Ground reconnaissance radar CFAR detection method and system

Abstract

The invention discloses a ground reconnaissance radar CFAR detection method and system, and belongs to the technical field of radar target detection. The method comprises the following steps: calculating a variable coefficient and a kurtosis statistical magnitude of reference window sample data; adaptively adjusting a weighting factor according to the kurtosis statistical magnitude; and iterating the weighting process to obtain a background clutter power estimation value. According to the method, the data uniformity is reflected by using the variable coefficient, the weighting factor is adaptively adjusted through the kurtosis, strong interference and abnormal values in a reference window are effectively suppressed, and the detection probability in a multi-target environment and the false alarm control capability at the clutter edge are improved. Meanwhile, by optimizing the iteration process, samples are quartered and weighted when conditions are met, the calculation amount is reduced, and the algorithm real-time performance and the environmental adaptability are improved.

Description

Technical Field

[0001] This invention belongs to the field of radar target detection technology, specifically a ground reconnaissance radar CFAR detection method and system. Background Technology

[0002] Target detection, a crucial component of ground-based reconnaissance radar signal processing, is a key node in the process, and its effectiveness forms the cornerstone of evaluating the overall radar system performance. The fundamental goal of this technology is to accurately extract key parameters such as the position and velocity of potential targets from echo signals. However, in practical applications, the observation background is often filled with heterogeneous clutter generated by natural phenomena and human activities. This interference causes significant fluctuations in environmental background power, severely impacting the confidence level of target detection.

[0003] In the performance composition of a radar system, target detection algorithms occupy a core position, and constant false alarm rate (CFAR) processing technology is a key component. Based on the estimation method of background clutter power, various CFAR algorithms developed in academia can be mainly categorized into three types: mean-based (ML), ordered statistical (OS), and other adaptive algorithms. Mean-based CFAR algorithms are computationally simple, estimating clutter power by calculating the arithmetic mean of sampled data within a reference window, and perform excellently in uniform clutter backgrounds. However, their main drawback is insufficient environmental adaptability; when multiple targets are present, strong interference signals can severely affect their detection performance. To overcome this limitation, ordered statistical CFAR (OS-CFAR) algorithms have emerged. This algorithm sorts the reference window samples and selects values ​​in a specific order (e.g., the first...). The OS-CFAR uses a small value to estimate the background power, thus effectively suppressing strong interference within the reference window and maintaining robustness in multi-target environments. However, the performance of OS-CFAR is highly dependent on the order parameter. Choice: If the value is too small, it can easily lead to an increase in false alarms in areas with strong clutter. If the value is too large, it may weaken the detection capability for multiple targets. Given that a single type of CFAR algorithm cannot handle all complex scenarios, adaptive algorithms that can self-adjust according to environmental characteristics have been widely adopted. For example, the weighted iterative CFAR (WAI-CFAR) algorithm weakens the interference of extreme values ​​in the reference window through weighted iteration, thereby generating an adaptive detection threshold and significantly improving the algorithm's detection performance in multi-target environments. Summary of the Invention

[0004] The purpose of this invention is to provide a CFAR detection method and system for ground reconnaissance radar. This improves the environmental adaptability and real-time performance of the algorithm.

[0005] To address the problems in the background art, the technical solution adopted by the present invention is as follows:

[0006] A ground reconnaissance radar CFAR detection method includes the following steps:

[0007] Step 1: Calculate the coefficient of variation and kurtosis statistic;

[0008] Step 2: Obtain the weighting factor based on the calculated kurtosis statistic;

[0009] Step 3: Iterate the weighting process and use the iteration result as the background clutter power estimate.

[0010] Furthermore, the specific process for calculating the coefficient of variation and kurtosis statistic in step 1 is as follows:

[0011] Step 1-1: Radar obtains reference window sample data for different range cells by emitting electromagnetic waves and sampling the echo signals. The coefficient of variation measures the variation of the reference window sample data. The relative proportion of the standard deviation to the mean yields a dimensionless measure of relative dispersion.

[0012] When outliers exist within the reference window, the coefficient of variation (CV) increases, deviating from the theoretical value under the sample distribution. The formula for the coefficient of variation (CV) is:

[0013] ,

[0014] in, This represents the standard deviation of the reference window sample data. This represents the mean of the reference window sample data;

[0015] For the scale parameter is The Weibull distribution has a coefficient of variation (CV) defined as:

[0016] ,

[0017] in, Represents the Gamma function;

[0018] Steps 1-2: Kurtosis describes the degree of centralization of a data distribution. The greater the deviation of the extreme values ​​in the reference window sample data from the mean, the greater the difference between its kurtosis and the theoretical kurtosis under the corresponding distribution. The definition of kurtosis K is:

[0019] ,

[0020] in, The fourth-order center distance of the reference window sample data; X (i) For the reference window sample data, the value of i ranges from 1 to 2N; Var2 (x) represents the square of the variance of the reference window sample data; E(x) represents the mean of the reference window sample data. ;

[0021] For the scale parameter is The theoretical value of the kurtosis K0 in the Weibull distribution is:

[0022] .

[0023] Furthermore, the specific process of step 2 is as follows:

[0024] Introducing a kurtosis factor ΔK to adjust the weighting factor The kurtosis factor ΔK is defined as:

[0025] ,

[0026] The expression for obtaining the new weighting factor is:

[0027] ,

[0028] in, Indicates the center of adjustment of the weighting factor. and The adjustment coefficient is used to obtain the adaptive weighting factor. After that, proceed After the next iteration, the weighted values ​​are:

[0029] .

[0030] Furthermore, step 3 iterates the weighting process, specifically as follows:

[0031] After each iteration, check whether the coefficient of variation and kurtosis of the current remaining samples are less than the theoretical values; check whether the number of the current remaining samples is less than half of the original number; if any of these conditions are met, divide the remaining samples into four equal parts, calculate the mean of each part, and perform three weighted iterations on these means to obtain the final background clutter power estimate.

[0032] A ground reconnaissance radar CFAR detection system, used to implement any one of the ground reconnaissance radar CFAR detection methods, characterized in that it includes:

[0033] The first module calculates the coefficient of variation and kurtosis statistic.

[0034] The second module obtains the weighting factor based on the calculated coefficient of variation and kurtosis statistic.

[0035] The third module iterates the weighting process and uses the iteration results as an estimate of the background clutter power.

[0036] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method.

[0037] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method.

[0038] A computer program product includes a computer program that, when executed by a processor, implements the steps of the method.

[0039] Compared with the prior art, the present invention has the following significant advantages:

[0040] (1) The present invention uses the coefficient of variation and kurtosis statistics to obtain the weighting factor. With the support of statistical theory, the setting of the weighting factor has a clearer physical meaning and improves the environmental adaptability of the algorithm. (2) The present invention iterates the weighting process, which reduces the amount of computation and improves the real-time performance of the algorithm. Attached Figure Description

[0041] Figure 1 This is a block diagram of the CKWAI-CFAR algorithm structure.

[0042] Figure 2 This is a comparison chart of the detection probabilities of CKWAI-CFAR under three interference scenarios on one side of the target.

[0043] Figure 3 This is a comparison chart of the detection probabilities of CKWAI-CFAR under three interference scenarios on each side of the target.

[0044] Figure 4 This is a comparison chart of CKWAI-CFAR detection probabilities under clutter edge conditions.

[0045] Figure 5 This is a comparison chart of false alarm probabilities between CKWAI and CFAR under clutter edge conditions. Detailed Implementation

[0046] This invention discloses a CFAR (Cross-Cumulative Array Detection) method and system for ground reconnaissance radar, capable of effectively detecting enemy moving personnel, vehicles, and low-altitude aircraft targets at night and in adverse weather conditions. Specifically, it calculates the coefficient of variation and kurtosis statistics, uses these statistics to obtain a weighting factor, and then iterates the weighting process, using the iteration result as an estimate of the background clutter power. This algorithm enhances the environmental adaptability of the weighting factor and further improves the algorithm's target detection capability in non-uniform environments, providing a feasible new solution for target detection in ground reconnaissance radar.

[0047] like Figure 1 As shown, a ground reconnaissance radar CFAR detection method includes the following steps:

[0048] The specific steps for calculating the coefficient of variation and kurtosis statistic in step 1 are as follows:

[0049] Step 1-1: The coefficient of variation (CV) measures the relative proportion of the standard deviation to the mean of the reference window sample data, providing a dimensionless measure of relative dispersion and effectively reflecting the homogeneity of the sample data. When outliers exist within the reference window, the CV increases significantly, deviating from the theoretical value under the sample distribution. The formula for the CV is:

[0050] ,

[0051] in, This represents the standard deviation of the reference window sample data. This represents the mean of the reference window sample data. For a scale parameter of... The Weibull distribution, whose coefficient of variation (CV) can be defined as:

[0052] ,

[0053] in, Represents the Gamma function;

[0054] Steps 1-2: Kurtosis describes the degree of central tendency of a data distribution. The greater the deviation of the extreme values ​​in a sample from the mean, the greater the difference between its kurtosis and the theoretical kurtosis of the corresponding distribution. The definition of kurtosis K is:

[0055]

[0056] in, X is the fourth-order central distance of the sample; (i) For the reference window sample data, the value of i ranges from 1 to 2N; Var 2 (x) represents the square of the variance of the reference window sample data; E(x) represents the mean of the reference window sample data. For scale parameter is The theoretical value of the kurtosis of the Weibull distribution is:

[0057]

[0058] Step 2: Obtain the weighting factor based on the calculated kurtosis statistic;

[0059] The properties of the coefficient of variation and kurtosis statistic provide theoretical support for determining the background clutter distribution based on sample data.

[0060] To better utilize kurtosis to adjust the weighting factor Here, we introduce the concept of kurtosis factor, which is defined as:

[0061]

[0062] This allows for automatic adjustment of the weighting factors to adapt to clutter variations under different environments, resulting in the following expression for the new weighting factors:

[0063]

[0064] in, Indicates the center of adjustment of the weighting factor. and The adjustment coefficient is used to obtain the adaptive weighting factor. Then, the algorithm can be obtained. The weighted value after the next iteration is:

[0065]

[0066] In step 3, the weighting process is iterated, and the iteration result is used as the background clutter power estimate.

[0067] Step 3-1: After each iteration, first check whether the coefficient of variation and kurtosis of the remaining samples are less than the theoretical values;

[0068] Step 3-2: Check if the current number of remaining samples is less than half of the original number;

[0069] Step 3-3: If any of the above conditions are met, the iteration will no longer follow the original rules. Instead, the remaining samples will be divided into four equal parts, the mean of each part will be calculated, and these means will be weighted and iterated three times to obtain the final background clutter power estimate.

[0070] Based on the same inventive concept, the present invention also provides a ground reconnaissance radar CFAR detection system, comprising:

[0071] The first module calculates the coefficient of variation and kurtosis statistic.

[0072] The second module obtains the weighting factor based on the calculated coefficient of variation and kurtosis statistic.

[0073] The third module iterates the weighting process and uses the iteration results as an estimate of the background clutter power.

[0074] The specific implementation methods of the first to third modules mentioned above are the same as the steps in the aforementioned ground reconnaissance radar CFAR detection method, and will not be repeated here.

[0075] This invention utilizes the coefficient of variation and kurtosis statistics to obtain an adaptive weighting factor and optimizes the iterative process, enabling the algorithm to maintain a high detection probability in weak clutter regions while effectively suppressing the false alarm probability in strong clutter regions, thus significantly improving the robustness of the algorithm in dealing with complex environments.

[0076] The following is a detailed description with reference to the embodiments:

[0077] Example

[0078] The specific conditions for this embodiment are: clutter follows a Weibull distribution, and the scale parameter... Shape parameters The total number of distance elements is 600, the length of each of the left and right protection elements is 2, and the length of each of the left and right reference elements is... The false alarm probability is set to OS-CFAR sample number .

[0079] First, the target is placed at distance cell 300. In the first scenario, there are three strong interferences on one side of the target; these interferences are placed at cells 305, 310, and 315. The resulting CKWAI-CFAR detection probability comparison chart is shown below. Figure 2 As shown in the figure, the CKWAI-CFAR algorithm has a higher detection probability than the other three WAI-CFAR algorithms. This is because the kurtosis of the reference window samples is higher than its theoretical value, thus allowing the weighting factor to be adaptively increased using the kurtosis factor. The value of is used to effectively weaken the influence of strong interference; the second case is that there are three interferences on both sides of the target. The interferences are placed in the 285th, 290th, 295th, 305th, 310th, and 315th distance cells, and the detection probability comparison diagram of CKWAI-CFAR is obtained as follows. Figure 3 As shown in the figure, the CKWAI-CFAR algorithm exhibits good performance in multi-target scenarios.

[0080] Subsequently, the clutter edge was set at the 300th distance cell. The range of 0-300 was considered a weak clutter region with an average power of 22dB, while the range of 301-600 was a strong clutter region with an average power of 42dB. The target was then placed at the 290th distance cell. The resulting comparison of CKWAI-CFAR detection probabilities in a clutter-edge environment is shown in the image below. Figure 4 As shown in the figure, when SCR=12dB, the detection probability of CKWAI-CFAR is 94.7%, which is significantly better than the other comparison algorithms.

[0081] No target is set, and the false alarm probability is set to The remaining settings are the same as before, and each distance unit is performed. The Monte Carlo simulation yielded the false alarm probability for the distance segment from 270 to 330 as follows: Figure 5 As shown in the figure, in the weak clutter region before the 300th distance cell, the CKWAI-CFAR algorithm has good false alarm retention capability and effectively avoids the "target masking effect".

[0082] As can be seen from the above embodiments, the ground reconnaissance radar CFAR detection method of the present invention, through adaptive adjustment of the weighting factor, not only effectively improves the "target masking effect" in the case of multiple targets and weak clutter, and increases the target detection probability, but also enhances the false alarm control capability in strong clutter areas and improves the environmental applicability of the algorithm.

Claims

1. A ground reconnaissance radar CFAR detection method, characterized in that, Includes the following steps: Step 1: Calculate the coefficient of variation and kurtosis statistic; Step 2: Obtain the weighting factor based on the calculated kurtosis statistic; Step 3: Iterate the weighting process and use the iteration result as the background clutter power estimate.

2. The ground reconnaissance radar CFAR detection method according to claim 1, characterized in that, The specific process for calculating the coefficient of variation and kurtosis statistic in step 1 is as follows: Step 1-1: Radar obtains reference window sample data for different range cells by emitting electromagnetic waves and sampling the echo signals. The coefficient of variation measures the variation of the reference window sample data. The relative proportion of the standard deviation to the mean yields a dimensionless measure of relative dispersion. When outliers exist within the reference window, the coefficient of variation (CV) increases, deviating from the theoretical value under the sample distribution. The formula for the coefficient of variation (CV) is: ， in, This represents the standard deviation of the reference window sample data. This represents the mean of the reference window sample data; For the scale parameter is The Weibull distribution has a coefficient of variation (CV) defined as: ， in, Represents the Gamma function; Steps 1-2: Kurtosis describes the degree of centralization of a data distribution. The greater the deviation of the extreme values ​​in the reference window sample data from the mean, the greater the difference between its kurtosis and the theoretical kurtosis under the corresponding distribution. The definition of kurtosis K is: ， in, The fourth-order center distance of the reference window sample data; X (i) For the reference window sample data, the value of i ranges from 1 to 2N; Var 2 (x) represents the square of the variance of the reference window sample data; E(x) represents the mean of the reference window sample data. ; For the scale parameter is The theoretical value of the kurtosis K0 in the Weibull distribution is: 。 3. The ground reconnaissance radar CFAR detection method according to claim 1, characterized in that, The specific process of step 2 is as follows: Introducing a kurtosis factor ΔK to adjust the weighting factor The kurtosis factor ΔK is defined as: ， The expression for obtaining the new weighting factor is: ， in, Indicates the center of adjustment of the weighting factor. and The adjustment coefficient is used to obtain the adaptive weighting factor. After that, proceed After the next iteration, the weighted values ​​are: 。 4. The ground reconnaissance radar CFAR detection method according to claim 1, characterized in that, Step 3 iterates the weighting process, and the specific steps are as follows: After each iteration, check whether the coefficient of variation and kurtosis of the current remaining samples are less than the theoretical values; check whether the number of the current remaining samples is less than half of the original number; if any of these conditions are met, divide the remaining samples into four equal parts, calculate the mean of each part, and perform three weighted iterations on these means to obtain the final background clutter power estimate.

5. A ground reconnaissance radar CFAR detection system, used to implement the ground reconnaissance radar CFAR detection method according to any one of claims 1-4, characterized in that, include: The first module calculates the coefficient of variation and kurtosis statistic. The second module obtains the weighting factor based on the calculated coefficient of variation and kurtosis statistic. The third module iterates the weighting process and uses the iteration results as an estimate of the background clutter power.

6. An electronic 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 program, it implements the steps of the method as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-4.

8. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-4.

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