Adaptive noise floor energy detection method and system based on FFT spectrum accumulation

Abstract

The invention relates to an adaptive noise floor energy detection method and system based on FFT frequency spectrum accumulation, and the method comprises the steps: carrying out the FFT frequency spectrum accumulation of an electromagnetic signal sampled by an ADC in an FPGA, and carrying out the channel division of a broadband frequency spectrum into sub-bands; counting and estimating the top and the bottom of the noise by sub-bands one by one; carrying out median filtering processing, subtraction and modular operation according to the obtained data to obtain optimal values of the energy bottom and the noise energy fluctuation range of each channel, carrying out summation to obtain a noise floor of each sub-band, and carrying out median filtering once to obtain a final noise floor value of each sub-band; and a noise factor is introduced to obtain a final adaptive constant false alarm threshold, and adaptive detection of noise floor energy is realized through the threshold. According to the invention, a parallel operation architecture based on the FPGA is adopted, so that the method is simple in calculation and high in sensitivity, and has the capability of detecting each sub-band signal at the same time.

Description

Technical Field

[0001] The present invention relates to the field of signal processing, and in particular to an adaptive noise floor energy detection method and system based on FFT spectrum accumulation. Background Art

[0002] Usually, signal reconnaissance in electronic countermeasures refers to the feature extraction of bandwidth, frequency and other information of the enemy's complex electromagnetic radiation signals. At this time, a digital receiver is required to perform accurate threshold detection on broadband signals under low signal-to-noise ratio to adapt to the reconnaissance of target signals under low signal-to-noise ratio.

[0003] Currently, mainstream adaptive threshold detection algorithms primarily employ dynamic threshold detection algorithms in the time or frequency domains. Time-domain statistics are primarily based on the time-domain cumulative detection method of noise variance. By statistically analyzing the mean and variance of noise over a period of time, the corresponding adaptive threshold is estimated based on the noise distribution. However, because time-domain detection lacks the FFT cumulative gain, the signal-to-noise sensitivity is generally low and it cannot adapt to signals arriving simultaneously at multiple frequencies. Frequency-domain detection typically uses the amplitude spectrum after FFT to estimate the noise floor mean, then designs a fixed threshold based on the statistically calculated value. Although this method is commonly used, it has significant disadvantages: when the signal is small or the noise floor is uneven, signals are prone to missed detection and false detection. Furthermore, this method has limited improvement in signal-to-noise ratio, as it does not perform noise floor statistics for each individual signal, resulting in a limited detection signal-to-noise ratio. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an adaptive noise floor energy detection method and system based on FFT spectrum accumulation, which solves the shortcomings of the prior art.

[0005] The object of the present invention is achieved by the following technical solution: an adaptive noise floor energy detection method based on FFT spectrum accumulation, the detection method comprising: S1. In the FPGA, the electromagnetic signal sampled by the ADC is first subjected to FFT spectrum accumulation, and then the broadband spectrum is divided into various sub-bands by channel; S2, count and estimate the top and bottom of the noise for each sub-band; S3. Perform median filtering and subtraction and modulo operation on the obtained broadband noise bottom data and noise top data to obtain the optimal values ​​of the energy bottom and noise energy fluctuation range of each channel; S4. Sum the obtained optimal values ​​of the energy floor of each channel and the noise energy fluctuation range to obtain the noise floor of each sub-band, and perform median filtering again to obtain the final noise floor value of each sub-band; S5. Introducing a noise factor to obtain a final adaptive constant false alarm threshold, and implementing adaptive detection of noise floor energy through the threshold.

[0006] The S1 specifically includes the following contents: An FFT module is used to perform parallel operations in a pipeline manner on the FPGA. The FFT spectrum amplitude is accumulated according to the number of spectrum cache frames, and the number of FFT points is set to 256 points. The accumulated data is divided into N segments according to FFT index 0 to N×M-1, and sent to the buffer of subband 0, subband 1, ..., subband N-1 in a data stream manner. Each subband is amplitude smoothed at its own index position according to the data volume, thereby reducing the fluctuation of noise energy.

[0007] The S2 specifically includes the following contents: Assume N = 16. Based on the obtained data frame point number of 256, set the FFT index range of channel 1 to 16, the FFT index range of channel 2 to 17 to 32, ..., and the index range of channel 16 to 241 to 256. The second largest value of the amplitude in each channel is taken as the top of the current sub-band energy, and the second smallest value of the amplitude in each channel is taken as the bottom of the current sub-band energy.

[0008] The S3 specifically includes the following contents: Based on the obtained broadband noise floor data, median filtering is performed in descending order of the sub-bands. The median value in the sliding window is used to replace the value of the current point, thereby effectively suppressing the impulse noise and obtaining the final energy floor of each channel. A difference and modulo operation is performed based on the obtained broadband noise bottom data and noise top data, and a histogram is performed as an interval unit according to the set amplitude value, and the second largest value is selected as the optimal value of the noise energy fluctuation range.

[0009] An adaptive noise floor energy detection system based on FFT spectrum accumulation, the system includes a spectrum accumulation and division module, a noise floor and top estimation module, a median filter and modulo operation module, a summation filter module and an adaptive threshold detection module; The spectrum accumulation and division module: first performs FFT spectrum accumulation on the electromagnetic signal sampled by the ADC in the FPGA, and then divides the broadband spectrum into various sub-bands; The noise floor and top estimation module is used to count and estimate the top and bottom of the noise in each sub-band; The median filtering and modulo operation module performs median filtering and difference and modulo operation on the obtained broadband noise bottom data and noise top data to obtain the optimal value of each channel energy bottom and noise energy fluctuation range; The summation filtering module: sums the obtained optimal values ​​of the energy floor of each channel and the noise energy fluctuation range to obtain the noise floor of each sub-band, and then performs a median filter to obtain the final noise floor value of each sub-band; The adaptive threshold detection module introduces a noise factor to obtain a final adaptive constant false alarm threshold, and realizes adaptive detection of noise floor energy through the threshold.

[0010] The spectrum accumulation and division module specifically includes the following contents: An FFT module is used to perform parallel operations in a pipeline manner on the FPGA. The FFT spectrum amplitude is accumulated according to the number of spectrum cache frames, and the number of FFT points is set to 256 points. The accumulated data is divided into N segments according to FFT index 0 to N×M-1, and sent to the buffer of subband 0, subband 1, ..., subband N-1 in a data stream manner. Each subband is amplitude smoothed at its own index position according to the data volume, thereby reducing the fluctuation of noise energy.

[0011] The noise floor and top estimation module specifically includes the following contents: Assume N = 16. Based on the obtained data frame point number of 256, set the FFT index range of channel 1 to 16, the FFT index range of channel 2 to 17 to 32, ..., and the index range of channel 16 to 241 to 256. The second largest value of the amplitude in each channel is taken as the top of the current sub-band energy, and the second smallest value of the amplitude in each channel is taken as the bottom of the current sub-band energy.

[0012] The median filtering and modulo operation module specifically includes the following contents: Based on the obtained broadband noise floor data, median filtering is performed in descending order of the sub-bands. The median value in the sliding window is used to replace the value of the current point, thereby effectively suppressing the impulse noise and obtaining the final energy floor of each channel. A difference and modulo operation is performed based on the obtained broadband noise bottom data and noise top data, and a histogram is performed as an interval unit according to the set amplitude value, and the second largest value is selected as the optimal value of the noise energy fluctuation range.

[0013] The present invention has the following advantages: an adaptive noise floor energy detection method and system based on FFT spectrum accumulation, adopts a parallel computing architecture based on FPGA, has simple calculation, high sensitivity, and the ability to simultaneously detect each sub-band signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Schematic diagram of the process of the present invention; Figure 2This is a flow chart of sub-band smoothed data after FFT accumulation according to the present invention; Figure 3 Detailed spectrum diagram divided into subbands Figure 4 The top and bottom diagrams are for each sub-channel energy; Figure 5 The bottom diagram of each channel energy after median filtering; Figure 6 This is the histogram statistical result graph; Figure 7 It is the noise floor curve after median filtering; Figure 8 Schematic diagram of adaptive constant false alarm threshold; Figure 9 This is the detection effect diagram of the adaptive noise floor energy detection algorithm based on FFT spectrum accumulation; Figure 10 This is the effect diagram of traditional FFT-based frequency domain broadband noise floor detection. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application provided below in conjunction with the drawings is not intended to limit the scope of protection of the present application for which protection is claimed, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application. The present invention is further described below in conjunction with the drawings.

[0016] like Figure 1 As shown, the present invention specifically relates to an adaptive noise energy detection method based on FFT spectrum accumulation. In an FPGA, the FFT spectrum accumulation of the digital signal broadband signal after AD sampling is performed, so that the signal components can be coherently accumulated after the FFT operation result, thereby improving the sensitivity of the digital receiver and the system time resolution. Then, the signal is evenly segmented according to the number of FFT points corresponding to the channel sub-band bandwidth, thereby improving the detection sensitivity of each sub-band signal and having a strong ability to adapt to complex electromagnetic environments. Specifically, the method includes the following contents: Step 1: If Figure 2As shown, FFT spectrum accumulation uses a single FFT module to perform parallel computations in a pipelined manner on the FPGA, with the FFT point count set to 256. FFT spectrum amplitude accumulation is then performed based on the number of spectrum buffer frames. The accumulated data is divided into N segments (N×M-1) according to FFT indexes 0 to N×M-1 (satisfying N×M=256). This data is then streamed to the buffers for subband 0, subband 1, and subband N-1. Each subband performs amplitude smoothing at its respective index position in a streamed manner to reduce noise energy fluctuations.

[0017] Step 2: Count and estimate the top and bottom of the noise for each subband. The specific process is as follows: Here, the broadband spectrum is divided into 16 segments (N=16), that is, the number of points in each segment M=16. According to the first step of the processing flow, the number of data frame points is 256 points, of which the FFT index range of channel 1 is 1~16, the FFT index range of channel 2 is 17~32, and so on. The FFT index range of channel 16 is 241~256. The channel division is as follows Figure 3 shown.

[0018] Then, the second largest value of the amplitude in each channel is taken as the top of the current sub-band energy, such as Figure 4 As shown in the top curve, the second minimum amplitude in each channel is taken as the bottom of the current sub-band energy, such as Figure 4 Shown in the bottom curve.

[0019] Step 3: Perform a median filter based on the broadband noise floor data obtained above (a total of M points) in the order of sub-band from low to high. The core principle is to take the median value in the sliding window instead of the value of the current point, thereby effectively suppressing the impulse noise and obtaining the final energy floor of each channel, such as Figure 5 As shown in the middle curve, it can be found that the noise envelope is effectively smoothed.

[0020] Step 4: Based on the broadband noise bottom data (a total of M points) and the noise top data (a total of M points) obtained in step 2, perform a modulo operation and then perform a histogram statistics based on the amplitude with 6dB as the interval unit to obtain the following: Figure 6 The statistical results shown are based on the above sample data. Here we select the second largest value of 20dB as the optimal value of the noise energy fluctuation range.

[0021] Step 5: Sum the optimal values ​​of the energy floor and noise energy fluctuation range of each channel to obtain the noise floor of each sub-band. In order to avoid large noise burr signals in each channel, a median filter is performed after the summation to obtain the final noise floor value of each sub-band. , that is, Figure 7 The adaptive noise floor curve is shown.

[0022] From above Figure 7 It can be seen that compared with Figure 5 The median noise floor curve (without median filtering) is obviously smoother, and it is found that the noise floor value estimated in the channel where the valid signal is located is slightly higher than that in other channels, showing the characteristics of adaptive threshold.

[0023] Step 6: Introducing noise factors Get the final constant false alarm threshold ,in, .

[0024] Since the present invention adopts spectrum accumulation technology, the signal components have been coherently accumulated, so in order to avoid the false alarm problem, the noise factor Generally, 3 is selected to ensure a margin of 9.5424 dB, and the adaptive constant false alarm threshold is finally obtained as Figure 8 As shown above Figure 8 It can be seen that the noise floor is convex at the center of the signal and shows a slowly decreasing trend outside the IF band (the filter bandwidth stopband). However, it is important to note that because the adaptive noise floor uses median filtering technology, the final adaptive constant false alarm threshold does not drop sharply with the IF filter bandwidth stopband, ensuring effective filtering of out-of-band spurious signals. At the same time, the adaptive noise floor statistics process does not lose out-of-band noise information, which is well suited to the Gaussian mathematical model of broadband noise.

[0025] From Figure 9 As shown, the adaptive noise floor energy detection algorithm based on FFT spectrum accumulation provided by this patent can effectively identify changes in the noise floor due to its implementation based on the FFT parallel architecture. At the time near the subscript 1400 point, the adaptive threshold calculated by this patent can adapt to the changes in the noise floor.

[0026] and Figure 10 The traditional frequency-domain broadband noise floor detection algorithm shown is mainly based on the statistics of the noise floor threshold over a period of time. The designed threshold is a constant threshold estimated under the entire bandwidth. No separate statistics are performed based on the noise floor within the sub-band. As a result, it is impossible to detect the signal more accurately within the entire broadband signal detection. Therefore, this algorithm is significantly superior to the traditional FFT-based broadband noise floor detection algorithm.

[0027] The foregoing description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein and should not be construed as excluding other embodiments. Rather, the present invention is capable of various other combinations, modifications, and improvements, and is capable of modifications within the scope of the concepts described herein, through the above teachings, or through techniques or knowledge in the relevant fields. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention are intended to be within the scope of the appended claims.

Claims

1. An adaptive noise floor energy detection method based on FFT spectrum accumulation, characterized by: The detection method comprises: S1. In the FPGA, the electromagnetic signal sampled by the ADC is first subjected to FFT spectrum accumulation, and then the broadband spectrum is divided into various sub-bands by channel; S2, count and estimate the top and bottom of the noise for each sub-band; S3. Perform median filtering and subtraction and modulo operation on the obtained broadband noise bottom data and noise top data to obtain the optimal values ​​of the energy bottom and noise energy fluctuation range of each channel; S4. Sum the obtained optimal values ​​of the energy floor of each channel and the noise energy fluctuation range to obtain the noise floor of each sub-band, and perform median filtering again to obtain the final noise floor value of each sub-band; S5. Introducing a noise factor to obtain a final adaptive constant false alarm threshold, and implementing adaptive detection of noise floor energy through the threshold.

2. The adaptive noise floor energy detection method based on FFT spectrum accumulation according to claim 1, characterized in that: The S1 specifically includes the following contents: An FFT module is used to perform parallel operations in a pipeline manner on the FPGA. The FFT spectrum amplitude is accumulated according to the number of spectrum cache frames, and the number of FFT points is set to 256 points. The accumulated data is divided into N segments according to FFT index 0 to N×M-1, and sent to the buffer of subband 0, subband 1, ..., subband N-1 in a data stream manner. Each subband is amplitude smoothed at its own index position according to the data volume, thereby reducing the fluctuation of noise energy.

3. The adaptive noise floor energy detection method based on FFT spectrum accumulation according to claim 2, characterized in that: The S2 specifically includes the following contents: Assume N = 16. Based on the obtained data frame point number of 256, set the FFT index range of channel 1 to 16, the FFT index range of channel 2 to 17 to 32, ..., and the index range of channel 16 to 241 to 256. The second largest value of the amplitude in each channel is taken as the top of the current sub-band energy, and the second smallest value of the amplitude in each channel is taken as the bottom of the current sub-band energy.

4. The adaptive noise floor energy detection method based on FFT spectrum accumulation according to claim 1, characterized in that: The S3 specifically includes the following contents: Based on the obtained broadband noise floor data, median filtering is performed in descending order of the sub-bands. The median value in the sliding window is used to replace the value of the current point, thereby effectively suppressing the impulse noise and obtaining the final energy floor of each channel. A difference and modulo operation is performed based on the obtained broadband noise bottom data and noise top data, and a histogram is performed as an interval unit according to the set amplitude value, and the second largest value is selected as the optimal value of the noise energy fluctuation range.

5. An adaptive noise floor energy detection system based on FFT spectrum accumulation, characterized by: The system includes a spectrum accumulation and division module, a noise floor and top estimation module, a median filter and modulo operation module, a summation filter module and an adaptive threshold detection module; The spectrum accumulation and division module: first performs FFT spectrum accumulation on the electromagnetic signal sampled by the ADC in the FPGA, and then divides the broadband spectrum into various sub-bands; The noise floor and top estimation module is used to count and estimate the top and bottom of the noise in each sub-band; The median filtering and modulo operation module performs median filtering and difference and modulo operation on the obtained broadband noise bottom data and noise top data to obtain the optimal value of each channel energy bottom and noise energy fluctuation range; The summation filtering module: sums the obtained optimal values ​​of the energy floor of each channel and the noise energy fluctuation range to obtain the noise floor of each sub-band, and then performs a median filter to obtain the final noise floor value of each sub-band; The adaptive threshold detection module introduces a noise factor to obtain a final adaptive constant false alarm threshold, and realizes adaptive detection of noise floor energy through the threshold.

6. The adaptive noise floor energy detection system based on FFT spectrum accumulation according to claim 5, characterized in that: The spectrum accumulation and division module specifically includes the following contents: An FFT module is used to perform parallel operations in a pipeline manner on the FPGA. The FFT spectrum amplitude is accumulated according to the number of spectrum cache frames, and the number of FFT points is set to 256 points. The accumulated data is divided into N segments according to FFT index 0 to N×M-1, and sent to the buffer of subband 0, subband 1, ..., subband N-1 in a data stream manner. Each subband is amplitude smoothed at its own index position according to the data volume, thereby reducing the fluctuation of noise energy.

7. The adaptive noise floor energy detection system based on FFT spectrum accumulation according to claim 6, characterized in that: The noise floor and top estimation module specifically includes the following contents: Assume N = 16. Based on the obtained data frame point number of 256, set the FFT index range of channel 1 to 16, the FFT index range of channel 2 to 17 to 32, ..., and the index range of channel 16 to 241 to 256. The second largest value of the amplitude in each channel is taken as the top of the current sub-band energy, and the second smallest value of the amplitude in each channel is taken as the bottom of the current sub-band energy.

8. The adaptive noise floor energy detection system based on FFT spectrum accumulation according to claim 5, characterized in that: The median filtering and modulo operation module specifically includes the following contents: Based on the obtained broadband noise floor data, median filtering is performed in descending order of the sub-bands. The median value in the sliding window is used to replace the value of the current point, thereby effectively suppressing the impulse noise and obtaining the final energy floor of each channel. A difference and modulo operation is performed based on the obtained broadband noise bottom data and noise top data, and a histogram is performed as an interval unit according to the set amplitude value, and the second largest value is selected as the optimal value of the noise energy fluctuation range.

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