A low false alarm rate detection method and device suitable for detecting slow and small targets

Abstract

The present invention discloses a low false alarm rate detection method and device suitable for detecting low-speed, slow, and small targets. The method comprises the following steps: collecting trace data containing low-speed, slow, and small targets, selecting a trace set of drone targets from the trace data; fitting the relationship between the actual amplitude and actual distance of each trace in the trace set using the least squares method to obtain a fitting curve; during the fitting process, using the actual amplitude of each trace in the trace set as a dependent variable and the actual distance of each trace in the trace set as an independent variable; calculating the residual of each trace in the trace set based on the fitting curve and the actual curve; the distribution of all residuals satisfies a normal distribution; calculating the coefficient of a detection threshold curve based on the normal distribution characteristics of all residuals; designing a detection threshold curve based on the coefficients of the fitting curve and the detection threshold curve; and performing constant false alarm detection using the detection threshold curve. The present invention can more accurately distinguish target points from noise points in low-speed, slow, and small targets, thereby reducing the false alarm probability of the radar.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar signal processing, and in particular relates to a low false alarm rate detection method and device suitable for detecting slow and small targets. Background Art

[0002] As an emerging low-altitude aircraft, drones are often difficult to detect with traditional radar due to their small size, high stealth, and high agility. Furthermore, the low-altitude environment is highly noisy and the application scenarios are complex. Therefore, when using radar to detect and track such targets, some noise is often misidentified as the target, affecting the radar system's accuracy, increasing its workload, and thus affecting its real-time performance.

[0003] Radar signal detection processes the mixed signal output by the receiver, which is often composed of target signals, noise, and other interference. It accurately identifies the target signal with a certain detection probability while keeping the probability of misjudging noise and other interfering signals low. The magnitude of the detection probability and false alarm probability depends on the amplitude distribution of the target signal and the characteristics of the noise and interference signals. Traditional constant false alarm detection methods often have difficulty effectively distinguishing between small, slow, and low-lying targets and noise artifacts, making it difficult to remove noise artifacts, which can affect subsequent radar data processing and target tracking.

[0004] The definition of false alarm probability is the probability that the radar believes there is a target in the received signal when there is no target in the signal. Constant false alarm detection technology adjusts the detection threshold to ensure that the false alarm probability remains constant under different signal environments. However, if the detection threshold is set too low, noise may be mistaken for a target; if it is set too high, the real target may be missed. The most basic constant false alarm processing is to estimate the noise and interference in a signal segment that needs to be detected, and set the threshold based on the estimated value to determine whether there is a target in the signal. The common constant false alarm detection methods are:

[0005] (1) Cell Averaging CFAR (CA-CFAR)

[0006] CA-CFAR uses multiple noise reference cells on either side of the target cell to calculate the mean background noise value and determine the detection threshold. Specifically, CA-CFAR first selects a guard cell interval on either side of the target cell to ensure that the noise is not directly affected by the target cell. It then selects several reference cells outside the guard cell and averages the signal amplitudes of these reference cells to generate an estimate of the background noise. The estimated noise level is then multiplied by a constant factor to set the target detection threshold. If the target cell's signal amplitude exceeds this threshold, the target is detected.

[0007] (2) Greatest Of CFAR (GO-CFAR)

[0008] GO-CFAR sets the detection threshold by selecting the reference unit with the larger noise level from multiple reference groups. In the specific steps, the two sides of the target unit will be divided into two sets of reference units, and the signal strength mean in each set will be calculated respectively. Subsequently, when calculating the threshold, GO-CFAR selects the maximum value of the two sets of signal strength means as the noise background estimate to set the detection threshold. This strategy ensures that when detecting isolated targets, the confusion from neighboring strong interfering targets can be effectively suppressed. Although GO-CFAR has good target isolation capabilities in environments with uneven backgrounds, it may have the risk of missing weak targets in dense target groups.

[0009] (3) Smallest Of CFAR (SO-CFAR)

[0010] SO-CFAR calculates the detection threshold by selecting the noise reference group with the lowest signal strength. During operation, two groups of reference cells are formed on either side of the target cell, and the signal strength mean is calculated for each group. The minimum of these two means is then selected as the background noise estimate. Compared to GO-CFAR, SO-CFAR is more suitable for use in multi-target environments because selecting the minimum value as the threshold effectively reduces the probability of false alarms due to nearby targets. However, in the case of strong background clutter or uneven noise, this choice can lead to erroneous background estimation, which in turn affects overall detection performance and needs to be adjusted according to actual needs.

[0011] (4) Ordered Statistics CFAR (OS-CFAR)

[0012] OS-CFAR is a sorting-based method designed to improve adaptability to environmental noise variations and the ability to suppress isolated noise points. Its principle begins by sorting the signal amplitudes of reference cells outside the target cell area from small to large. A fixed position in the sorting, such as the median or the top signal value, is selected as the noise background estimate. This sorting operation makes the noise estimate highly stable, effectively filtering out isolated strong noise points or outliers without affecting the final detection threshold. OS-CFAR is often used in environments with uneven background noise and is computationally intensive.

[0013] However, since environmental noise and clutter affect the propagation of radar signals, resulting in a decrease in signal quality, the above-mentioned existing constant false alarm detection method can remove some noise points, but it still cannot effectively remove noise points when facing low, slow and small targets in complex environments. In addition, it usually sets an absolute threshold. Such an absolute threshold cannot remove most noise points when detecting low, slow and small targets, resulting in a large number of noise points being reported to the radar system, forcing the radar system to process a large number of points, consuming a lot of computing resources and resulting in insufficient real-time performance. Summary of the Invention

[0014] In order to solve the above problems existing in the prior art, the present invention provides a low false alarm rate detection method and device suitable for detecting slow and small targets. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0015] In a first aspect, an embodiment of the present invention provides a low false alarm rate detection method suitable for detecting slow and small targets, including:

[0016] Collecting point trace data containing low, slow, and small targets, and selecting a point trace set of the UAV target from all the point trace data; each point trace in the point trace set includes an actual amplitude and an actual distance;

[0017] Using the least squares method, fitting the relationship between the actual amplitude and the actual distance to obtain a fitting curve; wherein, during the fitting process, the actual amplitude of each point trace in the point trace set is used as a dependent variable, and the actual distance of each point trace in the point trace set is used as an independent variable to construct the fitting curve;

[0018] Calculating the residual of each point in the point set according to the fitting curve and the actual curve corresponding to the point set; wherein the distribution of all residuals satisfies the normal distribution;

[0019] Calculate the coefficients of the detection threshold curve based on the normal distribution characteristics of all residuals;

[0020] Designing a detection threshold curve according to the fitting curve and the coefficients of the detection threshold curve;

[0021] The detection threshold curve is used to perform constant false alarm detection.

[0022] In one embodiment of the present invention, the coefficients of the detection threshold curve are calculated according to the normal distribution characteristics of all residuals, including:

[0023] Calculating the fitting amplitude of each point trace in the point trace set according to the fitting curve;

[0024] Calculating the residual of each point trace of the point trace set according to the fitted amplitude and the actual amplitude;

[0025] Calculating an average residual based on the residual of each point in the point set;

[0026] Calculating the standard deviation of the residuals based on the residual of each point in the point set and the average residual;

[0027] Query the probability distribution table of the standard normal distribution according to the preset expected threshold value to obtain the width of the confidence interval;

[0028] The coefficient of the detection threshold curve is calculated according to the standard deviation of the residual and the width of the confidence interval.

[0029] In one embodiment of the present invention, the formula for calculating the standard deviation of the residual is:

[0030]

[0031] Where σ represents the standard deviation of the residual, n represents the number of traces in the trace set, and r i Represents the residual of the i-th point in the point set, r i =y i -f(x i ), y i Indicates the actual amplitude of the i-th point in the point set, x i Indicates the actual distance of the i-th point in the point set, f(x i ) represents the actual distance x calculated based on the fitting curve i The fitting amplitude at Represents the mean of the residuals of all points in the trace set.

[0032] In one embodiment of the present invention, the formula for calculating the coefficient of the detection threshold curve is:

[0033]

[0034] Among them, A represents the coefficient of the detection threshold curve, n represents the number of points in the point set, and x i Indicates the actual distance of the i-th point in the point set, f(x i) represents the actual distance x calculated based on the fitting curve i The fitted amplitude at , σ represents the standard deviation of the residuals, and z represents the width of the confidence interval.

[0035] In one embodiment of the present invention, when the preset expected threshold is 98%, the width of the confidence interval is -2.05.

[0036] In one embodiment of the present invention, the designed detection threshold curve formula is expressed as:

[0037] f′(x)=A·f(x);

[0038] Where f′(x) represents the detection threshold curve, A represents the coefficient of the detection threshold curve, f(x) represents the fitting curve, x represents the set of actual distances of the points in the point set, and x={x1,…,x i ,…,x n}, x i Indicates the actual distance of the i-th point in the point set.

[0039] In one embodiment of the present invention, using the detection threshold curve to perform constant false alarm detection includes:

[0040] For each point in the point set, the execution steps include: using the detection threshold curve to calculate the estimated amplitude of the point, judging whether the actual amplitude of the point is greater than the estimated amplitude, if so, judging the point as a target point, if not, judging the point as a noise point, and removing the point from the point set.

[0041] In a second aspect, an embodiment of the present invention provides a low false alarm rate detection device suitable for detecting slow and small targets, the device comprising:

[0042] The acquisition module is used to collect point trace data containing low, slow and small targets, and select a point trace set of the UAV target from all the point trace data; each point trace in the point trace set includes an actual amplitude and an actual distance;

[0043] a fitting module, configured to fit the relationship between the actual amplitude and the actual distance using a least squares method to obtain a fitting curve; wherein, during the fitting process, the actual amplitude of each point trace in the point trace set is used as a dependent variable, and the actual distance of each point trace in the point trace set is used as an independent variable to construct the fitting curve;

[0044] A first calculation module is used to calculate the residual of each point in the point trace set according to the fitting curve and the actual curve corresponding to the point trace set; wherein the distribution of all residuals satisfies the normal distribution;

[0045] A second calculation module is used to calculate the coefficient of the detection threshold curve according to the normal distribution characteristics of all residuals;

[0046] A design module, configured to design a detection threshold curve according to the fitting curve and coefficients of the detection threshold curve;

[0047] The detection module is configured to perform constant false alarm detection using the detection threshold curve.

[0048] Beneficial effects of the present invention:

[0049] The low false alarm rate detection method proposed by the present invention is suitable for detecting low, slow and small targets. Compared with the existing constant false alarm rate detection method, the low false alarm rate detection method is simple but can detect the correct target while reducing the probability of noise points passing the detection, thereby reducing the false alarm probability of the radar. Specifically, by statistically analyzing the measured point trace data of a large number of unmanned aerial vehicle targets, the relationship between their distance and amplitude is fitted using the least squares method; in constant false alarm rate detection, the absolute threshold commonly used, which is calculated based on the noise mean, is replaced by a detection threshold curve designed based on the distance and amplitude characteristics of the unmanned aerial vehicle target. The design concept of the detection threshold curve is to utilize the relationship between the residual of the actual value and the fitted value of the unmanned aerial vehicle target point and the normal distribution, ensure a preset expected threshold, such as a target detection probability of more than 98%, obtain the coefficient of the detection threshold curve, design the target detection curve based on the fitting curve and the coefficient of the detection threshold curve, and use the target detection curve to perform constant false alarm rate detection. In summary, the detection method proposed in the present invention can more accurately distinguish between low-altitude, slow-moving, small target points and noise, reduce the false alarm probability of the radar, and have a good suppressive effect on radar clutter; reduce the impact of environmental clutter on radar detection of low-altitude, slow-moving, small targets, reduce the load of the radar system, and enhance the real-time performance and accuracy of the system. It has great application prospects for the detection and tracking of low-altitude, slow-moving, small targets such as drones in low-altitude environments, such as urban monitoring and airspace management.

[0050] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of a low false alarm rate detection method for detecting slow, small targets, provided by an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram showing all the points in the point trace set of the drone target provided by an embodiment of the present invention in terms of azimuth and distance;

[0053] Figure 3 This is a schematic diagram showing all the traces in the trace set provided by an embodiment of the present invention in terms of amplitude and distance;

[0054] Figure 4is a schematic diagram of a fitting curve provided by an embodiment of the present invention;

[0055] Figure 5 Schematic diagram of the distribution of all residuals corresponding to the point trace set provided by an embodiment of the present invention;

[0056] Figure 6 is a schematic diagram of a detection threshold curve provided by an embodiment of the present invention;

[0057] Figure 7 (a)~ Figure 7 (b) is a comparison diagram of the detection method using an absolute threshold and the detection method proposed by the present invention in a vertical upward flight scenario of a UAV provided by an embodiment of the present invention;

[0058] Figure 8 (a)~ Figure 8 (b) is a comparison chart of the detection method using an absolute threshold and the detection method proposed by the present invention in the S-shaped direct flight scenario of a drone provided by an embodiment of the present invention;

[0059] Figure 9 (a)~ Figure 9 (b) is a comparative diagram of the track display on a radar terminal of the same UAV in the same environment at different times and in different flight attitudes, using the absolute threshold detection method and the detection method proposed by the present invention, provided by an embodiment of the present invention;

[0060] Figure 10 The present invention provides a low false alarm rate detection device for detecting slow, small targets. DETAILED DESCRIPTION

[0061] The present invention will be further described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.

[0062] First, see Figure 1 The embodiment of the present invention provides a low false alarm rate detection method suitable for detecting slow and small targets, which specifically includes the following steps:

[0063] S10, collecting point trace data containing low, slow and small targets, and selecting a point trace set of the UAV target from all the point trace data; each point trace in the point trace set includes an actual amplitude and an actual distance.

[0064] Typically, after a radar transmission signal encounters a target in space, the reflected echo signal is received by the receiving antenna. Multiple continuous echo signals are usually combined into a frame of data for signal processing. For example, when using radar to detect a drone target during flight, radar echo signals are collected by limiting the drone to a certain distance and speed range. The collected radar echo signals are then processed, including pulse compression, moving target detection, constant false alarm detection, angle measurement, etc., to obtain point data containing low, slow, and small targets. Here, low, slow, and small targets are low-altitude, slow, and small targets, usually referring to small aircraft with a flight altitude below 1,000 meters, a flight speed below 200 kilometers per hour (56 meters per second), and a radar cross-section (RCS) of less than 2 square meters.

[0065] Since the UAV's motion is continuous and the distance and speed information of the UAV target are available during the point trace data collection, the point trace set of the UAV target can be obtained from the point trace data of the radar echo signal after signal processing. Figure 2 As shown, the information of the drone's target point can be obtained based on the continuity of the drone's movement.

[0066] S20. Use the least squares method to fit the relationship between the actual amplitude and the actual distance to obtain a fitting curve; wherein, during the fitting process, the actual amplitude of each point trace in the point trace set is used as a dependent variable, and the actual distance of each point trace in the point trace set is used as an independent variable to construct the fitting curve.

[0067] After obtaining the target's point set from S10, the least squares method is used to fit the relationship between the target's signal amplitude and distance. The least squares method is a mathematical optimization technique commonly used for data fitting. It finds the optimal function by minimizing the sum of the squared errors between the actual points and the fitted curve.

[0068] During the fitting process, the actual amplitude of each point in the point trace set of the embodiment of the present invention is used as the dependent variable, and the actual distance of each point in the point trace set is used as the independent variable to obtain a set of points (x1, y1), (x2, y2), ..., (x n ,y n ), {x1,…,x i ,…,x n} constitutes the set of actual distances of the points in the point set, {y1,…,y i ,…,y n} constitutes the set of actual amplitudes of the traces in the trace set, such as Figure 3 As shown. Assume that the function expression of the fitting curve is:

[0069] f(x)=p1x n +p2xn-1 +p3x n-2 +…+p n x+p n+1 (1);

[0070] Among them, x represents the set of actual distances of the points in the point set, x={x1,…,x i ,…,x n}, f(x) represents the fitting curve. According to the fitting curve f(x), the fitting amplitude at each actual distance in x can be calculated, {p1, p2, ..., p n+1} represents the polynomial coefficient, and n represents the number of points in the point set.

[0071] For any point in the point set, the residual calculation between it and the fitting curve is expressed as:

[0072] r i =y i -f(x i ) (2);

[0073] Among them, r i Represents the residual of the i-th point in the point set, x i Indicates the actual distance of the i-th point in the point set, y i Indicates the actual amplitude of the i-th point in the point set, f(x i ) represents the actual distance x calculated based on the fitting curve i The fitting amplitude at .

[0074] The goal of the least squares method is to minimize the sum of squares S of all residuals:

[0075]

[0076] Next, solve the fitting curve f(x). Here, we take f(x) as a cubic polynomial as an example: construct the process matrix X and process matrix Y based on the number n of traces in the trace set and the actual amplitude and actual distance of each trace in the trace set:

[0077]

[0078]

[0079] To minimize the sum of squares S of all residuals, the least squares solution can be obtained by solving the following normal equation:

[0080] X T Xp=X T Y (6);

[0081] Where p is the vector of polynomial coefficients, p = [p1 p2 p3 p4] T , [] T Represents the transposition operation; by solving formula (6), the polynomial coefficients can be obtained, and then the fitting curve represented by formula (1) can be determined as follows: Figure 4 shown.

[0082] S30. Calculate the residual of each point in the point trace set according to the fitted curve and the actual curve corresponding to the point trace set; wherein the distribution of all residuals satisfies the normal distribution.

[0083] In the process of determining the fitting curve in S20, the residual of each point in the point trace set can also be calculated based on the fitting curve and the actual curve corresponding to the point trace set. The specific residual calculation method is shown in formula (2) and will not be repeated here.

[0084] The residual is the deviation between the actual curve and the fitted curve. These target points are obtained by processing the radar echo signal of the drone by the same radar. There are certain errors in them. These errors may be caused by multiple independent noise sources such as measurement error, system error and environmental noise. According to the central limit theorem, as the number of samples increases, the distribution of the residual will approach the normal distribution. Figure 5 Therefore, the embodiment of the present invention considers that the distribution of the residuals approximately satisfies the normal distribution.

[0085] S40. Calculate the coefficients of the detection threshold curve according to the normal distribution characteristics of all residuals.

[0086] In the embodiment of the present invention, the coefficients of the detection threshold curve are calculated based on the normal distribution characteristics of all residuals, including:

[0087] The fitting amplitude of each point in the point trace set is calculated according to the fitting curve; the residual of each point in the point trace set is calculated according to the fitting amplitude and the actual amplitude; the average residual is calculated according to the residual of each point in the point trace set; the standard deviation of the residual is calculated according to the residual of each point in the point trace set and the average residual; the probability distribution table of the standard normal distribution is queried according to the preset expected threshold to obtain the width of the confidence interval; the coefficient of the detection threshold curve is calculated according to the standard deviation of the residual and the width of the confidence interval.

[0088] The formula for calculating the standard deviation of the residual in the embodiment of the present invention is:

[0089]

[0090] Where σ represents the standard deviation of the residual, n represents the number of traces in the trace set, and r i Represents the residual of the i-th point in the point set, r i =y i -f(x i), y i Indicates the actual amplitude of the i-th point in the point set, x i Indicates the actual distance of the i-th point in the point set, f(x i ) represents the actual distance x calculated based on the fitting curve i The fitting amplitude at Represents the mean of the residuals of all points in the trace set.

[0091] The formula for calculating the coefficient of the detection threshold curve in the embodiment of the present invention is:

[0092]

[0093] Among them, A represents the coefficient of the detection threshold curve, n represents the number of points in the point set, and x i Indicates the actual distance of the i-th point in the point set, f(x i ) represents the actual distance x calculated based on the fitting curve i The fitting amplitude at , σ represents the standard deviation of the residual, and z represents the width of the confidence interval. The width of the confidence interval z is determined by the actual detection expectation. For example, when the preset expected threshold is 98%, that is, when we expect 98% of the target points to pass the detection threshold curve, we query the probability distribution table of the standard normal distribution and find that the width of the confidence interval is -2.05.

[0094] S50 , designing a detection threshold curve according to the fitting curve and the coefficients of the detection threshold curve.

[0095] The embodiment of the present invention designs the detection threshold curve for constant false alarm detection based on the fitting curve determined in S20 and the coefficient of the detection threshold curve calculated in S40. Figure 6 As shown, the formula is expressed as:

[0096] f′(x)=A·f(x) (9);

[0097] Where f′(x) represents the detection threshold curve, A represents the coefficient of the detection threshold curve, f(x) represents the fitting curve, x represents the set of actual distances of the points in the point set, and x={x1,…,x i ,…,x n}, x i Indicates the actual distance of the i-th point in the point set.

[0098] S60: Perform constant false alarm detection using the detection threshold curve.

[0099] In an embodiment of the present invention, a detection threshold curve is used to perform constant false alarm detection, including: for each point in a point set, executing the steps including: using the detection threshold curve to calculate the estimated amplitude of the point, determining whether the actual amplitude of the point is greater than the estimated amplitude, and if so, determining that the point is a target point; if not, determining that the point is a noise point and removing the point from the point set. More specifically:

[0100] The embodiment of the present invention no longer uses an absolute threshold to filter the target points, but uses a detection threshold curve f′(x) designed according to the actual scene to filter the points from the point set, such as Figure 5 As shown. Determine whether a point is a target point:

[0101] The actual distance and actual amplitude of the point trace are taken out to obtain (x i ,y i ), x i is the actual distance, y i is the actual amplitude; substitute the actual distance of the point into the detection threshold curve to obtain f′(x i );Judge the actual amplitude y i and f′(x i ) size, if y i Greater than f′(x i ) is considered as the target point, otherwise it is considered as a noise point and removed from the point set. Repeat the above process to complete the detection of all points in the point set.

[0102] In order to verify the effectiveness of the low false alarm rate detection method for detecting slow and small targets provided by the embodiment of the present invention, a 24G linear frequency modulation continuous wave radar is used to collect data on the drone. The measured data are analyzed, respectively, for the drone's vertical upward flight and S-shaped straight flight. The target points are screened using the detection method with an absolute threshold of noise amplitude +12dB and the detection method proposed by the present invention. The comparison diagram of the drone's vertical upward flight scene is shown in the figure below. Figure 7 (a)~ Figure 7 As shown in (b), the comparison of the UAV S-shaped direct flight scene is as follows Figure 8 (a)~ Figure 8 (b) from Figure 7 (a)~ Figure 7 (b) Figure 8 (a)~ Figure 8 (b) It can be seen that compared with the absolute threshold detection method, the detection method proposed in the present invention has a better effect in suppressing clutter.

[0103] Afterwards, the radar system is verified as a whole. The radar is used to detect the drone in real time, and the radar terminal display interface shows the following: For the same drone in the same environment at different times and different flight postures, the track display comparison diagram after using the absolute threshold detection method and the detection method proposed by the present invention is as follows: Figure 9 (a)~ Figure 9 As shown in (b), Figure 9 (a)~ Figure 9 (b) It can be seen that a large number of miscellaneous points are eliminated after using the detection method proposed by the present invention.

[0104] In summary, the low false alarm rate detection method for detecting low, slow, and small targets proposed in an embodiment of the present invention is simple but can detect the correct target while reducing the probability of noise passing the detection, thereby reducing the false alarm probability of the radar, compared to the existing constant false alarm rate detection method. Specifically, by statistically analyzing a large number of measured point trace data of drone targets, the relationship between their distance and amplitude is fitted using the least squares method; in constant false alarm rate detection, the absolute threshold commonly used, which is calculated based on the noise mean, is replaced by a detection threshold curve designed based on the distance and amplitude characteristics of the drone target. The design idea of ​​the detection threshold curve is to utilize the relationship between the residual of the actual value and the fitted value of the drone target point and the normal distribution, to ensure a preset expected threshold, such as a target detection probability of more than 98%, to obtain the coefficient of the detection threshold curve, and to design a target detection curve by using the fitting curve and the coefficient of the detection threshold curve, so as to use the target detection curve for constant false alarm rate detection. In summary, the detection method proposed in the embodiment of the present invention can more accurately distinguish between low-altitude, slow-moving, small target points and noise, reduce the false alarm probability of the radar, and effectively suppress radar clutter. It also reduces the impact of environmental clutter on radar detection of low-altitude, slow-moving, small targets, reduces the load of the radar system, and enhances the real-time performance and accuracy of the system. It has great application prospects for the detection and tracking of low-altitude, slow-moving, small targets such as drones in low-altitude environments, such as urban monitoring and airspace management.

[0105] Second, see Figure 10 The embodiment of the present invention provides a low false alarm rate detection device suitable for detecting slow and small targets, the device comprising:

[0106] The acquisition module is used to collect the trace data of low, slow and small targets and select the trace set of the UAV target from all the trace data; each trace in the trace set includes the actual amplitude and actual distance;

[0107] A fitting module is used to fit the relationship between the actual amplitude and the actual distance using the least squares method to obtain a fitting curve; wherein, during the fitting process, the actual amplitude of each point in the point trace set is used as the dependent variable, and the actual distance of each point in the point trace set is used as the independent variable to construct the fitting curve;

[0108] A first calculation module is used to calculate the residual of each point in the point trace set according to the fitted curve and the actual curve corresponding to the point trace set; wherein the distribution of all residuals satisfies the normal distribution;

[0109] A second calculation module is used to calculate the coefficient of the detection threshold curve according to the normal distribution characteristics of all residuals;

[0110] A design module, used for designing a detection threshold curve according to the fitting curve and the coefficients of the detection threshold curve;

[0111] The detection module is used to perform constant false alarm detection using a detection threshold curve.

[0112] As for the device embodiment of the second aspect, since it is basically similar to the method embodiment of the first aspect, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment of the first aspect.

[0113] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0114] Although the present invention is described herein in conjunction with various embodiments, those skilled in the art may understand and implement other variations of the disclosed embodiments by reviewing the specification and accompanying drawings in the process of implementing the claimed invention. In the specification, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple components or steps. The fact that certain measures are described in different embodiments does not mean that these measures cannot be combined to produce good results.

[0115] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A low false alarm rate detection method suitable for detecting slow and small targets, characterized in that: The method comprises: Collecting point trace data containing low, slow, and small targets, and selecting a point trace set of the UAV target from all the point trace data; each point trace in the point trace set includes an actual amplitude and an actual distance; Using the least squares method, fitting the relationship between the actual amplitude and the actual distance to obtain a fitting curve; wherein, during the fitting process, the actual amplitude of each point trace in the point trace set is used as a dependent variable, and the actual distance of each point trace in the point trace set is used as an independent variable to construct the fitting curve; Calculating the residual of each point in the point set according to the fitting curve and the actual curve corresponding to the point set; wherein the distribution of all residuals satisfies the normal distribution; Calculate the coefficients of the detection threshold curve based on the normal distribution characteristics of all residuals; Designing a detection threshold curve according to the fitting curve and the coefficients of the detection threshold curve; The detection threshold curve is used to perform constant false alarm detection.

2. The low false alarm rate detection method for detecting slow and small targets according to claim 1, characterized in that: The coefficients of the detection threshold curve are calculated based on the normal distribution characteristics of all residuals, including: Calculating the fitting amplitude of each point trace in the point trace set according to the fitting curve; Calculating the residual of each point trace of the point trace set according to the fitted amplitude and the actual amplitude; Calculating an average residual based on the residual of each point in the point set; Calculating the standard deviation of the residuals based on the residual of each point in the point set and the average residual; Query the probability distribution table of the standard normal distribution according to the preset expected threshold value to obtain the width of the confidence interval; The coefficient of the detection threshold curve is calculated according to the standard deviation of the residual and the width of the confidence interval.

3. The low false alarm rate detection method suitable for detecting slow and small targets according to claim 2, characterized in that: The formula for calculating the standard deviation of the residuals is: Where σ represents the standard deviation of the residual, n represents the number of traces in the trace set, and r i Represents the residual of the i-th point in the point set, r i =y i -f(x i ), y i Indicates the actual amplitude of the i-th point in the point set, x i Indicates the actual distance of the i-th point in the point set, f(x i ) represents the actual distance x calculated based on the fitting curve i The fitting amplitude at Represents the mean of the residuals of all points in the trace set.

4. The low false alarm rate detection method suitable for detecting slow and small targets according to claim 2, characterized in that: The formula for calculating the coefficient of the detection threshold curve is: Among them, A represents the coefficient of the detection threshold curve, n represents the number of points in the point set, and x i Indicates the actual distance of the i-th point in the point set, f(x i ) represents the actual distance x calculated based on the fitting curve i The fitted amplitude at , σ represents the standard deviation of the residuals, and z represents the width of the confidence interval.

5. The low false alarm rate detection method suitable for detecting slow and small targets according to claim 2, characterized in that: When the preset expected threshold is 98%, the width of the confidence interval is -2.

05.

6. The low false alarm rate detection method suitable for detecting slow and small targets according to claim 1, characterized in that: The designed detection threshold curve formula is expressed as: f′(x)=A·f(x); Where f′(x) represents the detection threshold curve, A represents the coefficient of the detection threshold curve, f(x) represents the fitting curve, x represents the set of actual distances of the points in the point set, and x={x1,…,x i ,…,x n }, x i Indicates the actual distance of the i-th point in the point set.

7. The low false alarm rate detection method suitable for detecting slow and small targets according to claim 1, characterized in that: Performing constant false alarm detection using the detection threshold curve includes: For each point in the point set, the execution steps include: using the detection threshold curve to calculate the estimated amplitude of the point, judging whether the actual amplitude of the point is greater than the estimated amplitude, if so, judging the point as a target point, if not, judging the point as a noise point, and removing the point from the point set.

8. A low false alarm rate detection device suitable for detecting slow and small targets, characterized in that: The device comprises: The acquisition module is used to collect point trace data containing low, slow and small targets, and select a point trace set of the UAV target from all the point trace data; each point trace in the point trace set includes an actual amplitude and an actual distance; a fitting module, configured to fit the relationship between the actual amplitude and the actual distance using a least squares method to obtain a fitting curve; wherein, during the fitting process, the actual amplitude of each point trace in the point trace set is used as a dependent variable, and the actual distance of each point trace in the point trace set is used as an independent variable to construct the fitting curve; A first calculation module is used to calculate the residual of each point in the point trace set according to the fitting curve and the actual curve corresponding to the point trace set; wherein the distribution of all residuals satisfies the normal distribution; A second calculation module is used to calculate the coefficient of the detection threshold curve according to the normal distribution characteristics of all residuals; A design module, configured to design a detection threshold curve according to the fitting curve and coefficients of the detection threshold curve; The detection module is configured to perform constant false alarm detection using the detection threshold curve.

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