A segment mean constant false alarm rate detection method based on FFT accumulation

Abstract

The present application relates to the technical field of unmanned aerial vehicle detection, in particular to a segmented mean constant false alarm rate detection method based on FFT accumulation; first, the collected unmanned aerial vehicle digital signal is subjected to 75% time overlap FFT spectrum accumulation processing, then a segmented mean constant false alarm rate algorithm is used for dynamic threshold detection, a constant false alarm rate threshold V T is calculated, the size of the current frequency point data frame and the constant false alarm rate threshold V T is compared, and the index number and data frame of the frequency point greater than the constant false alarm rate threshold V T are output, the accuracy of noise threshold acquisition and the sensitivity of signal detection are improved, the problem of large noise variance fluctuation caused by non-uniform clutter such as GPS navigation, mobile phone communication signal and wifi signal in the environment is avoided, and more distant target detection is realized.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) detection technology, and more specifically, to a segmented mean constant false alarm rate (CFAR) detection method based on FFT accumulation. Background Technology

[0002] Constant false alarm rate (CFAR) processing is a signal processing technique that provides an adaptive detection threshold. Under constant false alarm rate detection conditions, the dynamic threshold value for amplitude detection is related to the noise power. Traditional CFARs include CA-CFAR, SO-CFAR, GO-CFAR, ordered statistical cell averaging (OSCA) CFAR, ordered statistical greater than (OSGO) CFAR, and ordered statistical smaller than (OSSO) CFAR, which are widely used in radar detection, radio signal monitoring, photoelectric tracking, and other fields.

[0003] Under constant false alarm rate (CFAR) probability detection, the dynamic threshold for amplitude detection is determined solely by the variance of the noise. Therefore, the dynamic threshold detection problem mainly lies in the dynamic detection of noise. For threshold dynamic detection of signals such as UAV remote control radio signals, aircraft image transmission signals, and navigation signals, CFAR technology is often used to first extract noise from the acquired UAV signals in the time or frequency domain, and then perform threshold decision and parameter measurement to achieve target recognition.

[0004] In conventional constant false alarm rate (CFAR) noise extraction, a sliding window covering several distance units is typically set at the front and rear edges of the detection unit. The mean of reference samples within the sliding window is used to form local estimates for the front and rear edges. These local estimates are then averaged, selected (larger or smaller), or weighted to determine the average background clutter estimate for the detection unit. However, for UAV remote control signals, which are often accompanied by non-uniform clutter such as navigation, mobile communication, and Wi-Fi, the noise variance fluctuates significantly. At low signal-to-noise ratios, traditional CFAR dynamic threshold detection techniques are computationally complex and have a low target detection probability. Summary of the Invention

[0005] This invention addresses the problems of computational complexity and low target detection probability in existing constant false alarm rate (CFAR) dynamic threshold detection techniques. It proposes a piecewise mean CFAR detection method based on FFT accumulation. First, the acquired UAV digital signal undergoes 75% time overlap FFT spectral accumulation processing. Then, a piecewise mean CFAR algorithm is used for dynamic threshold detection to calculate the CFAR threshold V. T Compare the data frames at the current frequency with the constant false alarm threshold V.T The size of the output is greater than the constant false alarm threshold V. T The frequency index number and data frame improve the accuracy of noise threshold acquisition and signal detection sensitivity, solve the problem of large noise variance fluctuation, and realize target detection at a greater distance.

[0006] The specific implementation details of this invention are as follows:

[0007] A segmented mean constant false alarm rate (CFAR) detection method based on FFT accumulation is proposed. This method performs 75% time overlap FFT spectral accumulation on the acquired UAV digital signal, and then uses a segmented mean CFAR algorithm for dynamic threshold detection to calculate the CFAR threshold V. T Compare the data frames at the current frequency with the constant false alarm threshold V. T The size of the output is greater than the constant false alarm threshold V. T The frequency index number and data frame.

[0008] To better realize the present invention, the method further includes the following steps:

[0009] Step 1: Perform a 75% time overlap FFT transform on the acquired UAV digital signal;

[0010] Step 2: Perform spectrum accumulation processing on the UAV digital signal after 75% time overlap FFT transformation;

[0011] Step 3: Use the segmented mean constant false alarm rate (CFAR) algorithm to perform dynamic threshold detection and extract the CFAR threshold of the accumulated UAV digital signal;

[0012] Step 4: Calculate the final constant false alarm rate (CFAR) threshold V based on the CFAR threshold. T ;

[0013] Step 5: Compare the data frame at the current frequency with the constant false alarm threshold V. T The size of the output is greater than the constant false alarm threshold V. T The frequency index number and data frame.

[0014] To better realize the present invention, step 1 further includes the following steps:

[0015] Step 11: Divide the UAV digital signal into four parallel UAV digital signals;

[0016] Step 12: Based on the pipeline depth of the UAV digital signal data frame, delay the four UAV digital signals sequentially by one-quarter of the pipeline depth to obtain four UAV digital signals with 75% time overlap.

[0017] Step 13: Simultaneously perform FFT transformation on the four 75% time overlap UAV digital signals to obtain four FFT UAV digital signals and four data frames and four index numbers corresponding to the four FFT UAV digital signals.

[0018] To better implement the present invention, step 2 is further described as follows: the four FFT UAV digital signals are time-aligned and modulo operations are performed. Based on the position of the frequency point corresponding to the index number, the data frames of the four FFT UAV digital signals are accumulated and summed to obtain the accumulated FFT UAV digital signal data frame X and index number I.

[0019] To better realize the present invention, step 3 further includes the following steps:

[0020] Step 31: Divide the data frame X into N segments according to the set number of segments N, number of segment points K, and index number I, in chronological order.

[0021] Step 32: Calculate the average value of N data frames based on the starting position i marked by index number I, and record the segment number n corresponding to each data frame;

[0022] Step 33: Use the segment number n as the write address of RAM port A, use the average value of the segments as the write data of RAM port A, and store the average value of the segments into RAM according to the time order.

[0023] Step 34: Use the bubble sort method to sort the segmented means in ascending order to obtain the indices m0, m1, m2, and m3 of the four smallest segmented means;

[0024] Step 35: Delay data frame X and index number I by K points to obtain data frame A1 and index number b1, and align them with the time sequence of the segment mean of the current segment.

[0025] Step 36: Calculate the piecewise variance based on the subscript position i corresponding to index number b1, and calculate the index n0 of the smallest piecewise variance;

[0026] Step 37: Use the index n0 of the smallest segment variance as the read address of RAM to calculate the constant false alarm threshold.

[0027] To better implement this invention, step 4 is further defined as follows: using the constant false alarm rate (CFAR) threshold as the noise floor Z, a noise factor α is introduced to obtain the final CFAR threshold V. T .

[0028] To better implement this invention, step 5 is further defined as follows: delaying data frame A1 and index number b1 simultaneously by NK clock cycles to obtain final data frame A2 and index number b2, and then comparing data frame A2 with the constant false alarm threshold value V. T Timing alignment, and finally, the constant false alarm threshold V. T The data frame A2 is input into the comparator, and the output is the index number and data frame corresponding to the frequency point that is greater than the constant false alarm threshold.

[0029] The present invention has the following beneficial effects:

[0030] (1) This invention improves the sensitivity and time resolution of the collected UAV digital signal by performing 75% time overlap FFT spectrum accumulation processing. Because the signal is accumulated multiple times, it avoids the problem of large noise variance fluctuation caused by non-uniform clutter such as GPS navigation, mobile communication signals, and wifi signals in the environment.

[0031] (2) Based on the cumulative processing, the present invention uses a segmented average to obtain the constant false alarm threshold for digital signal processing, which can quickly detect the noise floor threshold of the UAV. It has the characteristics of easy calculation, convenient implementation and high target detection probability.

[0032] (3) When acquiring the dynamic threshold, the present invention sorts and detects the FFT spectrum signal with 75% time overlap by segmented average, which improves the accuracy of noise threshold acquisition and the sensitivity of signal detection, and realizes target detection at a greater distance.

[0033] (4) The constant false alarm algorithm implemented in this invention is based on a parallel architecture and adopts a pipeline design, which is easy to implement on FPGA and consumes less resources. Attached Figure Description

[0034] Figure 1 A flowchart illustrating the segmented mean constant false alarm rate detection method based on FFT accumulation provided in this embodiment of the invention;

[0035] Figure 2 The flowchart of the accumulation process provided in the embodiments of the present invention;

[0036] Figure 3 A flowchart of the FFT transformation process with 75% time overlap provided for embodiments of the present invention. Detailed Implementation

[0037] To more clearly illustrate the technical solutions of the embodiments of the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments, and therefore should not be regarded as a limitation on the scope of protection. 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.

[0038] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set up," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0039] Example 1:

[0040] This embodiment proposes a segmented mean constant false alarm rate (CFAR) detection method based on FFT accumulation. The method involves performing 75% time overlap FFT spectrum accumulation on the acquired UAV digital signal, followed by dynamic threshold detection using the segmented mean CFAR algorithm to calculate the CFAR threshold V. T Compare the data frames at the current frequency with the constant false alarm threshold V. T The size of the output is greater than the constant false alarm threshold V. T The frequency index number and data frame.

[0041] Working principle: This embodiment first performs 75% time overlap FFT spectrum accumulation processing on the acquired UAV digital signal, and then uses a segmented mean constant false alarm rate (CFAR) algorithm for dynamic threshold detection to calculate the CFAR threshold V. T Compare the data frames at the current frequency with the constant false alarm threshold V. T The size of the output is greater than the constant false alarm threshold V. T The frequency index number and data frame improve the accuracy of noise threshold acquisition and signal detection sensitivity, solve the problem of large noise variance fluctuation, and realize target detection at a greater distance.

[0042] Example 2:

[0043] This embodiment is based on Embodiment 1 above and is described in the form of steps.

[0044] The method specifically includes the following steps:

[0045] Step 1: Perform a 75% time overlap FFT transform on the acquired UAV digital signal;

[0046] Furthermore, step 1 specifically includes the following steps:

[0047] Step 11: Divide the UAV digital signal into four parallel UAV digital signals;

[0048] Step 12: Based on the pipeline depth of the UAV digital signal data frame, delay the four UAV digital signals sequentially by one-quarter of the pipeline depth to obtain four UAV digital signals with 75% time overlap.

[0049] Step 13: Simultaneously perform FFT transformation on the four 75% time overlap UAV digital signals to obtain four FFT UAV digital signals and four data frames and four index numbers corresponding to the four FFT UAV digital signals.

[0050] Step 2: Perform spectrum accumulation processing on the UAV digital signal after 75% time overlap FFT transformation;

[0051] Further, the specific operation of step 2 is as follows: the four FFT UAV digital signals are time-aligned and modulo operation is performed. According to the position of the frequency point corresponding to the index number, the data frames of the four FFT UAV digital signals are accumulated and summed to obtain the accumulated FFT UAV digital signal data frame X and index number I.

[0052] Step 3: Use the segmented mean constant false alarm rate (CFAR) algorithm to perform dynamic threshold detection and extract the CFAR threshold of the accumulated UAV digital signal;

[0053] Furthermore, step 3 specifically includes the following steps:

[0054] Step 31: Divide the data frame X into N segments according to the set number of segments N, number of segment points K, and index number I, in chronological order.

[0055] Step 32: Calculate the average value of N data frames based on the starting position i marked by index number I, and record the segment number n corresponding to each data frame;

[0056] Step 33: Use the segment number n as the write address of RAM port A, use the average value of the segments as the write data of RAM port A, and store the average value of the segments into RAM according to the time order.

[0057] Step 34: Use the bubble sort method to sort the segmented means in ascending order to obtain the indices m0, m1, m2, and m3 of the four smallest segmented means;

[0058] Step 35: Delay data frame X and index number I by K points to obtain data frame A1 and index number b1, and align them with the time sequence of the segment mean of the current segment.

[0059] Step 36: Calculate the piecewise variance based on the subscript position i corresponding to index number b1, and calculate the index n0 of the smallest piecewise variance;

[0060] Step 37: Use the index n0 of the smallest segment variance as the read address of RAM to calculate the constant false alarm threshold.

[0061] Step 4: Calculate the final constant false alarm rate (CFAR) threshold V based on the CFAR threshold. T ;

[0062] Furthermore, step 4 specifically involves: using the constant false alarm rate (CFAR) threshold as the noise floor Z, introducing a noise factor α to obtain the final CFAR threshold V. T .

[0063] Step 5: Compare the data frame at the current frequency with the constant false alarm threshold V. T The size of the output is greater than the constant false alarm threshold V. T The frequency index number and data frame.

[0064] Further, step 5 specifically involves delaying data frame A1 and index number b1 by NK clock cycles to obtain the final data frame A2 and index number b2. Then, data frame A2 is compared with the constant false alarm threshold value V. T Timing alignment, and finally, the constant false alarm threshold V. T The data frame A2 is input into the comparator, and the output is the index number and data frame corresponding to the frequency point that is greater than the constant false alarm threshold.

[0065] The other parts of this embodiment are the same as those in Embodiment 1 above, so they will not be described again.

[0066] Example 3:

[0067] This embodiment is based on any one of the above embodiments 1-2, such as Figure 1 , Figure 2 , Figure 3 As shown, a specific embodiment will be described in detail.

[0068] This embodiment utilizes the received communication signal between the UAV and the remote controller, typically a frequency-hopping multi-bandwidth signal. Based on an FPGA parallel computing architecture, it performs spectral energy accumulation after 75% time overlap (FFT operation has a coherent accumulation effect on the signal), improving the sensitivity of the digital receiver and the system's time resolution. Building upon this, the algorithm for calculating the constant false alarm rate threshold using segmented averaging, proposed in this embodiment, is employed for digital signal processing. This allows for rapid detection of the UAV's noise floor threshold, offering advantages such as ease of calculation, convenient implementation, and high target detection probability.

[0069] In this embodiment, the UAV data sampled by the ADC is first accumulated using a 75% time overlap FFT spectrum in the FPGA, and then a segmented mean constant false alarm rate algorithm is used for dynamic threshold detection. When obtaining the dynamic threshold, the 75% time overlap FFT spectrum signal is sorted and detected by segmented mean, which improves the accuracy of noise threshold acquisition and the sensitivity of signal detection, and realizes target detection at a greater distance.

[0070] This embodiment uses a segmented mean constant false alarm rate (CFAR) detection method based on FFT spectrum accumulation. When the FPGA performs 75% time overlap FFT spectrum accumulation on the UAV data obtained from AD sampling, the software implementation employs four FFT serial IP cores. The FFT modules operate in a pipelined manner with a pipeline depth of 2048 points, and there are only 512 unique data points between adjacent data frames from different FFT modules. Then, a segmented mean CFAR threshold algorithm is directly applied for digital signal processing. The mean with the smallest variance among the four minimum means after sorting the segmented means is taken as the noise floor Z. A noise factor α is then introduced to obtain the final CFAR threshold V. T .

[0071] The structural principle block diagram of this embodiment is as follows: Figure 1 As shown, the formula used when calculating the constant false alarm threshold is:

[0072] (1)

[0073] Where α is the noise factor, and the extracted constant false alarm threshold Z and corresponding segment index are... The expression is as follows:

[0074] (2)

[0075] in, These are the indices of the four minimum values ​​after sorting the mean from smallest to largest. The operator is the variance of the segment corresponding to the four smallest means. This indicates obtaining the indices corresponding to the four minimum variances. That is, the number of the segment corresponding to the constant false alarm threshold.

[0076] In the FPGA, the UAV data sampled by the ADC is first subjected to FFT spectrum accumulation with 75% time overlap, and then dynamic threshold detection is performed using the segmented mean constant false alarm rate algorithm. The specific steps include:

[0077] Step S1: 75% time overlap, the 75% time overlap data frame pipeline consisting of 4 serial input data is as follows Figure 2 , Figure 3 As shown in the diagram, this method employs four FFT serial IP cores, with each FFT module operating in parallel in a pipelined manner. The pipeline depth is 2048 points, and the input data frame interval between adjacent FFT modules is set to 512 points of non-repeating data, achieving 75% time overlap of the FFT module input data. Then, the 75% time-overlapping data frames are divided into four paths and simultaneously subjected to FFT transformation to obtain their respective spectral data data1, data2, data3, and S4. Simultaneously, the FFT IP core outputs the corresponding index numbers k1, k2, k3, and x4 for each frequency point.

[0078] Step S2: Spectrum Accumulation: The four serial FFT spectrum data obtained in Step S1 are first aligned in time and then subjected to modulo (amplitude) calculation. Then, the data is accumulated at four points according to the frequency positions represented by the FFT index numbers. This yields the data frame X and index number I after 75% time overlap of the FFT spectrum accumulation. The specific steps are as follows:

[0079] Step S21: Delay the data output from FFT module 1 by 1036 clock cycles, the data output from FFT module 2 by 1024 clock cycles, and the data output from FFT module 3 by 512 clock cycles. This yields four time-aligned serial data frames S1, S2, S3, S4 and their corresponding index numbers x1, x2, x3, x4. Since the outputs of the four FFT modules have undergone time alignment, the output index numbers x1, x2, x3, x4 are in the same sequence. One of these index numbers, x1, will then be used for subsequent pipelined processing.

[0080] Step S22: Perform modulo operation on the four time-aligned serial data frames S1, S2, S3, and S4.

[0081] Step S23: Input the modulo-calculated data from the previous step into the adder, and perform a 4-point summation according to the frequency positions represented by the FFT index number. This yields the data frame X after 75% time overlap with the FFT spectrum. Simultaneously, the index number x1 needs to be delayed by 1 clock cycle to maintain timing alignment with the data frame X, thus obtaining the final output index number I.

[0082] Additionally, the number of accumulations can be set according to the actual application scenario, generally 128 frames. For long-distance drone signals, a larger value can be selected to improve signal detection sensitivity. Finally, the accumulated FFT data frame X and index number I are sent to the segmented mean constant false alarm rate threshold algorithm processing module for dynamic threshold detection.

[0083] Step S3: Calculation of the segmented mean constant false alarm rate threshold: After obtaining the serial data frame X and index number I after FFT spectrum accumulation, the threshold is calculated based on the set number of segments N, the number of segment points K (K=2048 / N), and the noise factor. ;refer to Figure 1 The design of the pipelined architecture is illustrated in the block diagram, and the final constant false alarm threshold is calculated. The algorithm implementation includes the following specific steps:

[0084] Step S31: Calculate the segment mean: The data frame X after FFT accumulation is divided into N segments according to the set number of segments N, the number of segment points K, and the index number I, in chronological order. The first segment after division is... (The corresponding index range is 0~K-1), the second sequence is (The corresponding index range is K~2K-1), and so on, the Nth segment of the sequence is... (The corresponding index range is {N-1}K~NK-1). Then, according to the starting position i marked by index number I, calculate the average value of each segment according to formula (3) and output the segment number n.

[0085] (3)

[0086] Where N and K are known, mean(n) represents the mean of the nth segment. ;

[0087] Because the mean operation uses a pipelined process, only one adder is needed. The final delay time is determined by the number of segments K, so it is only delayed by K clock cycles.

[0088] Step S32: Mean Value Storage: Store the segmented mean values ​​in the FPGA's RAM. The current segment number n is used as the write address of RAM port A, and mean(n) is used as the write data for RAM port A. The mean values ​​are stored in RAM in chronological order for use in step S36 to extract the constant false alarm rate threshold.

[0089] Step S33: Mean Sort: Sort the segmented means in ascending order using the bubble sort algorithm, and finally output the four smallest mean indices after sorting. When the last average of data frame X is output, the four minimum averages can be obtained.

[0090] Step S34: Calculate the segmented variance: Delay data frame X and index number I by K beats to obtain data frame A1 and index number b1, keeping them aligned with the current segment mean time sequence. Then, substitute data frame A1 into the variance formula (4) according to the lower table position i indicated by the current index number b1 to calculate the segmented variance. :

[0091] (4)

[0092] Step S35: Variance sorting: Combining the four minimum mean indices obtained in step S33 The variance corresponding to the four smallest piecewise means after sorting the piecewise means is denoted as . Then, according to formula (5), the indices of the four minimum variance values ​​can be obtained. .

[0093] (5)

[0094] Step S36: Obtain the constant false alarm threshold Z: Based on the calculation result in formula (5), This serves as the read address for the RAM in the FPGA. Then, the noise floor can be obtained according to formula (6). Ultimately, this was achieved by introducing a noise factor. This will give you the final constant false alarm threshold. .

[0095] (6)

[0096] Step S37: Delay data frame A1 and index number b1 by NK clock cycles to obtain the final data frame A2 and index number b2. This ensures that the constant false alarm threshold (CFAT) obtained in the previous step is consistent with the constant false alarm threshold (CFAT). Timing alignment.

[0097] Then the constant false alarm threshold The data is sent to the comparator along with the serial data frame A2. After comparison and judgment, if the value is greater than the threshold, the current frequency index number and valid data are output.

[0098] This embodiment is based on an FPGA parallel computing architecture and uses a 75% time overlap FFT transform on the AD sampled UAV data. Compared with the traditional method, it improves the system's signal sensitivity and time resolution. Based on the FFT transform, energy is accumulated in the spectrum, and the constant false alarm rate threshold is extracted using the segmented mean algorithm proposed in this embodiment. Because the signal is accumulated multiple times, it can avoid the problem of large noise variance fluctuations caused by non-uniform clutter such as GPS navigation, mobile communication signals, and Wi-Fi signals in the environment. Compared with the traditional CFAR detection algorithm, it improves the accuracy of target detection probability. The constant false alarm rate algorithm provided in this embodiment adopts a pipelined design, which is easy to implement on FPGA and consumes less resources.

[0099] The other parts of this embodiment are the same as any one of the embodiments 1-2 above, so they will not be described again.

[0100] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications or equivalent changes made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A segmented mean constant false alarm rate (CFAR) detection method based on FFT accumulation, the method specifically includes the following steps: Step 1: Perform a 75% time overlap FFT transform on the acquired UAV digital signal; Step 2: Perform spectrum accumulation processing on the UAV digital signal after 75% time overlap FFT transformation; Step 3: Use the segmented mean constant false alarm rate (CFAR) algorithm to perform dynamic threshold detection and extract the CFAR threshold of the accumulated UAV digital signal; Step 4: Calculate the final constant false alarm rate (CFAR) threshold V based on the CFAR threshold. T ; Step 5: Compare the data frame at the current frequency with the constant false alarm threshold V. T The size of the output is greater than the constant false alarm threshold V. T The frequency index number and data frame; The specific operation of step 2 is as follows: the four FFT UAV digital signals are time-aligned and modulo operation is performed. According to the position of the frequency point corresponding to the index number, the data frames of the four FFT UAV digital signals are accumulated and summed to obtain the data frame X and index number I of the accumulated FFT UAV digital signal. Step 3 specifically includes the following steps: Step 31: Divide the data frame X into N segments according to the set number of segments N, number of segment points K, and index number I, in chronological order. Step 32: Calculate the average value of N data frames based on the starting position i marked by index number I, and record the segment number n corresponding to each data frame; Step 33: Use the segment number n as the write address of RAM port A, use the average value of the segments as the write data of RAM port A, and store the average value of the segments into RAM according to the time order. Step 34: Use the bubble sort method to sort the segmented means in ascending order to obtain the indices m0, m1, m2, and m3 of the four smallest segmented means; Step 35: Delay data frame X and index number I by K points to obtain data frame A1 and index number b1, and align them with the time sequence of the segment mean of the current segment. Step 36: Calculate the piecewise variance based on the subscript position i corresponding to index number b1, and calculate the index n0 of the smallest piecewise variance; Step 37: Use the index n0 of the smallest segment variance as the read address of RAM to calculate the constant false alarm threshold.

2. The segmented mean constant false alarm rate detection method based on FFT accumulation as described in claim 1, characterized in that, Step 1 specifically includes the following steps: Step 11: Divide the UAV digital signal into four parallel UAV digital signals; Step 12: Based on the pipeline depth of the UAV digital signal data frame, delay the four UAV digital signals sequentially by one-quarter of the pipeline depth to obtain four UAV digital signals with 75% time overlap. Step 13: Simultaneously perform FFT transformation on the four 75% time overlap UAV digital signals to obtain four FFT UAV digital signals and four data frames and four index numbers corresponding to the four FFT UAV digital signals.

3. The segmented mean constant false alarm rate detection method based on FFT accumulation as described in claim 1, characterized in that, The specific operation of step 4 is as follows: using the constant false alarm rate threshold as the noise floor Z, introducing a noise factor α, and obtaining the final constant false alarm rate threshold V. T .

4. The segmented mean constant false alarm rate detection method based on FFT accumulation as described in claim 3, characterized in that, The specific operation of step 5 is as follows: delay data frame A1 and index number b1 simultaneously by NK clock cycles to obtain the final data frame A2 and index number b2, and then compare data frame A2 with the constant false alarm threshold value V. T Timing alignment, and finally, the constant false alarm threshold V. T The data frame A2 is input into the comparator, and the output is the index number and data frame corresponding to the frequency point that is greater than the constant false alarm threshold.

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