CN115441970A - Broadband signal detection method based on scale iteration and spectrum compensation - Google Patents

Broadband signal detection method based on scale iteration and spectrum compensation Download PDF

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CN115441970A
CN115441970A CN202211098822.9A CN202211098822A CN115441970A CN 115441970 A CN115441970 A CN 115441970A CN 202211098822 A CN202211098822 A CN 202211098822A CN 115441970 A CN115441970 A CN 115441970A
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signal
power spectrum
bandwidth
detection
iteration
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巩克现
杨晨旭
王忠勇
江桦
刘佳琪
郑向阳
王玮
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Zhengzhou University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0058—Allocation criteria
    • H04L5/006—Quality of the received signal, e.g. BER, SNR, water filling
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0058—Allocation criteria
    • H04L5/0064—Rate requirement of the data, e.g. scalable bandwidth, data priority

Abstract

The invention belongs to the technical field of wireless communication, in particular to a broadband signal detection method based on scale iteration and spectrum compensation, which aims at solving the problems that the existing detection algorithm of broadband signals is easily influenced by noise, has poor detection effect under low signal-to-noise ratio, higher complexity, long detection time, needs a large amount of prior information and the like, and provides the following scheme, comprising the following steps: a, a received signal is subjected to AD sampling to obtain a broadband sampling signal, and the broadband signal is divided into a plurality of sections of signals with proper bandwidths through digital channelization; the invention aims to make the noise floor estimation more accurate by using the characteristic of morphological filtering bandwidth screening aiming at broadband signals and avoid false detection caused by uneven noise floor; the error caused by strong pulse and white noise in the signal to the signal parameter estimation is overcome by utilizing the smooth iteration, the detection performance of the band aliasing signal is improved through the frequency spectrum compensation, the blind detection of the broadband signal is realized, and the detection accuracy is improved.

Description

Broadband signal detection method based on scale iteration and spectrum compensation
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a broadband signal detection method based on scale iteration and spectrum compensation.
Background
The purpose of broadband signal detection is to quickly find out signals on a broadband spectrum, estimate the center frequency and bandwidth of each signal and provide prior information for subsequent signal analysis and processing. The method has a great number of applications in various civil and military communication systems, for example, detection of a satellite spectrum, radar signal scanning, sensing of battlefield information and the like all need detection of a broadband spectrum.
However, in non-cooperative communication, spectrum detection is usually completed under the conditions of a lack of a priori information, a complex electromagnetic environment, an uneven colored noise floor and the like, and therefore a broadband signal detection algorithm is required to have a function of realizing blind detection without a priori information and resisting the uneven noise floor under a low signal-to-noise ratio.
Detection algorithms for broadband signals are typically as follows: the method comprises the steps of firstly, a signal energy detection method, such as an energy detection method of unknown deterministic signals provided by Urkowitz (1967), an improved multi-antenna signal energy detection scheme provided by Ringen (2017) and the like, has the advantage of frequency deviation resistance, is easily influenced by noise, and has a poor detection effect under a low signal-to-noise ratio; the cyclostationary detection method, such as an algorithm based on second-order cyclostationary property proposed by a.tani (2016), has the advantages of good detection performance, high complexity and long detection time; and thirdly, a matched filter detection method, such as a detection method combining a parallel matched filter and a segmented filter, proposed by z.zhang (2010), is an optimal signal detection method in theory, but needs a large amount of prior information such as carrier frequency, bandwidth and modulation mode, and is difficult to implement in practice and not suitable for uncooperative communication. In addition, some novel algorithms are proposed in recent years, and the signal detection performance is greatly improved compared with that of the classical algorithm. The gradient dual-threshold algorithm proposed by Zhang Yan (2016) calculates gradient in sections for broadband power spectrum, and then performs adaptive dual-threshold detection. The power spectrum cancellation method proposed by zipelham (2014) uses the ratio of the sum of partial spectral line intensities within the segmented frequency band to the sum of all spectral line intensities as a test statistic to make a determination of the presence of a signal.
Disclosure of Invention
The method is based on scale iteration and spectrum compensation and aims to solve the problems that an existing detection algorithm of the broadband signal is easily affected by noise, the detection effect is poor under the condition of low signal-to-noise ratio, the complexity is high, the detection time is long, a large amount of prior information is needed and the like.
In order to achieve the purpose, the invention adopts the following technical scheme:
a broadband signal detection method based on scale iteration and spectrum compensation comprises the following steps:
a, receiving signals, carrying out AD sampling to obtain broadband sampling signals, and dividing the broadband signals into a plurality of sections of signals with proper bandwidths through digital channelization;
b, taking several sections of broadband signals, and calculating a maximum power spectrum according to the power spectral density of each section of signal;
estimating and removing a colored noise substrate by using a scale iteration morphological filtering algorithm, and calculating a white noise power threshold;
and D, detecting a signal with the highest amplitude in the power spectrum, estimating the center frequency, the bandwidth and the starting and stopping positions of the signal frequency band, eliminating the signal according to a compensation algorithm, completing compensation, detecting one signal every time, performing the step D again according to the compensated power spectrum if the energy difference value of residual signals before and after two times of signal detection is higher than a preset energy threshold, and otherwise, finishing the signal detection.
Preferably, morphological filtering iteration is performed on the power spectrum by using the characteristic of morphological filtering bandwidth screening, so that the noise floor estimation is more accurate, errors caused by strong pulses and white noise in the signal to signal parameter estimation are overcome by using smoothing iteration, and the detection performance of the frequency band aliasing signal is improved by spectrum compensation.
Preferably, the step a specifically includes the following steps:
a1: calculating the center frequency of a real signal channelized sub-channel
Figure BDA0003838764950000021
D is a data extraction multiple, and the whole frequency band is divided into D symmetrical sub-bands of real signals;
a2: anti-aliasing filter h LP (N) is the low-pass FIR filter, K is the number of channels, D is the decimation factor, and N and D have an integer multiple relationship, i.e., K = FD, then the output of the kth sub-channel in the conventional channelization structure is:
Figure BDA0003838764950000031
a3: the above formula is rewritten as a polyphase filter structure, and the output expression of the k-th sub-channel is obtained as follows:
Figure BDA0003838764950000032
a4: centering frequency omega k In the formula, the output expression of the kth sub-channel at this time is:
Figure BDA0003838764950000033
preferably, in step B, the received data is segmented m times, where the length of each segment is L, and the interval between segments is K, and the extracted data may be represented in a matrix form:
Figure BDA0003838764950000034
estimating the power spectrum P of each segment of data by a periodogram method i (f) I.e. by
Figure BDA0003838764950000035
Where w (n) is a selected window function, the power spectrum of each segmented signal is found, and the maximum value of the power spectrum is found at each frequency point, i.e.
Figure BDA0003838764950000036
Preferably, the step C specifically includes the following steps:
b1: dimension B bi Dereferencing the minimum detection bandwidth B min For performing morphological filtering PN once 1 (f)；
B2: dimension B bi Value last filtering scale B bi-1 Twice of the maximum detection bandwidth B, if the size is larger than the maximum detection bandwidth B max Then with B max Performing morphological filtering for the scale, wherein the ith filtering result is PN i (f)；
B3, differentiating the filtering results of the ith and the (i-1) th adjacent two times to obtain a differential result delta PN i Comparing the difference results twice frequency point by frequency point if delta PN i (f)＞ΔPN i-1 (f) Then the scale at the frequency point f is updated to B bi Otherwise, the scale is kept unchanged;
b4 if B bi ＝B max Then stopping iteration;
b5, determining the dimension B at each frequency point f by iteration bi (f) Performing morphological filtering once to estimate a colored noise substrate;
b6, subtracting B5 from the original power spectrum to estimate a noise base to obtain a power spectrum with flat noise base;
b7, converting the power spectrum into a logarithmic power spectrum, dividing a proper number of power bands from 0 to the maximum value on the power spectrum, counting the number of frequency points falling in each band, wherein the more the number of the power bands is, the more accurate the estimation of the white noise threshold is, the difference is respectively carried out on the number of frequency points of the adjacent power bands, and the upper limit of the power band with the maximum difference result is used as the white noise power threshold.
Preferably, the step D specifically includes the following steps:
c1 finding the position f of the maximum in the power spectrum max As the center frequency f Ctr Amplitude P (f) at the center frequency Ctr ) -3dB calculation of the 3dB bandwidth B 3dB Left boundary of bandwidth is f 3dBL The right boundary is f 3dBR ；
C2, the power spectrum is in f 3dBL ,f 3dBR ]In the dimension λ sm ＝0.1*B 3dB Returning to C1 after smooth filtering processing, and entering C3 after circulation is carried out for five times;
c3, after five times of circular smoothing, the obtained bandwidth is 3dB bandwidth, and the final bandwidth is f 3dBL ,f 3dBR ]Calculating the center frequency within the range according to the following formula;
Figure BDA0003838764950000051
c4 at f 3dBL To the left, f 3dBR The first minimum value point or zero point of the right-searching power spectrum is used as the boundary of the main lobe of the signal, and the left boundary is f LS The right boundary is f RS In [ f) LS ,f RS ]Within the range, the signal is zeroed and eliminated, and the out-of-band slope k is calculated according to the following formula LS 、k RS ：
Figure BDA0003838764950000052
Figure BDA0003838764950000053
C5 at f LS ，f RS At each slope k LS 、k RS Into the estimated signal band f LS ,f RS ]Extending until the two lines intersect, and replacing the in-band spectral line of the estimated signal by the two extension lines to reduce the damage to the estimated signal;
and C6, if the estimated energy of the C4 is lower than the minimum judgment energy or the bandwidth is smaller than the minimum detection bandwidth, discarding the estimated energy or the bandwidth, and if the maximum amplitude of the residual power spectrum is not higher than a white noise threshold, finishing the signal detection.
The invention has the following beneficial effects: aiming at the broadband signal, morphological filtering iteration is carried out on the power spectrum by utilizing the characteristic of morphological filtering bandwidth screening, so that the noise floor estimation is more accurate, and false detection caused by uneven noise floor is avoided; the error caused by strong pulse and white noise in the signal to the signal parameter estimation is overcome by utilizing smooth iteration, the detection performance of the band aliasing signal is improved through frequency spectrum compensation, the blind detection of the broadband signal is realized, and the detection accuracy is improved.
Drawings
FIG. 1 is a flow chart of steps of a method for wideband signal detection based on scale iteration and spectral compensation;
FIG. 2 is a digital channelization structure;
FIG. 3 is a schematic diagram of a broadband signal spectrum;
FIG. 4 is a signal detection process;
FIG. 5 is a diagram illustrating colored noise floor estimation results;
FIG. 6 is a diagram illustrating the signal detection result;
fig. 7 shows the signal accuracy probability at different signal-to-noise ratios.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments.
Example one
As shown in fig. 1-7, a method for detecting a broadband signal based on scale iteration and spectrum compensation includes the following steps:
a, receiving signals, carrying out AD sampling to obtain broadband sampling signals, and dividing the broadband signals into a plurality of sections of signals with proper bandwidths through digital channelization;
b, taking several sections of broadband signals, and calculating a maximum power spectrum according to the power spectral density of each section of signal;
estimating and removing a colored noise substrate by using a scale iteration morphological filtering algorithm, and calculating a white noise power threshold;
and D, detecting the signal with the highest amplitude in the power spectrum, estimating the center frequency, the bandwidth and the starting and stopping positions of the signal frequency band, eliminating the signal according to a compensation algorithm, finishing compensation, detecting one signal each time, performing the step D again according to the compensated power spectrum if the energy difference value of the residual signal before and after the signal detection for two times is higher than a preset energy threshold, and otherwise, finishing the signal detection.
In this embodiment, the step a specifically includes the following steps:
a1: calculating center frequencies of real signal channelized sub-channels
Figure BDA0003838764950000061
D is a data extraction multiple, and the whole frequency band is divided into D symmetrical sub-bands of real signals;
a2: anti-aliasing filter h LP (N) is the low-pass FIR filter, K is the number of channels, D is the decimation factor, and N and D have an integer multiple relationship, i.e., K = FD, then the output of the kth sub-channel in the conventional channelization structure is:
Figure BDA0003838764950000071
a3: the above formula is rewritten as a polyphase filter structure, and the output expression of the kth subchannel obtained is:
Figure BDA0003838764950000072
a4: centering frequency omega k In the formula, the output expression of the kth sub-channel at this time is:
Figure BDA0003838764950000073
in this embodiment, in step B, the received data is segmented m times, the length of each segment is L, the interval between segments is K, and the extracted data can be represented in a matrix form:
Figure BDA0003838764950000074
estimating the power spectrum P of each segment of data by a periodogram method i (f) I.e. by
Figure BDA0003838764950000075
Where w (n) is a selected window function, the power spectrum of each segmented signal is found, and the maximum value of the power spectrum is found at each frequency point, i.e.
Figure BDA0003838764950000076
In this embodiment, the step C specifically includes the following steps:
b1: dimension B bi Dereferencing the minimum detection bandwidth B min For performing morphological filtering PN once 1 (f)；
B2: dimension B bi Value last filtering scale B bi-1 Twice of the maximum detection bandwidth B, if the size is larger than the maximum detection bandwidth B max Then use B max Performing morphological filtering for scale, wherein the ith filtering result is PN i (f)；
B3, differentiating the filtering results of the ith and the (i-1) th adjacent two times to obtain a differential result delta PN i Comparing the difference results twice frequency point by frequency point if delta PN i (f)＞ΔPN i-1 (f) Then the scale at the frequency point f is updated to be B bi Otherwise, the dimension is kept unchanged;
b4 is if B bi ＝B max Then stopping iteration;
b5, dimension B determined by iteration at each frequency point f bi (f) Performing primary morphological filtering to estimate a colored noise substrate;
b6, subtracting B5 from the original power spectrum to estimate a noise base to obtain a power spectrum with flat noise base;
and B7, converting the power spectrum into a logarithmic power spectrum, defining a proper number of power bands from 0 to the maximum value on the power spectrum, counting the number of frequency points falling in each band, wherein the more the number of the power bands is, the more accurate the white noise threshold estimation is, the frequency point numbers of adjacent power bands are respectively differentiated, and the upper limit of the power band with the maximum differential result is used as the white noise power threshold.
In this embodiment, the step D specifically includes the following steps:
c1 finding the position f of the maximum in the Power Spectrum max As the center frequency f Ctr Amplitude P (f) at the center frequency Ctr ) -3dB calculation of the 3dB bandwidth B 3dB Left boundary of bandwidth is f 3dBL Right boundary is f 3dBR ；
C2, the power spectrum is in f 3dBL ,f 3dBR ]In the dimension λ sm ＝0.1*B 3dB Returning to C1 after smooth filtering processing, and entering C3 after circulation is carried out for five times;
c3, after five times of circular smoothing, the obtained bandwidth is 3dB bandwidth, and the final bandwidth is f 3dBL ,f 3dBR ]Calculating the center frequency within the range according to the following formula;
Figure BDA0003838764950000081
c4 at f 3dBL To the left, f 3dBR The first minimum value point or zero point of the right-searching power spectrum is used as the boundary of the main lobe of the signal, and the left boundary is f LS Right boundary is f RS In [ f) LS ,f RS ]Within the range, signals are zeroed and eliminated, and an out-of-band slope k is calculated according to the formula LS 、k RS ：
Figure BDA0003838764950000091
Figure BDA0003838764950000092
C5 at f LS ，f RS At a slope k LS 、k RS Into the estimated signal band f LS ,f RS ]Extending until the two lines intersect, and replacing the in-band spectral line of the estimated signal by the two extension lines to reduce the damage to the estimated signal;
and C6, if the estimated energy of the C4 is lower than the minimum judgment energy or the bandwidth is smaller than the minimum detection bandwidth, discarding the estimated energy or the bandwidth, and if the maximum amplitude of the residual power spectrum is not higher than a white noise threshold, finishing the signal detection.
Example two
As shown in fig. 1 to 7, a method for detecting a broadband signal based on scale iteration and spectrum compensation includes the following steps:
a, receiving signals, carrying out AD sampling to obtain broadband sampling signals, and dividing the broadband signals into a plurality of sections of signals with proper bandwidths through digital channelization;
b, acquiring sections of broadband signals, and calculating a maximum power spectrum according to the power spectral density of each section of signal;
estimating and removing a colored noise substrate by using a scale iteration morphological filtering algorithm, and calculating a white noise power threshold;
and D, detecting a signal with the highest amplitude in the power spectrum, eliminating the signal according to a compensation algorithm, finishing compensation, detecting one signal every time, performing the step D again according to the compensated power spectrum if the energy difference value of the residual signal before and after two times of signal detection is higher than a preset energy threshold, and otherwise, finishing the signal detection.
In this embodiment, the step a specifically includes the following steps:
a1: calculating center frequencies of real signal channelized sub-channels
Figure BDA0003838764950000093
D is a data extraction multiple, and the whole frequency band is divided into D symmetrical sub-bands of real signals;
a2: anti-aliasing filter h LP (N) is the low-pass FIR filter, K is the number of channels, D is the decimation factor, and N and D have integer multiple relation, i.e., K = FD, then the output of the kth sub-channel in the conventional channelization structureComprises the following steps:
Figure BDA0003838764950000101
a3: the above formula is rewritten as a polyphase filter structure, and the output expression of the k-th sub-channel is obtained as follows:
Figure BDA0003838764950000102
a4: centering frequency omega k In the formula, the output expression of the kth sub-channel at this time is:
Figure BDA0003838764950000103
in this embodiment, in step B, the received data is segmented m times, where the length of each segment is L, and the interval between segments is K, and the extracted data may be represented in a matrix form:
Figure BDA0003838764950000104
estimating the power spectrum P of each segment of data by a periodogram method i (f) I.e. by
Figure BDA0003838764950000105
Where w (n) is a selected window function, the power spectrum of each segmented signal is found and the maximum of the power spectrum is found at each frequency point, i.e.
Figure BDA0003838764950000106
In this embodiment, the step C specifically includes the following steps:
b1: dimension B bi Dereferencing the minimum detection bandwidth B min For performing morphological filtering PN once 1 (f)；
B2 is the scale B bi Last filtering scale B bi-1 Twice of (2) to perform a morphological filtering, the ith filtering result being PN i (f)；
B3, differentiating the filtering results of the ith and the (i-1) th adjacent two times to obtain a differential result delta PN i If Δ PN i (f)＞ΔPN i-1 (f) Then the scale at the frequency point f is updated to B bi Otherwise, the scale is kept unchanged;
b4 is if B bi ＝B max Stopping iteration;
b5, determining the dimension B at each frequency point f by iteration bi (f) Performing primary morphological filtering to estimate a colored noise substrate;
b6, subtracting B5 from the original power spectrum to estimate a noise base to obtain a power spectrum with flat noise base;
and B7, converting the power spectrum into a logarithmic power spectrum, defining a proper number of power bands from 0 to the maximum value on the power spectrum, counting the number of frequency points falling in each band, wherein the more the number of the power bands is, the more accurate the white noise threshold estimation is, the frequency point numbers of adjacent power bands are respectively differentiated, and the upper limit of the power band with the maximum differential result is used as the white noise power threshold.
In this embodiment, the step D specifically includes the following steps:
c1 finding the position f of the maximum in the power spectrum max As the center frequency f Ctr Amplitude P (f) at the center frequency Ctr ) -3dB calculation of the 3dB bandwidth B 3dB Left boundary of bandwidth is f 3dBL The right boundary is f 3dBR ；
C2, the power spectrum is in f 3dBL ,f 3dBR ]In the dimension λ sm ＝0.1*B 3dB Returning to C1 after smooth filtering processing, and entering C3 after circulation is carried out for five times;
c3, after five times of circular smoothing, the obtained bandwidth is 3dB bandwidth, and the final bandwidth is f 3dBL ,f 3dBR ]Calculating the center frequency within the range according to the following formula;
Figure BDA0003838764950000111
c4 at f 3dBL To the left, f 3dBR Using the first minimum value point or zero point of the right-seeking power spectrum as the boundary of the main lobe of the signal at f LS ,f RS ]Within the range, signals are zeroed and eliminated, and an out-of-band slope k is calculated according to the formula LS 、k RS ：
Figure BDA0003838764950000121
Figure BDA0003838764950000122
C5 at f LS ，f RS At each slope k LS 、k RS Into the estimated signal band f LS ,f RS ]Extending until the two lines intersect, and reducing damage to the signal which is not estimated;
and C6, if the estimated energy of the C4 is lower than the minimum judgment energy or the bandwidth is smaller than the minimum detection bandwidth, discarding the estimated energy or the bandwidth, and if the maximum amplitude of the residual power spectrum is not higher than a white noise threshold, finishing the signal detection.
EXAMPLE III
As shown in fig. 1 to 7, a method for detecting a broadband signal based on scale iteration and spectrum compensation includes the following steps:
a, receiving signals, carrying out AD sampling to obtain broadband sampling signals, and dividing the broadband signals into a plurality of sections of signals with proper bandwidths through digital channelization;
b, taking several sections of broadband signals, and calculating a maximum power spectrum according to the power spectral density of each section of signal;
estimating and removing a colored noise substrate by using a scale iteration morphological filtering algorithm, and calculating a white noise power threshold;
and D, detecting a signal with the highest amplitude in the power spectrum, and performing the step D again by using the compensated power spectrum if the energy difference value of the residual signal before and after the signal detection twice is higher than a preset energy threshold, otherwise, finishing the signal detection.
In this embodiment, the step a specifically includes the following steps:
a1: calculating the center frequency of a real signal channelized sub-channel
Figure BDA0003838764950000123
D is a data extraction multiple, and the whole frequency band is divided into D symmetrical sub-bands of real signals;
a2: anti-aliasing filter h LP (N) is the low-pass FIR filter, K is the number of channels, D is the decimation factor, and N and D have an integer multiple relationship, i.e., K = FD, then the output of the kth sub-channel in the conventional channelization structure is:
Figure BDA0003838764950000131
a4: centering frequency omega k In the formula, the output expression of the kth sub-channel at this time is:
Figure BDA0003838764950000132
in this embodiment, in step B, the received data is segmented m times, where the length of each segment is L, and the interval between segments is K, and the extracted data may be represented in a matrix form:
Figure BDA0003838764950000133
estimating the power spectrum P of each segment of data by a periodogram method i (f) I.e. by
Figure BDA0003838764950000134
Where w (n) is a selected window function, the power spectrum of each segmented signal is found, and the maximum value of the power spectrum is found at each frequency point, i.e.
Figure BDA0003838764950000135
In this embodiment, the step C specifically includes the following steps:
b1: dimension B bi Dereferencing the minimum detection bandwidth B min For performing morphological filtering PN once 1 (f)；
B2 is the scale B bi Value last filtering scale B bi-1 Twice of the maximum detection bandwidth B, if the size is larger than the maximum detection bandwidth B max Then with B max Performing morphological filtering for the scale, wherein the ith filtering result is PN i (f)；
B3, differentiating the filtering results of the ith and the (i-1) th adjacent two times to obtain a differential result delta PN i Comparing the difference results twice frequency point by frequency point if delta PN i (f)＞ΔPN i-1 (f) Then the scale at the frequency point f is updated to B bi Otherwise, the scale is kept unchanged;
b4, determining the dimension B at each frequency point f by iteration bi (f) Performing morphological filtering once to estimate a colored noise substrate;
b5, subtracting the B5 from the original power spectrum to estimate a noise base to obtain a power spectrum with flat noise base;
b6, converting the power spectrum into a logarithmic power spectrum, dividing a proper number of power bands from 0 to the maximum value on the power spectrum, counting the number of frequency points falling in each band, and taking the upper limit of the power band with the maximum difference result as a white noise power threshold.
In this embodiment, the step D specifically includes the following steps:
c1 finding the position f of the maximum in the power spectrum max As a center frequency f Ctr Amplitude P (f) at the center frequency Ctr ) -3dB calculation of the 3dB bandwidth B 3dB Left boundary of bandwidth is f 3dBL The right boundary is f 3dBR ；
C2, the power spectrum is in f 3dBL ,f 3dBR ]In the scale λ sm ＝0.1*B 3dB Performing smoothingReturning to C1 after filtering treatment, and entering C3 after circulation for five times;
c3, the obtained bandwidth is 3dB bandwidth, and the final f 3dBL ,f 3dBR ]Calculating the center frequency within the range according to the following formula;
Figure BDA0003838764950000141
c4 at f 3dBL To the left, f 3dBR The first minimum value point or zero point of the right-searching power spectrum is used as the boundary of the main lobe of the signal, and the left boundary is f LS The right boundary is f RS In [ f) LS ,f RS ]Within the range, the signal is zeroed and eliminated, and the out-of-band slope k is calculated according to the following formula LS 、k RS ：
Figure BDA0003838764950000142
Figure BDA0003838764950000143
C5 at f LS ，f RS At a slope k LS 、k RS Into the estimated signal band f LS ,f RS ]Extending until two lines intersect, and replacing the in-band spectral line of the estimated signal by two extended lines to reduce the damage to the signal which is not estimated;
and C6, if the estimated energy of the C4 is lower than the minimum judgment energy or the bandwidth is smaller than the minimum detection bandwidth, discarding the estimated energy or the bandwidth, and if the maximum amplitude of the residual power spectrum is not higher than a white noise threshold, finishing the signal detection.
Example four
As shown in fig. 1 to 7, a method for detecting a broadband signal based on scale iteration and spectrum compensation includes the following steps:
a, receiving a signal, and obtaining a broadband sampling signal through AD sampling;
b, acquiring sections of broadband signals, and calculating a maximum power spectrum according to the power spectral density of each section of signal;
estimating and removing a colored noise substrate by using a scale iteration morphological filtering algorithm, and calculating a white noise power threshold;
and D, detecting the signal with the highest amplitude in the power spectrum, estimating the center frequency, the bandwidth and the starting and stopping positions of the signal frequency band, eliminating the signal according to a compensation algorithm, finishing compensation, detecting one signal each time, performing the step D again according to the compensated power spectrum if the energy difference value of the residual signal before and after the signal detection for two times is higher than a preset energy threshold, and otherwise, finishing the signal detection.
In this embodiment, the step a specifically includes the following steps:
a1: calculating the center frequency of a real signal channelized sub-channel
Figure BDA0003838764950000151
D is a data extraction multiple, and the whole frequency band is divided into D symmetrical sub-bands of real signals;
a2: anti-aliasing filter h LP (N) is the low-pass FIR filter, K is the number of channels, D is the decimation factor, and N and D have an integer multiple relationship, i.e., K = FD, then the output of the kth sub-channel in the conventional channelization structure is:
Figure BDA0003838764950000152
a3: the above formula is rewritten as a polyphase filter structure, and the output expression of the kth subchannel obtained is:
Figure BDA0003838764950000161
a4: centering frequency omega k In the formula, the output expression of the kth sub-channel at this time is:
Figure BDA0003838764950000162
in this embodiment, in step B, the received data is segmented m times, the length of each segment is L, the interval between segments is K, and the extracted data can be represented in a matrix form:
Figure BDA0003838764950000163
estimating the power spectrum P of each segment of data by a periodogram method i (f) I.e. by
Figure BDA0003838764950000164
Where w (n) is a selected window function, the power spectrum of each segmented signal is found, and the maximum value of the power spectrum is found at each frequency point, i.e.
Figure BDA0003838764950000165
In this embodiment, the step C specifically includes the following steps:
b1: dimension B bi Dereferencing minimum detection bandwidth B min For performing morphological filtering PN once 1 (f)；
B2 is the scale B bi Value last filtering scale B bi-1 Twice as many as the number of the first time, the ith filtering result is PN i (f)；
B3, differentiating the filtering results of the adjacent two times of the ith and the (i-1) th times if delta PN i (f)＞ΔPN i-1 (f) Then the scale at the frequency point f is updated to be B bi Otherwise, the scale is kept unchanged;
b4 if B bi ＝B max Then stopping iteration;
b5, dimension B determined by iteration at each frequency point f bi (f) Performing primary morphological filtering to estimate a colored noise substrate;
b6, subtracting B5 from the original power spectrum to estimate a noise base to obtain a power spectrum with flat noise base;
b7, converting the power spectrum into a logarithmic power spectrum, wherein the more the power bands are, the more accurate the white noise threshold estimation is, the frequency points of the adjacent power bands are respectively differentiated, and the upper limit of the power band with the maximum differentiation result is used as the white noise power threshold.
In this embodiment, the step D specifically includes the following steps:
c1 finding the position f of the maximum in the power spectrum max As the center frequency f Ctr Amplitude P (f) at the center frequency Ctr ) -3dB calculation of the 3dB bandwidth B 3dB Left boundary of bandwidth of f 3dBL Right boundary is f 3dBR ；
C2, the power spectrum is in f 3dBL ,f 3dBR ]In the scale λ sm ＝0.1*B 3dB Returning to C1 after smooth filtering processing, and entering C3 after circulation for five times;
c3, after five times of circular smoothing, the obtained bandwidth is 3dB bandwidth, and the final bandwidth is f 3dBL ,f 3dBR ]Calculating the center frequency within the range according to the following formula;
Figure BDA0003838764950000171
c4 at f 3dBL To the left, f 3dBR The first minimum value point or zero point of the right-sought power spectrum is used as the boundary of the main lobe of the signal, and the left boundary is f LS Right boundary is f RS In [ f LS ,f RS ]Within the range, the signal is zeroed and eliminated, and the out-of-band slope k is calculated according to the following formula LS 、k RS ：
Figure BDA0003838764950000172
Figure BDA0003838764950000173
C5 at f LS ，f RS At a slope k LS 、k RS Into the estimated signal band f LS ,f RS ]ExtensionUntil the two lines intersect, replacing the in-band spectral line of the estimated signal with the two extension lines, and reducing the damage to the estimated signal;
and C6, if the estimated energy of the C4 is lower than the minimum judgment energy or the bandwidth is smaller than the minimum detection bandwidth, discarding the estimated energy or the bandwidth, and if the maximum amplitude of the residual power spectrum is not higher than a white noise threshold, finishing the signal detection.
EXAMPLE five
As shown in fig. 1 to 7, a method for detecting a broadband signal based on scale iteration and spectrum compensation includes the following steps:
a, receiving signals, carrying out AD sampling to obtain broadband sampling signals, and dividing the broadband signals into a plurality of sections of signals with proper bandwidths through digital channelization;
b, taking several sections of broadband signals, and calculating a maximum power spectrum according to the power spectral density of each section of signal;
estimating and removing a colored noise substrate by using a scale iteration morphological filtering algorithm, and calculating a white noise power threshold;
and D, detecting a signal with the highest amplitude in the power spectrum, estimating the center frequency, the bandwidth and the starting and stopping positions of the signal frequency band, and performing the step D again by using the compensated power spectrum if the energy difference value of the residual signal before and after two times of signal detection is higher than a preset energy threshold, otherwise, finishing the signal detection.
In this embodiment, the step a specifically includes the following steps:
a1: calculating the center frequency of a real signal channelized sub-channel
Figure BDA0003838764950000181
The whole frequency band is divided into D symmetrical sub-bands of real signals;
a2: anti-aliasing filter h LP (N) is a low-pass FIR filter, N has an integer multiple relation to D, i.e., K = FD, then the K-th sub-channel in the conventional channelization structure has the output:
Figure BDA0003838764950000182
a3: the above formula is rewritten as a polyphase filter structure, and the output expression of the k-th sub-channel is obtained as follows:
Figure BDA0003838764950000191
a4: centering frequency omega k In the formula, the output expression of the kth sub-channel at this time is:
Figure BDA0003838764950000192
in this embodiment, in step B, the received data is segmented m times, where the length of each segment is L, and the interval between segments is K, and the extracted data may be represented in a matrix form:
Figure BDA0003838764950000193
estimating the power spectrum P of each segment of data by a periodogram method i (f) I.e. by
Figure BDA0003838764950000194
Where w (n) is a selected window function, the power spectrum of each segmented signal is found, and the maximum value of the power spectrum is found at each frequency point, i.e.
Figure BDA0003838764950000195
In this embodiment, the step C specifically includes the following steps:
b1: dimension B bi Dereferencing the minimum detection bandwidth B min For performing morphological filtering PN once 1 (f)；
B2: dimension B bi Value last filtering scale B bi-1 Twice as many as the first morphological filtering toB max Performing morphological filtering for scale, wherein the ith filtering result is PN i (f)；
B3, differentiating the filtering results of the ith and the (i-1) th adjacent two times if delta PN i (f)＞ΔPN i-1 (f) Then the scale at the frequency point f is updated to be B bi Otherwise, the dimension is kept unchanged;
b4 is if B bi ＝B max Stopping iteration;
b5, determining the dimension B at each frequency point f by iteration bi (f) Performing morphological filtering once to estimate a colored noise substrate;
b6, subtracting B5 from the original power spectrum to estimate a noise base to obtain a power spectrum with flat noise base;
and B7, defining a proper number of power bands from 0 to the maximum value on the power spectrum, counting the number of frequency points falling in each band, and taking the upper limit of the power band with the maximum difference result as a white noise power threshold when the white noise threshold is more accurately estimated.
In this embodiment, the step D specifically includes the following steps:
c1 finding the position f of the maximum in the Power Spectrum max As a center frequency f Ctr At the central frequency amplitude P (f) Ctr ) -3dB calculation of the 3dB bandwidth B 3dB Left boundary of bandwidth of f 3dBL The right boundary is f 3dBR ；
C2, the power spectrum is in f 3dBL ,f 3dBR ]In the dimension λ sm ＝0.1*B 3dB Returning to C1 after smooth filtering processing, and entering C3 after circulation is carried out for five times;
c3, after five times of circular smoothing, the obtained bandwidth is 3dB bandwidth, and the final bandwidth is f 3dBL ,f 3dBR ]Calculating the center frequency within the range according to the following formula;
Figure BDA0003838764950000201
c4 at f 3dBL To the left, f 3dBR The first minimum value point or zero point of the right-sought power spectrum is used as the boundary of the main lobe of the signal, and the left boundary is f LS Right boundary is f RS In [ f LS ,f RS ]Within the range, the signal is zeroed and eliminated, and the out-of-band slope k is calculated according to the following formula LS 、k RS ：
Figure BDA0003838764950000202
Figure BDA0003838764950000203
C5 at f LS ，f RS At a slope k LS 、k RS Into the estimated signal band f LS ,f RS ]Extending until the two lines intersect, and reducing damage to the signal which is not estimated;
and C6, if the estimated energy of the C4 is lower than the minimum judgment energy or the bandwidth is smaller than the minimum detection bandwidth, discarding the estimated energy or the bandwidth, and if the maximum amplitude of the residual power spectrum is not higher than a white noise threshold, finishing the signal detection.
Thus, the signal detection process is shown in fig. 4.
The simulation results of the algorithm performance are shown in fig. 5-7, and the simulation parameters are set as follows: the signal sampling rate is 8MHz, the symbol rate is 2MBaud, and the modulation mode is QPSK. The sampling time is set to 25ms, there are 3 TDMA signals, and each signal burst duration is 0.36ms. The channel environment is additive white gaussian noise.
In order to verify the estimation accuracy of the colored noise base of the present patent, fig. 5 shows the estimation result of the colored noise base of a certain broadband power spectrum and the effect after the noise base is removed. The signal bandwidth is 37.5MHz, the center frequency is 963MHz, and the sampling rate is 75MHz. The noise floor is tightly attached to the fluctuation of the signal, the power spectrum noise floor is flat after colored noise is removed, only white noise is remained, and the number of the signals is unchanged, so that the algorithm has good noise floor estimation performance and is suitable for being used under the fluctuation noise floor condition.
In order to verify the multi-signal detection performance of the present patent, fig. 6 shows a detection result of a certain broadband power spectrum signal. It can be seen that both strong and weak signals can be detected, and when the signals overlap, the strong signal does not affect the detection of the weak signal.
In order to further verify the stability of the performance of the algorithm, the experimental conditions are set as follows: the broadband signal contains 25 sub-signals, the modulation modes comprise BPSK, QPSK, 8PSK, 8QAM and 16QAM, the signal bandwidth range is 2 k-5 MHz, the signal noise ratio of a Gaussian white noise channel is-14-6 dB. Because the energy detection algorithm, the gradient dual-threshold algorithm and the power spectrum cancellation method are greatly influenced by the uneven noise floor, the power spectrum noise floor is the flat noise floor. As can be seen from FIG. 7, the correct detection probability of the algorithm is far higher than that of the classical energy detection algorithm, the same-gradient dual-threshold algorithm and the power spectrum cancellation method have little difference when the signal-to-noise ratio is extremely low, the algorithm is higher than the three algorithms at-10 dB, and the detection probability is higher than 90% when the SNR is larger than or equal to 0 dB.
For a specific calculation process of the wideband signal detection and measurement method based on scale iteration and spectrum compensation, reference may be made to the above embodiments, and details of the embodiments of the present invention are not described herein again.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: it is to be understood that modifications may be made to the technical solutions described in the foregoing embodiments, or equivalents may be substituted for some of the technical features thereof, but such modifications or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (6)

1. A broadband signal detection and measurement method based on scale iteration and spectrum compensation is characterized by comprising the following steps:
a, receiving signals are subjected to AD sampling to obtain broadband sampling signals, and the broadband signals are divided into multiple sections of signals with proper bandwidths through digital channelization;
b, taking several sections of broadband signals, and calculating a maximum power spectrum according to the power spectral density of each section of signal;
estimating and removing a colored noise substrate by using a scale iteration morphological filtering algorithm, and calculating a white noise power threshold;
and D, detecting the signal with the highest amplitude in the power spectrum, estimating the center frequency, the bandwidth and the starting and stopping positions of the signal frequency band, eliminating the signal according to a compensation algorithm, finishing compensation, detecting one signal each time, performing the step D again according to the compensated power spectrum if the energy difference value of the residual signal before and after the signal detection for two times is higher than a preset energy threshold, and otherwise, finishing the signal detection.
2. The wideband signal detection method based on scale iteration and spectrum compensation as claimed in claim 1, wherein the characteristic of morphological filtering bandwidth screening is utilized to perform morphological filtering iteration on the power spectrum, so that the noise floor estimation is more accurate, the error caused by strong pulse and white noise in the signal to the signal parameter estimation is overcome by utilizing smooth iteration, and the detection performance of the band-aliased signal is improved by spectrum compensation.
3. The wideband signal detection method based on scale iteration and spectral compensation according to claim 2, wherein the step a specifically includes the steps of:
a1: calculating center frequencies of real signal channelized sub-channels
Figure FDA0003838764940000011
D is a data extraction multiple, and the whole frequency band is divided into D symmetrical sub-bands of real signals;
a2: anti-aliasing filter h LP (N) is the low-pass FIR filter, K is the number of channels, D is the decimation factor, and N and D have an integer multiple relationship, i.e., K = FD, then the output of the kth sub-channel in the conventional channelization structure is:
Figure FDA0003838764940000021
a3: the above formula is rewritten as a polyphase filter structure, and the output expression of the k-th sub-channel is obtained as follows:
Figure FDA0003838764940000022
a4: centering frequency omega k In the formula, the output expression of the kth sub-channel at this time is:
Figure FDA0003838764940000023
4. the wideband signal detection method based on scale iteration and spectrum compensation according to claim 3, wherein in step B, the received data is segmented m times, each segment has a length of L and an interval of K, and the extracted data can be represented in a matrix form:
Figure FDA0003838764940000024
estimating the power spectrum P of each segment of data by a periodogram method i (f) I.e. by
Figure FDA0003838764940000025
Where w (n) is a selected window function, the power spectrum of each segmented signal is found, and the maximum value of the power spectrum is found at each frequency point, i.e.
Figure FDA0003838764940000026
5. The wideband signal detection method based on scale iteration and spectral compensation according to claim 4, wherein the step C specifically comprises the steps of:
b1: dimension B bi Dereferencing minimum detection bandwidth B min For performing morphological filtering PN once 1 (f)；
B2 is the scale B bi Last filtering scale B bi-1 Twice as many as the maximum detection bandwidth B, if the size is larger than the maximum detection bandwidth max Then with B max Performing morphological filtering for scale, wherein the ith filtering result is PN i (f)；
B3, differentiating the filtering results of the ith and the (i-1) th adjacent two times to obtain a differential result delta PN i Comparing the difference results twice frequency point by frequency point if delta PN i (f)＞ΔPN i-1 (f) Then the scale at the frequency point f is updated to B bi Otherwise, the dimension is kept unchanged;
b4 if B bi ＝B max Then stopping iteration;
b5, determining the dimension B at each frequency point f by iteration bi (f) Performing primary morphological filtering to estimate a colored noise substrate;
b6, subtracting B5 from the original power spectrum to estimate a noise base to obtain a power spectrum with flat noise base;
and B7, converting the power spectrum into a logarithmic power spectrum, defining a proper number of power bands from 0 to the maximum value on the power spectrum, counting the number of frequency points falling in each band, wherein the more the number of the power bands is, the more accurate the white noise threshold estimation is, the frequency point numbers of adjacent power bands are respectively differentiated, and the upper limit of the power band with the maximum differential result is used as the white noise power threshold.
6. The wideband signal detection and measurement method based on scale iteration and spectral compensation according to claim 5, wherein the step D specifically includes the following steps:
c1 finding the position f of the maximum in the power spectrum max As a center frequency f Ctr Amplitude P (f) at the center frequency Ctr ) -3dB calculation of the 3dB bandwidth B 3dB Left boundary of bandwidth is f 3dBL The right boundary is f 3dBR ；
C2, the power spectrum is in f 3dBL ,f 3dBR ]In the dimension λ sm ＝0.1*B 3dB Performing smoothing filteringReturning to C1 after treatment, and entering C3 after five times of circulation;
c3, after five times of circular smoothing, the obtained bandwidth is 3dB bandwidth, and the final bandwidth is f 3dBL ,f 3dBR ]Calculating the center frequency within the range according to the following formula;
Figure FDA0003838764940000031
c4 at f 3dBL To the left, f 3dBR The first minimum value point or zero point of the right-sought power spectrum is used as the boundary of the main lobe of the signal, and the left boundary is f LS The right boundary is f RS In [ f LS ,f RS ]Within the range, the signal is zeroed and eliminated, and the out-of-band slope k is calculated according to the following formula LS 、k RS ：
Figure FDA0003838764940000041
Figure FDA0003838764940000042
C5 at f LS ，f RS At a slope k LS 、k RS Into the estimated signal band f LS ,f RS ]Extending until the two lines intersect, and replacing the in-band spectral line of the estimated signal by the two extension lines to reduce the damage to the estimated signal;
and C6, if the estimated energy of the C4 is lower than the minimum judgment energy or the bandwidth is smaller than the minimum detection bandwidth, discarding the estimated energy or the bandwidth, and if the maximum amplitude of the residual power spectrum is not higher than a white noise threshold, finishing the signal detection.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116430354A (en) * 2023-06-13 2023-07-14 天津大学合肥创新发展研究院 FMCW laser radar target information resolving method and system
CN116430354B (en) * 2023-06-13 2023-08-22 天津大学合肥创新发展研究院 FMCW laser radar target information resolving method and system

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