A Fast Target Detection Method and System Based on Background Entropy

Abstract

This invention discloses a method and system for rapid detection of dense targets based on background entropy, comprising: calculating the reference background entropy of the current environment when the radar is powered on and operating normally and there is no target, and determining the background environment decision threshold for group targets using the current value; after the radar is operating normally, receiving radar echo data, generating a range sequence, and calculating the background entropy of each range cell in the sequence, comparing it with the background threshold value, and if it is less than the background threshold value, it is a group target background environment; if it is not detected in the group target background environment, then conventional cell average constant false alarm detection is used; if it is detected in the group target background environment, then deletion constant false alarm detection is used; pipelined detection is used to complete the real-time detection of targets in the entire range cell of the radar; this reduces the complexity of engineering implementation and reduces false alarms and missed alarms in conventional target detection, effectively ensuring rapid detection of dense targets in complex background environments.

Description

Technical Field

[0001] This invention belongs to the field of phased array radar signal processing technology, and particularly relates to a method and system for rapid detection of dense targets based on background entropy. Background Technology

[0002] Phased array radar signal processing mainly involves target detection, tracking, and imaging. Typically, the choice of target detection threshold significantly impacts detection performance. If the threshold is too high, the false alarm probability decreases, but a large number of missed alarms may occur. Conversely, if the threshold is set too high, the detection probability increases, resulting in a large number of false alarms.

[0003] In modern radar signal processing, various constant false alarm rate (CFAR) methods are used to improve radar detection performance, ensuring that radar signal detection has CFAR characteristics, so that the radar maintains a high target detection capability while having a lower false alarm rate.

[0004] Currently used constant false alarm rate (CFAR) detection methods, such as cell averaging, selecting a large cell averaging value, and selecting a small cell averaging value, perform well in uniform background or single-target environments, but suffer significant detection loss in multi-target environments. Ordered statistical CFAR, based on an ordered statistical ranking of selected reference cells, chooses the most suitable reference cell as the decision threshold. This method improves detection capability in multi-target environments, but inappropriate reference cell selection can lead to false or missed alarms. Furthermore, the ranking process is time-consuming and difficult to implement in engineering.

[0005] Therefore, achieving rapid detection of targets requires both optimal detection performance and feasibility in engineering implementation, which is currently a technical challenge in this field. Summary of the Invention

[0006] To address the aforementioned shortcomings of existing technologies, this invention provides a method and system for rapid detection of dense targets based on background entropy. By calculating background entropy to determine the current background environment, different constant false alarm rate (CFAR) detection methods are adaptively selected, and the detection method for dense targets is further optimized to improve detection performance when multiple targets appear in the background environment, while reducing the complexity of engineering implementation.

[0007] This invention is achieved through the following technical solution:

[0008] A fast method for detecting dense targets based on background entropy includes the following steps:

[0009] Step S1: When the radar is powered on and working normally and there is no target, acquire the echo data of the current single pulse, calculate the reference background entropy E of the current environment, and determine the group target background environment decision threshold T through the current value;

[0010] Step S2: After the radar is working normally, receive radar echo data, generate a range sequence X, and calculate the background entropy E0 of each range unit in sequence X. Compare it with the background threshold T. If it is less than the background threshold T, it is the background environment of the group target.

[0011] Step S3: If the detection is not performed in the background environment of the target group, then the conventional unit average constant false alarm rate detection is adopted. The Nth data of sequence X is detected, and the average value u of the data from NP-1 to N-2 and the data from N+2 to N+1+P is obtained. In order to eliminate the interference of neighboring targets, the data on both sides of the detection value, namely the N-1 and N+1 numbers, are used as protection units and are not included in the cumulative sum.

[0012] Step S4: If the detection is performed in the background environment of the target group, then the constant false alarm rate (CFAR) detection is removed. The Nth data in sequence X is detected, and the data from NP-1 to N-2 and from N+2 to N+1+P are sorted. The first r largest sample data are removed, and the average value u of the remaining radar data is obtained. To eliminate interference from nearby targets, the data on both sides of the detection value, namely the N-1 and N+1 numbers, are used as protection units and are not included in the summation.

[0013] Step S5: Output the detection threshold s by multiplying the unit average value u and the threshold M;

[0014] Step S6: Detect the value x of the sequence X detection unit. N Compare with the threshold s, if the detected value x N If the value is greater than the threshold s, then a target exists; otherwise, no target exists.

[0015] Step S7: Repeat steps S3 to S6 to complete the real-time detection of targets in the entire range cell of the radar using a pipelined detection method;

[0016] Where P is the number of reference cells, and r is the larger value after sorting the current echo data.

[0017] Preferably, the method for calculating the reference background entropy E in step S1 includes:

[0018] Step S11: Acquire background echo data without a target, and take 2P data points [x1, x2, ..., x 2P ], thus obtaining its amplitude distribution

[0019]

[0020] Step S12: The reference background entropy is calculated as follows:

[0021]

[0022] Preferably, in step S1, the decision threshold T for the group target background environment is determined by the reference background entropy E, and is usually taken as a threshold value that is less than 1.5 dB of the reference background entropy.

[0023] Preferably, the method for calculating the background entropy E0 of each distance unit in sequence X in step S2 is as follows:

[0024] Step S21: Acquire the current radar echo data, and take 2P data points before and after the detection unit [x] N-P-1 ,…,x N-2 ,x N+2 ,…x N+1+P ], thus obtaining its amplitude distribution

[0025]

[0026] In the above formula, l = N-P+1,…,N-2,N+2,…,N+1+P, where l is the distance value of the P reference units before and after the detection unit excluding the protection unit;

[0027] Step S22: The background entropy E0 of the current detection range cell is:

[0028]

[0029] Preferably, in step S5, the threshold M is determined by the false alarm probability P. fa The reference unit P is determined, and the false alarm probability threshold M is calculated using the following formula: M = 2P(P fa -1 / (2*P) -1).

[0030] This invention provides a fast dense target detection system based on background entropy, comprising:

[0031] The background entropy calculation module is used to acquire the echo data of the current single pulse when the radar is powered on and operating normally without a target, calculate the reference background entropy E of the current environment, and determine the group target background environment decision threshold T based on the current value.

[0032] The target group background environment determination module is used to receive radar echo data after the radar is working normally, generate a range sequence X, calculate the background entropy E0 of each range unit of sequence X, compare it with the background threshold value T, and if it is less than the background threshold value, it is the target group background environment.

[0033] The first detection module is used to detect data in the background environment of the target group if it is not the target group. In this case, it adopts the conventional unit average constant false alarm rate detection, detects the Nth data of sequence X, and averages the data from NP-1 to N-2 and the data from N+2 to N+1+P to obtain the unit average value u. In order to eliminate the interference of neighboring targets, the data on both sides of the detection value, namely the N-1 and N+1 numbers, are used as protection units and are not included in the cumulative sum.

[0034] The second detection module is used to detect the Nth data in sequence X if the target group is in the background environment. If so, constant false alarm detection is used. The Nth data in sequence X is detected, and the N-1 to N-2 data and the N+2 to N+1+P data are sorted. The first r larger sample data are removed, and the average value u of the remaining radar data is obtained. In order to eliminate the interference of nearby targets, the data on both sides of the detection value, namely the N-1 and N+1 numbers, are used as protection units and are not included in the summation.

[0035] The threshold calculation module is used to output the detection threshold s by multiplying the unit average value u and the threshold M;

[0036] The target determination module is used to determine the detection value x of the sequence X detection unit. N Compare with the threshold s, if the detected value x N If the value is greater than the threshold s, then a target exists; otherwise, no target exists.

[0037] The real-time detection module is used to repeatedly execute the first detection module to the target judgment module, and is used to complete the real-time detection of targets in the entire range unit of the radar using pipelined detection.

[0038] Where P is the number of reference cells, and r is the larger value after sorting the current echo data.

[0039] Preferably, the method for calculating the reference background entropy E in the background entropy calculation module includes:

[0040] The first amplitude distribution calculation unit is used to acquire background echo data in a targetless environment, taking 2P data points [x1, x2, ..., x]. 2P ], thus obtaining its amplitude distribution

[0041]

[0042] The reference background entropy calculation unit is used to calculate the reference background entropy as follows:

[0043]

[0044] Preferably, the method for calculating the background entropy E0 of each distance unit in the group target background environment determination module is as follows:

[0045] The second amplitude distribution calculation unit is used to acquire the current radar echo data, taking 2P data points before and after the detection unit [x]. N-P-1 ,…,x N-2 ,x N+2 ,…x N+1+P ], thus obtaining its amplitude distribution

[0046]

[0047] In the above formula, l = N-P+1,…,N-2,N+2,…,N+1+P, where l is the distance value of the P reference units before and after the detection unit excluding the protection unit;

[0048] The current background entropy calculation unit, used to detect the background entropy E0 of the current distance unit, is:

[0049]

[0050] This invention provides a rapid dense target detection device based on background entropy, including a memory and a processor. The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, it implements the rapid dense target detection method based on background entropy as described in the embodiments of this invention.

[0051] The present invention provides a computer-readable medium for storing a computer program, characterized in that, when the computer program is executed by one or more processors, it implements the method for rapid detection of dense targets based on background entropy as described in the embodiments of the present invention.

[0052] The advantages of this invention compared to the prior art are:

[0053] 1. This invention utilizes the difference in background entropy between echo information with and without targets to set a background entropy threshold and determine whether the current background environment is a group target environment. Then, a group target detection method based on deletion averaging is used to achieve rapid detection of dense targets. This invention, by judging the background environment, selects the deletion averaging constant false alarm rate (CFAR) detection method for targets in a group target background environment, and selects the conventional unit averaging CFAR detection method for targets in a single target background environment. This reduces the complexity of engineering implementation and reduces false alarms and missed alarms in conventional target detection, effectively ensuring rapid detection of dense targets in complex background environments.

[0054] 2. Improved detection performance. This invention selects conventional mean constant false alarm rate (CFAR) detection for single-target environments and removes CFAR detection for dense multi-target environments, thereby fully leveraging the advantages of different detectors and achieving optimal detection performance.

[0055] 3. Improved environmental adaptability. This invention analyzes the background environment through background entropy calculation. When multiple targets are present, it adaptively selects and deletes constant false alarm rate (CFAR) detections, effectively improving the radar target detection's ability to cope with complex environments. Attached Figure Description

[0056] Figure 1 This is a flowchart of the fast detection process for dense targets based on background entropy as described in an embodiment of the present invention;

[0057] Figure 2 This is the background entropy calculation result for the radar echo with or without a target in the embodiments of the present invention;

[0058] Figure 3 This is a schematic diagram illustrating the principle of removing constant false alarm rate (CFAR) detection in an embodiment of the present invention.

[0059] Figure 4 This is a graph showing the conventional mean constant false alarm rate (CFAR) detection results when there are two targets in an embodiment of the present invention.

[0060] Figure 5 This is a partial magnified view of the conventional mean constant false alarm rate (CFAR) detection result when there are two targets in an embodiment of the present invention.

[0061] Figure 6 This is a diagram showing the result of constant false alarm detection when there are two targets in an embodiment of the present invention.

[0062] Figure 7 This is a partially enlarged view of the deletion constant false alarm detection result when there are two targets in an embodiment of the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] This invention provides a fast detection method for dense targets based on background entropy. It determines the current background environment by calculating background entropy, adaptively selects different constant false alarm rate (CFAR) detection methods, and further optimizes the detection method for dense targets to improve detection performance when multiple targets appear in the background environment, while reducing the complexity of engineering implementation.

[0065] like Figure 1 As shown, the specific implementation steps of the present invention are as follows:

[0066] Step S1: When the radar is powered on normally and there is no target, acquire the echo data of the current single pulse, calculate the reference background entropy E of the current environment, and determine the group target background environment decision threshold T through the current value.

[0067] Step S11: Take 2P echo data points [x1, x2, ..., x 2P ], thus obtaining its amplitude distribution

[0068]

[0069] Step S12: The reference background entropy is calculated as follows:

[0070]

[0071] E is the background entropy obtained without a target;

[0072] Background entropy is obtained using echo data, such as Figure 2 For example 1, the echo background entropy calculation charts were obtained under multiple statistical tests with and without a target. It can be seen that the waveform entropy under the background with a target is less than that under the background without a target. The background entropy threshold can be used to effectively judge the background environment.

[0073] After obtaining the reference background entropy E, the decision threshold T for the group target environment is determined. Here, T is taken as E-1.5.

[0074] Step S2: After the radar is working normally, it receives radar echo data, generates a range sequence X, and calculates the background entropy E0 of each range cell in sequence X. It compares the entropy with the background threshold T. If the entropy is less than the threshold, it is considered to be the background environment of the group target.

[0075] Step S21: Acquire current radar echo data and obtain detection unit x N Number of 2P before and after [x] N-P-1 ,…,x N-2 ,x N+2 ,…x N+1+P ], thus obtaining its amplitude distribution

[0076]

[0077] In the above formula (l=N-P+1,…,N-2,N+2,…,N+1+P), l represents the P reference elements before and after removing the protection element.

[0078] Step S22: The background entropy E0 of the current detection range cell is:

[0079]

[0080] E0 is the background entropy calculated when the radar is operating normally and receiving echoes.

[0081] Step S3: For detection in environments that are not group targets, conventional unit average constant false alarm rate (CFAR) detection is used. The Nth data point of sequence X is detected, and the average value u of the data points from NP-1 to N-2 and from N+2 to N+1+P is obtained. To eliminate interference from nearby targets, the data points on both sides of the detected value, namely the N-1 and N+1 numbers, are used as protection units and are not included in the cumulative sum.

[0082] Step S4: For the detection of the background environment of the group targets, the constant false alarm rate (CFAR) detection is removed. The processed image is shown below. Figure 3 As shown, the Nth data point of sequence X is detected, and the data points from NP-1 to N-2 and from N+2 to N+1+P are sorted. The first r largest sample data points are removed, and the average value u of the remaining radar data is obtained. To eliminate interference from nearby targets, the data points on both sides of the detected value, namely the N-1 and N+1 numbers, are used as protection units and are not included in the summation. P is the number of reference units, where r represents the larger values ​​after sorting the current echo data. In the case of dense targets, these are considered target points.

[0083] Step S5: Output the detection threshold s by multiplying the unit average value u and the threshold M.

[0084] The threshold M is determined by the false alarm probability P. fa The reference unit P is determined, and the false alarm probability M = 2P(P) fa -1 / (2*P) -1) to obtain the threshold value M.

[0085] Step S6: Detect the value x of the sequence X detection unit. N Compare with the threshold s, if the detected value x N If the value is greater than the threshold s, then a target exists; otherwise, no target exists.

[0086] Step S7: Repeat steps S3 to S6 to complete the real-time detection of targets in the entire range cell of the radar using a pipelined detection method.

[0087] The following two sets of simulations further illustrate the rapid detection performance of dense targets using the present invention:

[0088] Simulation Example 2: Let P fa =10 -6 With reference element P = 16, the detection threshold M = 13.8 is obtained. Assume there are two target echoes, 15m apart, with a bandwidth of 50MHz. The results of conventional constant false alarm rate (CFAR) detection are as follows: Figure 4-5 As shown, the results of removing the constant false alarm rate (CFAR) detection method are as follows: Figure 6-7As shown, the curves representing threshold values ​​are all located at the top, while the curves representing inspection results are all located at the bottom. Simulation examples demonstrate that in a multi-target environment, the deletion constant false alarm rate (CFAR) detection method of this invention can detect multiple targets, while the conventional mean CFAR method fails to detect them, fully illustrating the feasibility of this method.

[0089] In summary, the background entropy-based rapid target detection method provided in this solution not only solves the problem of missed detection in multi-target detection, but also retains the advantages of simple design and low computational load in single-target detection. It fully leverages the advantages of both methods and is very suitable for engineering applications of radar target detection.

[0090] Example 2

[0091] Based on the same concept, this invention provides a fast dense target detection system based on background entropy, comprising:

[0092] The background entropy calculation module is used to acquire the echo data of the current single pulse when the radar is powered on and operating normally without a target, calculate the reference background entropy E of the current environment, and determine the group target background environment decision threshold T based on the current value.

[0093] The target group background environment determination module is used to receive radar echo data after the radar is working normally, generate a range sequence X, calculate the background entropy E0 of each range unit of sequence X, compare it with the background threshold value T, and if it is less than the background threshold value, it is the target group background environment.

[0094] The first detection module is used to detect data in the background environment of the target group if it is not the target group. In this case, it adopts the conventional unit average constant false alarm rate detection, detects the Nth data of sequence X, and averages the data from NP-1 to N-2 and the data from N+2 to N+1+P to obtain the unit average value u. In order to eliminate the interference of neighboring targets, the data on both sides of the detection value, namely the N-1 and N+1 numbers, are used as protection units and are not included in the cumulative sum.

[0095] The second detection module is used to detect the Nth data in sequence X if the target group is in the background environment. If so, constant false alarm detection is used. The Nth data in sequence X is detected, and the N-1 to N-2 data and the N+2 to N+1+P data are sorted. The first r larger sample data are removed, and the average value u of the remaining radar data is obtained. In order to eliminate the interference of nearby targets, the data on both sides of the detection value, namely the N-1 and N+1 numbers, are used as protection units and are not included in the summation.

[0096] The threshold calculation module is used to output the detection threshold s by multiplying the unit average value u and the threshold M;

[0097] The target determination module is used to determine the detection value x of the sequence X detection unit.N Compare with the threshold s, if the detected value x N If the value is greater than the threshold s, then a target exists; otherwise, no target exists.

[0098] The real-time detection module is used to repeatedly execute the first detection module to the target judgment module, and is used to complete the real-time detection of targets in the entire range unit of the radar using pipelined detection.

[0099] Where P is the number of reference units, and r represents several larger values ​​after sorting the current echo data, which are considered target points when dense targets are present.

[0100] Preferably, the method for calculating the reference background entropy E in the background entropy calculation module includes:

[0101] The first amplitude distribution calculation unit is used to acquire background echo data in a targetless environment, taking 2P data points [x1, x2, ..., x]. 2P ], thus obtaining its amplitude distribution

[0102]

[0103] The reference background entropy calculation unit is used to calculate the reference background entropy as follows:

[0104]

[0105] Preferably, the method for calculating the background entropy E0 of each distance unit in the group target background environment determination module is as follows:

[0106] The second amplitude distribution calculation unit is used to acquire the current radar echo data, taking 2P data points before and after the detection unit [x]. N-P-1 ,…,x N-2 ,x N+2 ,…x N+1+P ], thus obtaining its amplitude distribution

[0107]

[0108] In the above formula, l = N-P+1,…,N-2,N+2,…,N+1+P, where l is the distance value of the P reference units before and after the detection unit excluding the protection unit;

[0109] The current background entropy calculation unit, used to detect the background entropy E0 of the current distance unit, is:

[0110]

[0111] The specific principles and implementation methods of the above modules and units are as described in the method in Embodiment 1, and will not be repeated here.

[0112] Example 3

[0113] Based on the same concept, the present invention provides a rapid detection device for dense targets based on background entropy, including a memory and a processor. The memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, it implements the rapid detection method for dense targets based on background entropy as described in Embodiment 1 of the present invention.

[0114] This background entropy-based rapid target detection device can vary considerably depending on its configuration and performance. It may include one or more central processing units (CPUs) and memory, and one or more storage media (e.g., one or more mass storage devices) for storing applications or data. The memory and storage media can be temporary or persistent storage. The program stored on the storage media may include one or more modules, each of which may include a series of instruction operations on the background entropy-based rapid target detection device.

[0115] The present invention provides a computer-readable medium storing a computer program that, when executed by one or more processors, implements the method for rapid detection of dense targets based on background entropy as described in Embodiment 1 of the present invention.

[0116] If the modules in Embodiment 2 are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in software form. The computer-readable storage medium can be a non-volatile computer-readable storage medium, or it can be a volatile computer-readable storage medium. The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the method for rapid detection of dense targets based on background entropy in Embodiment 1.

[0117] Those skilled in the art will understand that the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in software. This computer software is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A fast method for detecting dense targets based on background entropy, characterized in that, Includes the following steps: Step S1: When the radar is powered on and working normally and there is no target, acquire the echo data of the current single pulse, calculate the reference background entropy E of the current environment, and determine the group target background environment decision threshold T through the current value; Step S2: After the radar is working normally, receive radar echo data, generate a range sequence X, and calculate the background entropy E0 of each range unit in sequence X. Compare it with the background threshold T. If it is less than the background threshold T, it is the background environment of the group target. Step S3: If the detection is not performed in the background environment of the target group, then the conventional unit average constant false alarm rate detection is adopted. The Nth data of sequence X is detected, and the average value u of the data from NP-1 to N-2 and the data from N+2 to N+1+P is obtained. In order to eliminate the interference of neighboring targets, the data on both sides of the detection value, namely the N-1 and N+1 numbers, are used as protection units and are not included in the cumulative sum. Step S4: If the detection is performed in the background environment of the target group, then the constant false alarm rate (CFAR) detection is removed. The Nth data in sequence X is detected, and the data from NP-1 to N-2 and from N+2 to N+1+P are sorted. The first r largest sample data are removed, and the average value u of the remaining radar data is obtained. To eliminate interference from nearby targets, the data on both sides of the detection value, namely the N-1 and N+1 numbers, are used as protection units and are not included in the summation. Step S5: Output the detection threshold s by multiplying the unit average value u and the threshold M; Step S6: Detect the value x of the sequence X detection unit. N Compare with the threshold s, if the detected value x N If the value is greater than the threshold s, then a target exists; otherwise, no target exists. Step S7: Repeat steps S3 to S6 to complete the real-time detection of targets in the entire range cell of the radar using a pipelined detection method; Where P is the number of reference units, and r represents several larger values ​​after the current echo data is sorted.

2. The method for fast detection of dense targets based on background entropy as described in claim 1, characterized in that, The method for calculating the reference background entropy E in step S1 includes: Step S11: Acquire background echo data without a target, and take 2P data points [x1, x2, ..., x 2P ], thus obtaining its amplitude distribution Step S12: The reference background entropy is calculated as follows:

3. The method for fast detection of dense targets based on background entropy as described in claim 1, characterized in that, In step S1, the threshold T for judging the background environment of the group target is determined by the reference background entropy E, and is usually taken as 1.5dB less than the reference background entropy.

4. The method for fast detection of dense targets based on background entropy as described in claim 1, characterized in that, The method for calculating the background entropy E0 of each distance unit in sequence X in step S2 is as follows: Step S21: Acquire the current radar echo data, and take 2P data points before and after the detection unit [x] N-P-1 ,…,x N-2 ,x N+2 ,…x N+1+P ], thus obtaining its amplitude distribution In the above formula, l = N-P+1,…,N-2,N+2,…,N+1+P, where l is the distance value of the P reference units before and after the detection unit excluding the protection unit; Step S22: The background entropy E0 of the current detection range cell is:

5. The method for fast detection of dense targets based on background entropy as described in claim 1, characterized in that, In step S5, the threshold M is determined by the false alarm probability P. fa The reference unit P is determined, and the false alarm probability threshold M is calculated using the following formula: M = 2P(P fa -1 / (2*P) -1).

6. A rapid detection system for dense targets based on background entropy, characterized in that, include: The background entropy calculation module is used to acquire the echo data of the current single pulse when the radar is powered on and operating normally without a target, calculate the reference background entropy E of the current environment, and determine the group target background environment decision threshold T based on the current value. The target group background environment determination module is used to receive radar echo data after the radar is working normally, generate a range sequence X, calculate the background entropy E0 of each range unit of sequence X, compare it with the background threshold value T, and if it is less than the background threshold value, it is the target group background environment. The first detection module is used to detect data in the background environment of the target group if it is not the target group. In this case, it adopts the conventional unit average constant false alarm rate detection, detects the Nth data of sequence X, and averages the data from NP-1 to N-2 and the data from N+2 to N+1+P to obtain the unit average value u. In order to eliminate the interference of neighboring targets, the data on both sides of the detection value, namely the N-1 and N+1 numbers, are used as protection units and are not included in the cumulative sum. The second detection module is used to detect the Nth data in sequence X if the target group is in the background environment. If so, constant false alarm detection is used. The Nth data in sequence X is detected, and the N-1 to N-2 data and the N+2 to N+1+P data are sorted. The first r larger sample data are removed, and the average value u of the remaining radar data is obtained. In order to eliminate the interference of nearby targets, the data on both sides of the detection value, namely the N-1 and N+1 numbers, are used as protection units and are not included in the summation. The threshold calculation module is used to output the detection threshold s by multiplying the unit average value u and the threshold M; The target determination module is used to determine the detection value x of the sequence X detection unit. N Compare with the threshold s, if the detected value x N If the value is greater than the threshold s, then a target exists; otherwise, no target exists. The real-time detection module is used to repeatedly execute the first detection module to the target judgment module, and is used to complete the real-time detection of targets in the entire range unit of the radar using pipelined detection. Where P is the number of reference units, and r represents several larger values ​​after the current echo data is sorted.

7. The rapid detection system for dense targets based on background entropy as described in claim 6, characterized in that, The method for calculating the reference background entropy E in the background entropy calculation module includes: The first amplitude distribution calculation unit is used to acquire background echo data in a targetless environment, taking 2P data points [x1, x2, ..., x]. 2P ], thus obtaining its amplitude distribution The reference background entropy calculation unit is used to calculate the reference background entropy as follows:

8. The rapid detection system for dense targets based on background entropy as described in claim 6, characterized in that, The method for calculating the background entropy E0 of each distance unit in sequence X in the group target background environment determination module is as follows: The second amplitude distribution calculation unit is used to acquire the current radar echo data, taking 2P data points before and after the detection unit [x]. N-P-1 ,…,x N-2 ,x N+2 ,…x N+1+P ], thus obtaining its amplitude distribution In the above formula, l = N-P+1,…,N-2,N+2,…,N+1+P, where l is the distance value of the P reference units before and after the detection unit excluding the protection unit; The current background entropy calculation unit, used to detect the background entropy E0 of the current distance unit, is:

9. A rapid detection device for dense targets based on background entropy, characterized in that, The method includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor, when executing the computer-readable instructions, implements the method for rapid detection of dense targets based on background entropy as described in any one of claims 1 to 5.

10. A computer-readable medium storing a computer program, characterized in that, When the computer program is executed by one or more processors, it implements the method for rapid detection of dense targets based on background entropy as described in any one of claims 1 to 5.

Images