vector. ; Among them, the weighted joint feature vector The element corresponding to the b-th window in the z-th frame is expressed by the formula: ， This represents the element in the weighted joint feature vector corresponding to the b-th window in the z-th frame. Step 42: Construct the filtering strategy. The filtering strategies include: foil interference filtering strategy or corner reflection interference filtering strategy; Step 43: Based on the filtering strategy and the weighted joint feature vector, perform passive interference removal processing on the radar data to be suppressed to obtain radar data after removing passive interference information. The specific process is as follows: The radar data after passive interference information includes: radar data after removing chaff interference information or radar data after removing corner reflection interference information; When removing chaff interference, a chaff interference filtering strategy is applied to process the radar data to be suppressed to remove chaff interference, resulting in radar data with chaff interference removed. When performing corner reflection interference removal, a corner reflection interference filtering strategy is applied to perform preliminary corner reflection interference removal processing on the radar data to be suppressed, resulting in radar data with preliminary corner reflection interference removal information. Then, a signal peak point detection method is applied to process the radar data with preliminary corner reflection interference removal information, resulting in radar data with corner reflection interference removed.

9. A passive interference suppression method for radar data according to claim 8, characterized in that, The foil interference filtering strategy in step four-two is expressed as follows: In the formula, Indicates the average joint characteristic, t1 represents the maximum joint feature, t2 represents the first threshold, and t2 represents the second threshold. This means that both conditions need to be met. This indicates that at least one of the conditions must be met. The average joint feature is the average of all elements of the weighted joint feature vector; The maximum joint feature is the maximum value of all elements in the weighted joint feature vector; The corner reflection interference filtering strategy in step four-two is expressed as follows: In the formula, This represents the third threshold. This indicates the fourth threshold.

10. A SAR chaff interference suppression device based on complex domain echo statistical characteristics, characterized in that, In step four, the radar data after initial removal of corner reflection interference is processed using a signal peak point detection method to obtain radar data after removing corner reflection interference; the specific process is as follows: A1: Extract the upper envelope from the radar signal data after angle inversion suppression to obtain the upper envelope signal; A2: On the acquired upper envelope signal, a local extremum detection algorithm is used to process the upper envelope signal and identify all local maxima points; A3: Set the threshold and neighborhood window size, and count the number N of maximum points exceeding the preset threshold within the neighborhood window; A4: Calculate the relative intensity at each maximum point. The relative intensity at the i-th maximum point is expressed by the formula: in, Let the magnitude be the value at the i-th maximum point. This represents the magnitude of the j-th maximum point, and N is the number of maximum points within the neighborhood window that exceed a preset threshold. A5: Assign a weighted score to each maximum point based on the number of peak points and relative intensity: The weighted score of the i-th maximum point is expressed by the formula: Where n is the number of peak points exceeding the threshold in the current interval. For experience weights; A6: Based on the weighted score ranking, select the range coordinates of the P maximum points with the highest scores as the ship target positions to obtain radar data after removing angular reflection interference information.

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