Multi-phase filtering channelized ultra-wideband signal detection and fusion method and system

Abstract

The invention belongs to the technical field of digital communication, and particularly relates to a multi-phase filtering channelized ultra-wideband signal detection and fusion method and system.The multi-phase filtering channelized ultra-wideband signal detection and fusion method comprises the steps that channelization is conducted, specifically, a receiving bandwidth is evenly divided into K sub-channels, 50% overlapping is adopted between every two adjacent sub-channels, the method comprises the following steps: designing an appropriate prototype filter according to frequency response, determining a multiphase filter coefficient according to the number of sub-channels and the prototype filter, carrying out D-time extraction at the front end of the multiphase filter, enabling the extracted signal to enter the multiphase filter for filtering, and then carrying out fast Fourier inverse transform on K points output each time to obtain output signals of K sub-channels, time domain amplitude detection: acquiring arrival time and pulse width information of the signal in a time domain; according to the method, the problems of missing detection and feature detection errors of multiple time domain overlapped signals in a single channel are solved, and accurate broadband signal parameters are obtained by using the corresponding relationship between the signal frequency domain and the channel passband.

Description

Technical Field

[0001] The present invention belongs to the technical field of digital communication, and particularly relates to a method and system for multi-phase filtering channelization of ultra-wideband signal detection and fusion. Background Technique

[0002] The modern battlefield electromagnetic environment is becoming increasingly complex. Electronic warfare receivers need to have high performance such as high sensitivity, large dynamic range, wide instantaneous bandwidth, and the ability to quickly intercept information and perform real-time processing. Digital channelized receivers combine the advantages of channelization and digitization. They not only have high comprehensive performance in terms of frequency range, bandwidth range, and dynamic range like channelized receivers, but also can improve the stability and flexibility of the receiver through digitization, ensure the gain consistency of each channel, reduce the influence of non-linear distortion usually associated with analog devices, and digital receivers also have the advantages of high integration, small size, and low power consumption. Digital channelized receivers have become an important development direction for current electronic warfare receivers.

[0003] Existing digital channelization methods mainly include: channelization methods based on digital down-conversion, methods based on polyphase decomposition filters and fast Fourier transforms, and frequency domain filtering methods. For a channelizer based on the structure of a polyphase filter bank (PFB), the sub-channels of its polyphase filtering are evenly spaced in the frequency domain and the parameters are fixed, and there is an overlap in the transition band between adjacent sub-channels. However, existing polyphase filtering channelization methods have problems in that it is difficult to identify signal characteristics when processing cross-channel broadband signals, and when there are multiple time-domain overlapping signals in a single channel, there are prone to missed detections and incorrect feature detections.

[0004] Therefore, it is necessary to design a polyphase filtering channelization method to improve the above problems. Summary of the Invention

[0005] To solve the problems of the existing technology, the present invention provides a method and system for multi-phase filtering channelization of ultra-wideband signal detection and fusion, including the following steps:

[0006] Channelization: evenly divide the received bandwidth into K sub-channels, with 50% overlap between adjacent sub-channels. Design a suitable prototype filter according to the frequency response, determine the polyphase filter coefficients according to the number of sub-channels and the prototype filter, perform D-fold decimation at the front end of the polyphase filter, and the decimated signal enters the polyphase filter for filtering, and then perform an inverse fast Fourier transform on the K points output each time to obtain the output signals of the K sub-channels;

[0007] Time-domain amplitude detection: perform envelope smoothing on the signals of each channel to reduce noise disturbance, then perform amplitude detection, obtain the arrival time and pulse width information of the signals in the time domain, and perform a fast Fourier transform on the detected time-domain signals to obtain the signal spectrum;

[0008] Spectrum detection: Smooth the signal spectrum to reduce the influence of noise, and use the amplitude to determine whether there are multiple signals; if there is only a single signal, estimate its starting frequency and ending frequency; if there are multiple signals, estimate the starting frequency and ending frequency of each signal respectively, then take the corresponding frequency-domain signal for inverse fast Fourier transform and perform time-domain feature detection again to distinguish the arrival time and pulse width of multiple signals.

[0009] Parameter fusion: According to the starting frequency and ending frequency of the signal, determine whether the signal falls within the channel passband or the transition band. If all signals fall within the transition band, discard the signal; if all signals fall within the channel passband, merge the parameters and directly store the current signal parameters; if the signal falls within both the channel passband and the transition band, search for a signal that overlaps both in time domain and frequency domain with the current signal in the next adjacent channel, and repeat this process for the qualified signals until a signal with an ending frequency within the channel passband is found, and fuse all the signal features in this process to obtain cross-channel signal parameters.

[0010] Furthermore, in the channelization step, design a suitable prototype filter through the Chebyshev approximation algorithm according to the frequency response. After performing k-channel polyphase decomposition on the prototype low-pass filter and inserting zeros by a factor of 2, obtain the tap coefficients of each of the k polyphase filter branches.

[0011] Furthermore, in the time-domain amplitude detection step, take the channel with the minimum energy as the silent channel, take the signal power value of the silent channel plus the threshold factor a as the time-domain noise threshold, perform time-domain detection according to the time-domain noise threshold, and filter out signals with a pulse width less than 0.1 us as glitches.

[0012] Furthermore, perform a fast Fourier transform on the detected time-domain signal to obtain the signal spectrum, specifically including: intercept the corresponding signal in the time domain and take the corresponding data of the signal of the silent channel respectively for N-point fast Fourier transform, the number of points of the fast Fourier transform is the signal length N, and obtain the signal spectrum by taking the amplitude of the time-domain signal.

[0013] Furthermore, in the spectrum detection step, estimate the noise power of the signal of the silent channel within the channel passband, add the noise power value plus the threshold factor β as the frequency-domain noise detection threshold, and filter out glitches less than 5 points.

[0014] Furthermore, in the parameter fusion step, after detecting K channels, sort them in ascending order of channel number and then perform parameter merging. The starting frequency of the first signal to be merged after sorting is not in the low-side transition band of the corresponding channel.

[0015] Further, the method further includes: in the time-domain amplitude detection step, if it is detected that multiple signals overlap in time domain within a single channel, spectrum detection is performed on the multiple signals according to the frequency-domain noise detection threshold, and the start frequency and end frequency of each signal are estimated respectively, so as to distinguish multiple signals that overlap in time domain within the single channel.

[0016] Further, in the application scenario of the method, the sampling rate is 2.4 GHz, 2.4 GHz is evenly divided into K = 32 channels according to the channel width of 75 MHz, 50% overlap is adopted between adjacent channels, and the received signal decimation factor D = 16.

[0017] Further, the method further includes: in the parameter fusion step, if a signal falls within both the channel passband and the transition band, search for a signal that overlaps with the current signal in both time domain and frequency domain in the next adjacent channel, and repeat this process for the qualified signals until a signal with an end frequency within the channel passband is found, and fuse all the signal characteristics in this process to obtain the cross-channel signal parameters.

[0018] A multi-phase filtering channelized ultra-wideband signal parameter detection system includes:

[0019] A channelization module, configured to evenly divide the received bandwidth into K sub-channels, adopt 50% overlap between adjacent sub-channels, design a suitable prototype filter according to the frequency response, determine the multi-phase filter coefficients according to the number of sub-channels and the prototype filter, perform D-fold decimation at the front end of the multi-phase filter, the decimated signal enters the multi-phase filter for filtering, and then perform an inverse fast Fourier transform on the K points output each time to obtain the output signals of the K sub-channels;

[0020] A time-domain amplitude detection module, configured to smooth the envelope of the signal of each channel to reduce noise disturbance, then perform amplitude detection, obtain the arrival time and pulse width information of the signal in the time domain, and perform a fast Fourier transform on the detected time-domain signal to obtain the signal spectrum;

[0021] A spectrum detection module, configured to smooth the signal spectrum to reduce the influence of noise, and use the amplitude to determine whether there are multiple signals; if there is only a single signal, estimate its start frequency and end frequency; if there are multiple signals, estimate their start frequencies and end frequencies respectively, then perform an inverse fast Fourier transform on the corresponding frequency-domain signals and perform time-domain feature detection again to distinguish the arrival times and pulse widths of the multiple signals;

[0022] The parameter fusion module is used to determine whether a signal falls within the channel passband or the transition band based on the starting frequency and ending frequency of the signal. If the signal entirely falls within the transition band, the signal is discarded; if the signal entirely falls within the channel passband, the parameters are merged and the current signal parameters are directly stored; if the signal falls within both the channel passband and the transition band, a signal that overlaps with the current signal both in time domain and frequency domain is searched for in the next adjacent channel, and this process is repeated for the eligible signals until a signal with an ending frequency within the channel passband is found, and the signal characteristics of all signals in this process are fused with each other to obtain cross-channel signal parameters.

[0023] Advantages of the present invention:

[0024] The multi-phase filtering channelization signal detection and parameter fusion method of the present invention, aiming at the problem that it is difficult to identify the characteristics of wideband signals in a wideband uniform channel structure, uses the corresponding relationship between the signal frequency domain and the channel passband to determine whether the signal is a cross-channel signal, and fuses all signal characteristics to obtain accurate wideband signal parameters; aiming at the problems of missed detection and incorrect feature detection of multiple time-domain overlapping signals in a single channel in a wideband uniform channel structure, through spectrum detection and inverse fast Fourier transform, using spectrum detection to determine whether multiple signals can be distinguished in the frequency domain, if so, inverse fast Fourier transform is performed on each of them respectively and then time-domain amplitude detection is performed again, and after time-domain amplitude detection, combined with the number of points of the inverse fast Fourier transform, the true arrival time and pulse width of the signal are obtained, solving the problems of missed detection and incorrect feature detection of multiple time-domain overlapping signals in a single channel, and significantly improving the accuracy of signal detection and parameter estimation. Description of the Drawings

[0025] Figure 1 It is a schematic diagram of the multi-phase filtering channelization structure of the present invention;

[0026] Figure 2 It is a flowchart of the ultra-wideband signal detection and parameter fusion of the present invention. Detailed Embodiments

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0028] Please refer to Figure 1-2 , the present invention provides a multi-phase filtering channelization ultra-wideband signal detection and fusion method and system, including the following steps:

[0029] Channelization: The received bandwidth is evenly divided into K sub-channels with 50% overlap between adjacent sub-channels. A suitable prototype filter is designed according to the frequency response. The polyphase filter coefficients are determined based on the number of sub-channels and the prototype filter. D-fold decimation is performed at the front end of the polyphase filter. The decimated signal enters the polyphase filter for filtering, and then the inverse fast Fourier transform (IFFT) is performed on the K points output each time to obtain the output signals of the K sub-channels;

[0030] Time-domain amplitude detection: The signals of each channel are envelope-smoothed to reduce noise perturbation, and then amplitude detection is performed to obtain the time of arrival (TOA) and pulse width (PW) information of the signals in the time domain. The fast Fourier transform (FFT) is performed on the detected time-domain signals to obtain the signal spectrum;

[0031] Spectrum detection: The signal spectrum is smoothed to reduce the influence of noise. It is determined whether there are multiple signals by using the amplitude. If there is only a single signal, its start frequency and end frequency are estimated. If there are multiple signals, the start frequency and end frequency of each signal are estimated respectively, and then the corresponding frequency-domain signals are taken for the inverse fast Fourier transform and then the time-domain feature detection is performed again to distinguish the time of arrival and pulse width of the multiple signals;

[0032] Parameter fusion: According to the start frequency and end frequency of the signal, it is judged whether the signal falls within the channel passband or the transition band. If the signal entirely falls within the transition band, the signal is discarded. If the signal entirely falls within the channel passband, the parameters are merged and the current signal parameters are directly stored. If the signal falls within both the channel passband and the transition band, a signal that overlaps with the current signal in both the time domain and the frequency domain is searched for in the next adjacent channel. This process is repeated for the qualified signals until a signal with an end frequency within the channel passband is found. The signal characteristics of all signals in this process are fused with each other to obtain the cross-channel signal parameters.

[0033] Further, in the channelization step, a suitable prototype filter is designed by using the Chebyshev approximation algorithm according to the frequency response. After performing K-channel polyphase decomposition on the prototype low-pass filter and inserting zeros by a factor of 2, the tap coefficients of each of the K polyphase filter branches are obtained.

[0034] Further, in the time-domain amplitude detection step, the power value of the silent channel signal plus the threshold factor a is taken as the time-domain noise threshold (thr0). Time-domain detection is performed according to the time-domain noise threshold, and signals with a pulse width less than 0.1 us are filtered out as glitches;

[0035] Further, perform a fast Fourier transform on the detected time-domain signal to obtain the signal spectrum, specifically including: intercept the corresponding signal in the time domain and take the data corresponding to the quiet channel signal respectively for N-point fast Fourier transform. The number of points for the fast Fourier transform is the signal length N, and obtain the signal spectrum by taking the amplitude of the time-domain signal;

[0036] Further, in the spectrum detection step, estimate the noise power of the quiet channel signal within the channel passband, add the noise power value and the threshold factor β as the frequency-domain noise detection threshold (thr1), and perform spectrum detection according to the frequency-domain noise detection threshold (thr1) to filter out the spikes less than 5 points.

[0037] Further, in the parameter fusion step, after detecting K channels, sort them in ascending order of channel number and then perform parameter merging. The starting frequency of the first signal to be merged after sorting is not in the low-side transition band of the corresponding channel.

[0038] Further, the method further includes: in the time-domain amplitude detection step, if multiple signals are detected to overlap in the time domain within a single channel, perform spectrum detection on the multiple signals according to the frequency-domain noise detection threshold, and estimate the starting frequency and ending frequency of each signal respectively, so as to distinguish the multiple signals overlapping in the time domain within a single channel;

[0039] Further, in the application scenario of the method, the sampling rate is 2.4 GHz, 2.4 GHz is evenly divided into K = 32 channels according to the channel width of 75 MHz, and the received signal decimation factor D = 16.

[0040] Further, the method further includes: in the parameter fusion step, if the signal falls within both the channel passband and the transition band at the same time, find the signal that overlaps with the current signal in both the time domain and the frequency domain in the next adjacent channel, and repeat this process for the qualified signals until a signal with the ending frequency within the channel passband is found, and fuse the signal characteristics of all signals in this process to obtain the cross-channel signal parameters.

[0041] A multi-phase filtering channelized ultra-wideband signal parameter detection system, including:

[0042] A channelization module, used to evenly divide the received bandwidth into K sub-channels, with 50% overlap between adjacent sub-channels, design a suitable prototype filter according to the frequency response, determine the multi-phase filter coefficients according to the number of sub-channels and the prototype filter, perform D-fold decimation at the front end of the multi-phase filter, and the decimated signal enters the multi-phase filter for filtering, and then perform an inverse fast Fourier transform on the K points output each time to obtain the output signals of the K sub-channels;

[0043] The time-domain amplitude detection module is used to smooth the envelope of the signals of each channel to reduce noise disturbance, and then perform amplitude detection to obtain the arrival time and pulse width information of the signals in the time domain. The detected time-domain signals are subjected to fast Fourier transform to obtain the signal spectrum;

[0044] The spectrum detection module is used to smooth the signal spectrum to reduce the influence of noise, and use the amplitude to determine whether there are multiple signals; if there is only a single signal, estimate its starting frequency and ending frequency; if there are multiple signals, estimate their starting frequencies and ending frequencies respectively, and then take the corresponding frequency-domain signals for inverse fast Fourier transform and then perform time-domain feature detection again to distinguish the arrival time and pulse width of multiple signals;

[0045] The parameter fusion module is used to judge whether the signal falls within the channel passband or the transition band according to the starting frequency and ending frequency of the signal. If all signals fall within the transition band, the signal is discarded; if all signals fall within the channel passband, the parameters are merged and the current signal parameters are directly stored; if the signal falls within both the channel passband and the transition band, search for signals that overlap both in time domain and frequency domain with the current signal in the next adjacent channel, and repeat this process for the qualified signals until a signal with the ending frequency within the channel passband is found, and the signal characteristics of all in this process are fused with each other to obtain the cross-channel signal parameters;

[0046] Among them, channelization: evenly divide the received bandwidth into K sub-channels, with 50% overlap between adjacent sub-channels. Design a suitable prototype filter according to the frequency response, determine the polyphase filter coefficients according to the number of sub-channels and the prototype filter, perform D-fold decimation at the front end of the polyphase filter, and the decimated signal enters the polyphase filter for filtering, and then perform inverse fast Fourier transform on the K points output each time to obtain the output signals of K sub-channels; in the specific implementation, the sampling rate is 2.4 GHz, the channel width is 75 MHz, divided into K = 32 channels, with 50% overlap between adjacent channels, and the decimation multiple D of the received signal is 16. As Figure 1 The channelization structure design process shown is as follows:

[0047] 1) Shunt the signal into K paths, that is, divide the signal s(n) into K = 32 paths s 0 (m), s 1 (m)... s K-1 , where n = m * D and D = 16 is the decimation multiple.

[0048] 2) Use the built-in filter design algorithm (Prime-factor-based FIR Filter Design) in the mathematical application software (Matlab, MATrix LABoratory) to design the prototype filter, whose passband cut-off frequency The blocking starting frequency is After the K-path polyphase decomposition and 2-fold interpolation zero of the prototype filter, the tap coefficients of each of the K polyphase filter branches are obtained, such as Figure 1 the filter h 0 (m), h 1 (m)... h K-1 (m).

[0049] The 32-channel signals are convolved with the corresponding filter coefficients respectively, and 32 points are output at each moment, and these 32 points are subjected to inverse fast Fourier transform;

[0050] Multiply by (-1) (k-1)n , k = 0, 1,... K-1, as the output of each channel.

[0051] Specifically, the channelization process includes prototype filter design: using the filter design algorithm (Prime-factor-based FIR Filter Design) built in the mathematical application software (Matlab, MATrixLABoratory) to design the prototype filter, and determining the parameters of the filter according to the received bandwidth and channel division requirements.

[0052] Determination of polyphase filter coefficients: Perform 32-path polyphase decomposition on the designed prototype filter and perform 2-fold interpolation zero to obtain the tap coefficients of each of the 32 polyphase filter branches.

[0053] Decimation and filtering: Decimate the received signal by 16 times, and the decimated signal enters the polyphase filter for filtering, and 32 points are output each time.

[0054] Inverse fast Fourier transform processing: Perform inverse fast Fourier transform on the 32 points output each time to obtain the output signals of 32 sub-channels.

[0055] According to the frequency response, use the filter design algorithm built in the Matlab (MATrix LABoratory) mathematical application software. The filter design algorithm designs a suitable prototype filter through the Chebyshev approximation (Parks-McClellan) algorithm to achieve the optimal matching of the frequency response. The suitable prototype filter has excellent frequency response characteristics; the channel passband cut-off frequency of the prototype filter is 75 MHz, and the stopband start frequency is 85 MHz. After the K-path polyphase decomposition and 2-fold interpolation zero of the prototype filter, the tap coefficients of each of the K polyphase filter branches are obtained.

[0056] Such as Figure 2 shown is the block diagram of signal detection and parameter fusion processing of the present invention, and the processing flow is introduced as follows:

[0057] (1) Since 32 channels cover 0 - 1200 MHz and the frequency domain range of the broadband intermediate frequency signal is 100 MHz - 1100 MHz, within different channel frequency ranges, the channel with the minimum energy is taken as the silent channel, and its noise power plus the threshold factor a (the factor is adjustable) is used as the time-domain noise threshold (thr0);

[0058] (2) Time-domain amplitude detection: To avoid noise disturbance, the output signal of each sub-channel is subjected to envelope smoothing processing, and the noise power of the silent channel is estimated. Then, time-domain amplitude detection is performed to obtain the arrival time and pulse width of the signal in the time domain and record the channel number. According to the time-domain noise threshold, the rising edge and falling edge of the signal are determined. The signal pulse width = termination time - arrival time of the signal. Signals with a pulse width (PW, Pulse Width) less than 0.1 us are filtered out as glitches. The corresponding signal in the time domain is intercepted and subjected to an N-point fast Fourier transform. The number of points of the fast Fourier transform is the signal length N, and the amplitude is taken to obtain the signal spectrum. At the same time, the corresponding data of the silent channel signal is taken for an N-point fast Fourier transform to estimate its noise power within the channel passband. The noise power value plus the threshold factor β (the factor is adjustable) is used as the frequency-domain noise detection threshold (thr1);

[0059] (3) Spectrum detection: The detected time-domain signal is subjected to a fast Fourier transform to obtain the signal spectrum. The signal spectrum is smoothed to reduce the influence of noise. It is determined whether there are multiple signals using the amplitude, and glitches less than 5 points are filtered out. The rising edge is the starting frequency of the signal, the falling edge is the termination frequency of the signal, and the center frequency is the average of the two. If there is only one rising edge, it is determined as a single signal, and its starting frequency and termination frequency are directly output. Otherwise, each segment of the spectrum is detected separately. First, the starting frequency and termination frequency of each signal are estimated respectively. Then, the corresponding frequency-domain signal is subjected to an inverse fast Fourier transform and then time-domain amplitude detection again to obtain the starting point and termination point of the signal corresponding to this spectrum. Finally, the product of the ratio of the starting point and the signal length N and the total time from the starting point to the termination point of the signal corresponding to this spectrum is the true arrival time and pulse width, achieving the purpose of separating the parameters of multiple signals with time-domain overlap within a single channel;

[0060] (4) Parameter fusion: After detecting 32 channels, the parameters are merged after sorting in ascending order of channel numbers (the starting frequency of the first signal to be merged after sorting must not be in the lower transition band of the corresponding channel). It is determined whether the signal falls within the channel passband or the transition band based on the starting frequency and the ending frequency of the signal. If all signals fall within the transition band, the signal is discarded. If all signals fall within the channel passband, the parameters are merged and the current signal parameters are directly stored. If the starting frequency of the signal is within the channel passband and the ending frequency is within the transition band, it is determined whether there is a signal in the next channel that overlaps with this signal in the time domain. If there is, it is denoted as s1. It is determined whether the starting frequency of s1 is within the lower transition band. If not, it is skipped. If it is, it is determined whether the ending frequency is within the passband or the transition band. If it is within the passband, the search is terminated. If it is within the transition band, this process is repeated until the ending frequency is within the passband. The minimum arrival time of all signals in this process is taken as the arrival time of this cross-channel signal, and the maximum termination time is taken as the signal termination time. The frequency is the same.

[0061] Through the above specific implementation manners, the multi-phase filtering channelization ultra-wideband signal detection and fusion method of the present invention can effectively detect the parameters of ultra-wideband signals and improve the accuracy and reliability of ultra-wideband signal parameter detection.

[0062] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A polyphase filtering channelized ultra-wideband signal detection and fusion method, characterized in that: The following steps are involved: Channelization: The receiving bandwidth is evenly divided into K sub-channels, with 50% overlap between adjacent sub-channels. A suitable prototype filter is designed according to the frequency response. The coefficients of the polyphase filter are determined according to the number of sub-channels and the prototype filter. D-fold decimation is performed at the front end of the polyphase filter. The decimated signal enters the polyphase filter for filtering, and then the K points of each output are subjected to inverse fast Fourier transform to obtain the output signals of the K sub-channels. Time domain amplitude detection: The signal of each channel is envelope smoothed to reduce noise disturbance, and then amplitude detection is performed to obtain the arrival time and pulse width information of the signal in the time domain. The detected time domain signal is fast Fourier transformed to obtain the signal spectrum; Spectrum detection: Smooth the signal spectrum to reduce the impact of noise, and use the amplitude to determine whether there are multiple signals; if there is only a single signal, estimate its starting frequency and ending frequency; if there are multiple signals, estimate the starting frequency and ending frequency of each signal respectively, then take the corresponding frequency domain signal for inverse fast Fourier transform and perform time domain feature detection again to determine the arrival time and pulse width of multiple signals; Parameter fusion: Based on the signal starting frequency and ending frequency, determine whether the signal falls within the channel passband or the transition band. If the signal falls entirely within the transition band, the signal is discarded. If the signal falls entirely within the channel passband, the parameters are merged and the current signal parameters are directly stored. If the signal falls within both the channel passband and the transition band, search for a signal in the next adjacent channel that overlaps with the current signal in both the time domain and the frequency domain. Repeat this process for signals that meet the requirements until a signal with an ending frequency within the channel passband is found. All signal features in this process are fused together to obtain cross-channel signal parameters.

2. The polyphase filtering channelized ultra-wideband signal detection and fusion method according to claim 1 is characterized in that: In the channelization step, a suitable prototype filter is designed according to the frequency response through the Chebyshev approximation algorithm. After k-way polyphase decomposition and 2-fold interpolation of zeros on the prototype low-pass filter, the tap coefficients of k polyphase filter branches are obtained.

3. The polyphase filtering channelized ultra-wideband signal detection and fusion method according to claim 1, characterized in that: In the time domain amplitude detection step, the channel with the minimum energy is taken as the silent channel, the signal power value of the silent channel plus the threshold factor a is taken as the time domain noise threshold, and time domain detection is performed based on the time domain noise threshold, and the signal with a pulse width less than 0.1us is filtered out as a burr.

4. The polyphase filtering channelized ultra-wideband signal detection and fusion method according to claim 1, characterized in that: The detected time domain signal is subjected to fast Fourier transform to obtain the signal spectrum, specifically including: intercepting the corresponding signal in the time domain and taking the corresponding data of the silent channel signal to perform N-point fast Fourier transform respectively, the number of fast Fourier transform points is the signal length N, and taking the amplitude of the time domain signal to obtain the signal spectrum.

5. The polyphase filtering channelized ultra-wideband signal detection and fusion method according to claim 4, characterized in that: In the spectrum detection step, the noise power of the silent channel signal in the channel passband is estimated, and the noise power value plus the threshold factor β is used as the frequency domain noise detection threshold to filter out burrs less than 5 points.

6. The polyphase filtering channelized ultra-wideband signal detection and fusion method according to claim 1, characterized in that: In the parameter fusion step, after the K channels are detected, the parameters are merged after sorting them from small to large according to the channel numbers. After sorting, the starting frequency of the first signal to be merged is not in the low-side transition band of the corresponding channel.

7. The polyphase filtering channelized ultra-wideband signal detection and fusion method according to claim 1, characterized in that: The method also includes: in the time domain amplitude detection step, if it is detected that multiple signals overlap in the time domain in a single channel, spectrum detection is performed on the multiple signals according to the frequency domain noise detection threshold, and the starting frequency and the ending frequency of each signal are estimated respectively, so as to distinguish the multiple signals overlapping in the time domain in the single channel.

8. The polyphase filtering channelized ultra-wideband signal detection and fusion method according to claim 1, characterized in that: In the application scenario of the method, the sampling rate is 2.4 GHz, 2.4 GHz is evenly divided into K=32 channels according to the channel width of 75 MHz, and the received signal extraction multiple D=16.

9. The polyphase filtering channelized ultra-wideband signal detection and fusion method according to claim 1, characterized in that: The method also includes: in the parameter fusion step, if the signal falls into the channel passband and transition band at the same time, searching for a signal that overlaps with the current signal in both time domain and frequency domain in the next adjacent channel, repeating this process for signals that meet the requirements until a signal with an end frequency within the channel passband is found, and fusing all signal features in this process to obtain cross-channel signal parameters.

10. A polyphase filtering channelized ultra-wideband signal parameter detection system, characterized in that: include: The channelization module is used to evenly divide the receiving bandwidth into K sub-channels, with 50% overlap between adjacent sub-channels, design a suitable prototype filter according to the frequency response, determine the polyphase filter coefficient according to the number of sub-channels and the prototype filter, perform D-fold decimation at the front end of the polyphase filter, and the decimated signal enters the polyphase filter for filtering, and then performs inverse fast Fourier transform on the K points output each time to obtain the output signal of the K sub-channels; The time domain amplitude detection module is used to perform envelope smoothing on the signal of each channel to reduce noise disturbance, and then perform amplitude detection to obtain the arrival time and pulse width information of the signal in the time domain, and perform fast Fourier transform on the detected time domain signal to obtain the signal spectrum; The spectrum detection module is used to smooth the signal spectrum to reduce the impact of noise, and use the amplitude to determine whether there are multiple signals; if there is only a single signal, estimate its starting frequency and ending frequency; if there are multiple signals, estimate their starting frequency and ending frequency respectively, then take the corresponding frequency domain signal for inverse fast Fourier transform and then perform time domain feature detection again to determine the arrival time and pulse width of multiple signals; The parameter fusion module is used to determine whether the signal falls within the channel passband or the transition band based on the signal starting frequency and ending frequency. If the signal falls entirely within the transition band, the signal is discarded; if the signal falls entirely within the channel passband, the parameters are merged and the current signal parameters are directly stored; if the signal falls within both the channel passband and the transition band, the next adjacent channel is searched for a signal that overlaps with the current signal in both the time domain and the frequency domain, and this process is repeated for signals that meet the requirements until a signal with an ending frequency within the channel passband is found. All signal features in this process are fused together to obtain cross-channel signal parameters.

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