CA-CFAR detection method and system based on FFT

Abstract

The invention discloses a CA-CFAR detection method and system based on FFT, and the method specifically comprises the steps: firstly obtaining an input signal sequence, constructing a convolution kernel according to a preset reference unit number and a preset protection unit number, and carrying out the zero filling of the signal sequence and the convolution kernel; fast Fourier transform is carried out on the signal sequence and the convolution kernel after zero filling, and point multiplication operation is carried out in a frequency domain; performing inverse FFT (Fast Fourier Transform) on a point multiplication result to obtain a time domain convolution result, and intercepting an effective part according to a signal length; and finally, calculating a detection threshold according to the false alarm probability, and judging the to-be-detected unit. According to the method, the CFAR clutter estimation process is equivalently converted into convolution of the signal and the specific convolution kernel, and frequency domain convolution is realized by using FFT, so that the calculation efficiency is improved while the precision is ensured, and rapid detection of large-scale signals is realized.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and in particular to a CA-CFAR detection method and system based on FFT. Background Technology

[0002] Constant False Alarm Rate (CFAR) is a statistical decision method widely used in radar target detection. Its core idea is to adaptively adjust the detection threshold under a given false alarm probability to distinguish targets from noise or clutter. Traditional CA-CFAR (Cell Averaging Constant False Alarm Rate) estimates the local clutter level by placing reference cells on both sides of the cell to be detected, and then calculates the detection threshold based on this estimate.

[0003] like Figure 1 As shown, in the traditional implementation method, the key steps of CA-CFAR detection are: (1) performing a sliding window operation on the input signal; (2) calculating the average value of the reference cells on both sides of each unit to be detected; (3) calculating the detection threshold according to the given false alarm probability; (4) comparing the unit to be detected with the threshold and making a target decision.

[0004] However, the traditional CA-CFAR algorithm uses a time-domain sliding window method to calculate the reference window, resulting in a computational complexity of approximately [missing information]. ,in For signal length, The computational overhead increases significantly when the signal size is large, such as a two-dimensional matrix range-Doppler map, or when there are many reference units, making it difficult to meet real-time processing requirements. Furthermore, traditional sliding window methods lack scalability on embedded or GPU parallel platforms, impacting the real-time performance of high-resolution radar systems.

[0005] Therefore, how to improve the computational efficiency of the CFAR algorithm while ensuring detection performance has become a key issue in radar signal processing. Summary of the Invention

[0006] The purpose of this invention is to provide a CA-CFAR detection method that can improve computational efficiency while ensuring accuracy, and achieve rapid detection of large-scale signals.

[0007] The technical solution to achieve the purpose of this invention is: an FFT-based CA-CFAR detection method, comprising the following steps:

[0008] Step 1: Obtain the input signal sequence ;

[0009] Step 2: Based on the preset number of reference units With the number of protection units Construct convolution kernel ;

[0010] Step 3, Signal and convolution kernel Perform zero-filling;

[0011] Step 4: Fill the zeros separately , Perform a fast Fourier transform;

[0012] Step 5: Perform dot multiplication in the frequency domain;

[0013] Step 6: Analyze the results Perform inverse FFT transformation to obtain temporal convolution results And extract the effective portion based on the signal length;

[0014] Step 7: Based on the false alarm probability Calculate the detection threshold and make a decision on the unit to be detected.

[0015] Furthermore, step 2, based on a preset number of reference units... With the number of protection units Construct convolution kernel convolution kernel Defined as:

[0016] (1)

[0017] The convolution kernel The structure can be adjusted to accommodate different numbers of reference elements. and number of protection units It is used for constant false alarm detection under different signal-to-noise ratio environments.

[0018] Furthermore, the signal described in step 3 and convolution kernel To perform zero-filling, the formula is:

[0019] (2).

[0021] Furthermore, step 4 involves separately processing the zero-filled samples... , The formula for performing a Fast Fourier Transform is:

[0022] (3).

[0024] Furthermore, the dot product operation performed in the frequency domain as described in step 5 is as follows:

[0025] (4).

[0027] Furthermore, the result described in step 6 Perform inverse FFT transformation to obtain temporal convolution results And based on the signal length, the effective portion is truncated, as follows:

[0028] Step 6.1, Review the results Perform inverse FFT transformation to obtain temporal convolution results The formula is:

[0029] (5)

[0030] Step 6.2: Extract the temporal convolution result based on the signal length. The effective part, the formula is:

[0031] (6).

[0033] An FFT-based CA-CFAR detection system is provided. This system implements the aforementioned FFT-based CA-CFAR detection method. The system includes an input module, a convolution kernel construction module, a zero-padding module, a Fourier transform module, a dot product operation module, an inverse FFT transform module, and a decision module.

[0034] Input module, acquires input signal sequence ;

[0035] The convolution kernel construction module is based on a preset number of reference units. With the number of protection units Construct convolution kernel ;

[0036] Zero-fill module, for signals and convolution kernel Perform zero-filling;

[0037] The Fourier transform module performs zero-padding on the following: , Perform a fast Fourier transform;

[0038] The dot product operation module performs dot product operations in the frequency domain;

[0039] The inverse FFT transform module processes the results. Perform inverse FFT transformation to obtain temporal convolution results And extract the effective portion based on the signal length;

[0040] The decision module is based on the false alarm probability. Calculate the detection threshold and make a decision on the unit to be detected.

[0041] A computer device includes a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the FFT-based CA-CFAR detection method by executing the computer instructions.

[0042] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the FFT-based CA-CFAR detection method.

[0043] A computer program product includes computer instructions for causing a computer to execute the FFT-based CA-CFAR detection method.

[0044] Compared with the prior art, the present invention has the following significant advantages: (1) It transforms the CFAR clutter estimation process into an equivalent convolution of the signal with a specific convolution kernel, and uses FFT to achieve frequency domain convolution, which improves computational efficiency while ensuring accuracy, and realizes rapid detection of large-scale signals; (2) It is suitable for rapid target detection of pulse compression output data, radar echo signals or other one-dimensional time-series signals, with a signal length of or number of reference units Compared to the traditional sliding window convolution method, the computation time can be reduced by about 70% to 90%. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the traditional sliding window CA-CFAR detection method.

[0046] Figure 2 This is a flowchart illustrating a CA-CFAR detection method based on FFT according to the present invention.

[0047] Figure 3 This is a line graph comparing the CA-CFAR detection method based on FFT and the traditional sliding window CA-CFAR detection method over time with the number of detection units in this embodiment of the invention. Detailed Implementation

[0048] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0049] In one specific embodiment, such as Figure 2As shown, an FFT-based CA-CFAR detection method is provided, including the following steps:

[0050] Step 1: Obtain the input signal sequence ;

[0051] Step 2: Based on the preset number of reference units With the number of protection units Construct convolution kernel convolution kernel Defined as:

[0052] (1)

[0053] Furthermore, the convolution kernel The structure can be flexibly adjusted to accommodate different numbers of reference elements. and number of protection units This makes it suitable for constant false alarm rate (CFAR) detection under different signal-to-noise ratio (SNR) environments.

[0054] Step 3, Signal and convolution kernel To perform zero-filling, the formula is:

[0055] (2)

[0056] Step 4: Fill the zeros separately , The formula for performing a Fast Fourier Transform is:

[0057] (3)

[0058] Step 5: Perform a dot product operation in the frequency domain, using the following formula:

[0059] (4)

[0060] Step 6: Analyze the results Perform inverse FFT transformation to obtain temporal convolution results And based on the signal length, the effective portion is truncated, as follows:

[0061] Step 6.1, Review the results Perform inverse FFT transformation to obtain temporal convolution results The formula is:

[0062] (5)

[0063] Step 6.2: Extract the temporal convolution result based on the signal length. The effective part, the formula is:

[0064] (6)

[0065] Step 7: Based on the false alarm probability Calculate the detection threshold and make a decision on the unit to be detected.

[0066] Furthermore, the frequency domain transformation result of the convolution kernel can be pre-calculated and cached for reuse in multiple detections, thereby reducing the amount of real-time computation.

[0067] Furthermore, the method is applicable to rapid target detection of pulse compression output data, radar echo signals, or other one-dimensional time-series signals, within a signal length range. or number of reference units Compared to the traditional sliding window convolution method, the computation time can be reduced by about 70% to 90%.

[0068] Furthermore, the method can be extended to two-dimensional CA-CFAR detection.

[0069] In one specific embodiment, the performance advantages of the method of the present invention are illustrated by comparing the FFT-based CA-CFAR detection method provided by the present invention with the traditional sliding window CA-CFAR detection method.

[0070] The pulse count is set to 2048, the distance element count to 4096, the protection element count to 4, and the false alarm probability to 1e-6. The number of reference elements is increased from 4 to 128. The total computation time of the FFT method and the traditional sliding window method is compared as follows: Figure 3 As shown. By Figure 3 It can be seen that when the number of reference cells is small, the computational cost of the FFT method is slightly greater than that of the sliding window method, but as the reference window increases, the efficiency advantage of the FFT method becomes significant.

[0071] when At the same time, the time consumption of the FFT method remains basically constant, while that of the sliding window method increases linearly with the number of reference cells. Therefore, the method of this invention has a significant performance advantage in large-scale CFAR detection.

[0072] In one specific embodiment, an FFT-based CA-CFAR detection system is provided. This system implements the aforementioned FFT-based CA-CFAR detection method. The system includes an input module, a convolution kernel construction module, a zero-padding module, a Fourier transform module, a dot product operation module, an inverse FFT transform module, and a decision module.

[0073] Input module, acquires input signal sequence ;

[0074] The convolution kernel construction module is based on a preset number of reference units. With the number of protection units Construct convolution kernel ;

[0075] Zero-fill module, for signals and convolution kernel Perform zero-filling;

[0076] The Fourier transform module performs zero-padding on the following: , Perform a fast Fourier transform;

[0077] The dot product operation module performs dot product operations in the frequency domain;

[0078] The inverse FFT transform module processes the results. Perform inverse FFT transformation to obtain temporal convolution results And extract the effective portion based on the signal length;

[0079] The decision module is based on the false alarm probability. Calculate the detection threshold and make a decision on the unit to be detected.

[0080] In one specific embodiment, a computer device is provided, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the FFT-based CA-CFAR detection method by executing the computer instructions.

[0081] In one specific embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the FFT-based CA-CFAR detection method.

[0082] In one specific embodiment, a computer program product is provided, including computer instructions for causing a computer to execute the FFT-based CA-CFAR detection method.

[0083] It should be noted that the FFT-based CA-CFAR detection system provided in this embodiment of the invention is used to execute all the process steps of the FFT-based CA-CFAR detection method in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0084] This invention also provides a computer device. The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as an FFT-based CA-CFAR detection method program. When the processor executes the computer program, it implements the steps in the various FFT-based CA-CFAR detection method embodiments described above. Alternatively, when the processor executes the computer program, it implements the functions of each module in the various device embodiments described above.

[0085] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer device.

[0086] The computer device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of computer devices and do not constitute a limitation on the computer device. It may include more or fewer components than described above, or a combination of certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0087] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the computer device, connecting various parts of the computer device via various interfaces and lines.

[0088] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0089] If the modules / units integrated into the computer device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0090] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0091] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A FFT-based CA-CFAR detection method, characterized in that, The method comprises the following steps: Step 1, obtaining an input signal sequence ; Step 2, according to the preset reference unit number with the protection unit number , construct a convolution kernel ; Step 3, padding the signal and the convolution kernel zero padding; Step 4, perform fast Fourier transform on the zero-padded , respectively; Step 5, performing point multiplication operation in frequency domain; Step 6, result Performing inverse FFT transform to obtain time domain convolution result and intercepting the valid part according to the signal length; Step 7, false alarm probability The detection threshold is calculated to make a decision on the unit under test.

2. The FFT-based CA-CFAR detection method of claim 1, wherein, The reference unit number according to the preset The protection unit number The convolution kernel is constructed The convolution kernel is defined as: （1） The convolution kernel The structure can be adjusted to adapt to different reference unit numbers And the number of protection units For constant false alarm detection in different signal-to-noise ratio environments.

3. The FFT-based CA-CFAR detection method of claim 2, wherein, the signal described in step 3 and the convolution kernel zero padding is performed, with the formula: （2）。 4. The FFT-based CA-CFAR detection method of claim 3, wherein, zero-padded , are subjected to a fast Fourier transform, the formula of which is: （3）。 5. The FFT-based CA-CFAR detection method of claim 4, wherein, The formula of the point multiplication operation in the step 5 is: （4）。 6. The FFT-based CA-CFAR detection method of claim 5, wherein, The result described in step 6 Performing inverse FFT transform to obtain time domain convolution result And according to the signal length, the effective part is intercepted, specifically as follows: Step 6.1, on the result Performing an inverse FFT transform obtains the time domain convolution result The formula is: （5） Step 6.2: Extract the temporal convolution result based on the signal length. The effective part, the formula is: （6）。 7. A FFT-based CA-CFAR detection system, characterized in that, The system is used for implementing the FFT-based CA-CFAR detection method in any one of claims 1-6, and the system comprises an input module, a convolution kernel construction module, a zero padding module, a Fourier transform module, a point multiplication operation module, an inverse FFT transform module and a decision module: An input module acquires an input signal sequence ; The convolution kernel construction module is configured to construct a convolution kernel according to a preset reference cell number and a protection cell number , and construct the convolution kernel ; zero padding module, to signal and a convolution kernel perform zero padding; a Fourier transform module, respectively, performs fast Fourier transform on the zero-padded , ​ The point multiplication operation module performs point multiplication operation in frequency domain; an inverse FFT transform module, to the result performing an inverse FFT transform to obtain a time-domain convolution result and cutting the effective part according to the signal length; A decision module decides the to-be-detected unit according to the false alarm probability The detection threshold is calculated to decide the to-be-detected unit.

8. A computer device, comprising: The method comprises the following steps: A memory and a processor, which are in communication connection with each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the FFT-based CA-CFAR detection method in any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps in the FFT-based CA-CFAR detection method in any one of claims 1-6.

10. A computer program product, characterised in that, The computer instructions are used to make the computer execute the FFT-based CA-CFAR detection method in any one of claims 1-6.

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