Multi-target collaborative signal detection method, device and storage medium

Abstract

The present invention provides a multi-target collaborative signal detection method, comprising: obtaining received signals based on the same signal source, determining the number of signal subbands and the bandwidth of the signal subbands in each received signal in the same manner; using the FGoF algorithm to perform spectrum sensing on the information between the signal subbands of each received signal to obtain the detection probability of each signal subband on the received signal; collaboratively fusing the detection probabilities of signal subbands with the same sequence number on each received signal to obtain the fusion probability of the signal subband with the sequence number; when the fusion probability of the signal subband is greater than the false alarm probability, determining that the signal subband is a valid signal; otherwise, determining that the signal subband is a noise signal. The present invention utilizes the universality of FGoF for noise detection to achieve multi-target signal detection in various non-Gaussian noise situations, and can also ensure that multiple signals under the same broadband conditions are not missed, thereby greatly improving signal detection efficiency.

Description

Technical Field

[0001] The present invention relates to a multi-target cooperative signal detection method, and belongs to the technical field of spectrum sensing. Background Art

[0002] Traditional cognitive radio signal detection methods include energy detection, matched filtering, eigenvalue detection, and cyclostationary methods. While energy detection offers low detection time overhead and algorithmic complexity, its detection performance is susceptible to dynamic background noise. Matched filtering offers the best detection performance and is fast, but requires a large amount of prior information, making practical applications difficult. While eigenvalue detection offers good robustness, it lacks a specific closed-form threshold solution and exhibits high algorithmic complexity. While cyclostationary detection also offers strong robustness, it also requires a high time overhead.

[0003] Most detection methods are generally applicable to background noise under Gaussian conditions. However, in reality, when affected by interference and other factors, background noise often exhibits the dynamic characteristics of non-Gaussian noise. Therefore, building a model based on Gaussian noise does not meet some practical needs. Therefore, it is necessary to build a model under non-Gaussian noise types. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a multi-target collaborative signal detection method that can achieve broadband multi-target detection in a complex electromagnetic environment and accurately detect each target signal.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] In a first aspect, the present invention provides a multi-target cooperative signal detection method, comprising:

[0007] Get based on the same signal source A received signal, the The received signal is preset by Detection nodes are generated;

[0008] Determining the number of signal subbands in each received signal and the bandwidth of each signal subband in the same manner;

[0009] For each received signal, the frequency domain goodness of fit FGoF algorithm is used to perform spectrum sensing on the information between each signal sub-band to obtain the detection probability of each signal sub-band on the received signal;

[0010] According to the order of each signal subband on each received signal, the detection probabilities of the signal subbands with the same sequence number are collaboratively integrated to obtain the fusion probability of the signal subband with the sequence number;

[0011] The decision is made by comparing the fusion probability of each signal subband with the preset false alarm probability, including:

[0012] In response to the fusion probability being less than the false alarm probability, determining that the signal subband is a noise signal;

[0013] In response to the fusion probability being not less than the false alarm probability, the signal sub-band is determined to be a valid signal.

[0014] Furthermore, the determining of the number of signal subbands in each received signal and the bandwidth of each signal subband includes: for any received signal,

[0015] Sample the received signal at a preset sampling rate to obtain the total number of samples contained in the received signal ;

[0016] According to the total number of samples Set the number of signal subbands and the bandwidth of each signal subband. The formula is:

[0017] ;

[0018] Where, is the bandwidth of each signal subband, and is an integer.

[0019] Furthermore, the use of the frequency domain goodness of fit FGoF algorithm to perform spectrum sensing on information between each signal sub-band to obtain the detection probability of each signal sub-band on the received signal includes:

[0020] S1: For each received signal Perform fast Fourier transform to obtain the corresponding frequency domain signal , the formula is:

[0021] ;

[0022] Where, ; ;

[0023] S2: Obtain the spectrum of each signal subband in each received signal. The formula is:

[0024] Xm , r = [ X m ( ( r − 1 ) N 2 R ) X m ( ( r − 1 ) N 2 R + 1 ) … X m ( rN 2 R − 1 ) ] ;

[0025] Where, ; For the The received signal The spectrum of the signal sub-band;

[0026] S3: Calculate the power spectrum of each signal subband in each received signal. The formula is:

[0027] ;

[0028] Where, For the The received signal The power spectrum of the signal sub-band;

[0029] S4: Use KS test to obtain the KS statistic of each signal subband, and then obtain the maximum value of KS statistic ,include:

[0030] S41: Determine the first sample of KS test and the second sample :

[0031]

[0032] B m , r = [ Y m , 1 Y m , 2 … Y m , r − 1 Y m , r + 1 … Y m , R ] ;

[0033] S42: Acquire Cumulative distribution function of and Cumulative distribution function of , and then calculate the KS statistic , and take Assign the maximum value , the formula is:

[0034] ;

[0035] ;

[0036] Where, For the The received signal KS statistics of the signal subband; For the first sample The number of samples included, For the second sample The number of samples included; For the The current threshold of the received signal; For the The first received signal The assumption that the signal sub-band is a valid signal; For the The first received signal The assumption that the signal subbands are noise signals;

[0037] S5: In response to ;calculate The detection probability of the corresponding signal sub-band is calculated, and the samples contained in the signal sub-band are deleted to obtain The new received signal is composed of signal sub-bands, and endowment , return to S4;

[0038] In response to , the loop ends and the detection probability of all remaining signal subbands is calculated.

[0039] Furthermore, the The calculation formula is:

[0040] ;

[0041] P f = ψ ( [ N ' + 0 . 12 + 0 . 11 N ' ] η m ) ;

[0042] Where, , is the equivalent sample size, is the false alarm probability.

[0043] Furthermore, the detection probability of the signal sub-band is calculated by The probability of success is obtained, and the calculation formula is:

[0044] .

[0045] Furthermore, the collaborative fusion of the detection probabilities of the signal subbands with the same sequence number includes: using the Fisher combination probability to combine the detection probabilities of the subbands of each received signal with the detection probabilities of the subbands of the same sequence number. The detection probabilities of the signal sub-bands are collaboratively integrated to obtain the The fusion probability of signal sub-bands is:

[0046] ;

[0047] Where, For the The received signal The detection probability of a signal subband.

[0048] In a second aspect, the present invention provides an electronic device comprising a processor and a memory connected to the processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method described in the first aspect is implemented.

[0049] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in the first aspect.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The multi-target collaborative signal detection method provided by the present invention, on the one hand, uses the frequency domain goodness of fit to test the universality of FGoF for noise detection, starting from the dynamic background environment and non-ideal communication channel of each detection node, is not affected by dynamic noise, and can realize multi-target signal detection under various non-Gaussian noise conditions; on the other hand, the method provided by the present invention does not need to rely on other prior information, uses the information between the signal sub-bands of the entire broadband for blind detection, and uses the power spectrum information of each signal sub-band and the remaining signal sub-bands through goodness of fit to mine the correlation between the signal sub-bands, screen out the signal sub-band with the most serious cumulative distribution deviation, and compare it with the threshold, remove the signal sub-band with the most serious cumulative distribution deviation and exceeding the threshold, reduce its influence on the detection effect of the remaining signal sub-bands, so that multiple signals under the same broadband conditions will not be missed, and greatly improve the signal detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 1 is a flow chart of multi-target cooperative signal detection in Example 1 of the present invention;

[0053] Figure 2 is the SNR-P of the method in Example 2 of the present invention at different signal strengths d picture;

[0054] Figure 3 is the SNR-P of the method in Example 3 of the present invention when the number of signals is different d picture;

[0055] Figure 4 1 is an ROC curve diagram of the method in Example 4 of the present invention when the number of detection nodes is different;

[0056] Figure 5 3 is an ROC curve diagram of the method under different dynamic noises in Example 5 of the present invention. DETAILED DESCRIPTION

[0057] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments.

[0058] Example 1

[0059] According to a first aspect of the present invention, this embodiment provides a multi-target cooperative signal detection method, including:

[0060] Step 1: Get the signal from the same source A received signal, the The received signal is preset by A detection node is generated.

[0061] Specifically, such as Figure 1 As shown, the application scenario of this embodiment includes a primary user PU and Cognitive users SU1~SU M In the cognitive radio network, the signal source is generated by the primary user PU. Cognitive users SU1~SU M form A detection node receives the signal from the primary user and generates A received signal.

[0062] Step 2: Determine the number of signal sub-bands in each received signal and the bandwidth of each signal sub-band in the same manner.

[0063] Specifically, first, the same sampling rate is used to The received signals are sampled and each received signal generates discrete samples , and then set the number of signal subbands and the bandwidth of the signal subband for each received signal according to the following formula:

[0064] ;

[0065] Where, is the bandwidth of the signal subband, and is an integer.

[0066] Step 3: For each received signal, use the frequency domain goodness of fit (FGoF) algorithm to perform spectrum sensing on the information between each signal subband to obtain the detection probability of each signal subband on the received signal, including:

[0067] Step 31: For each received signal Perform fast Fourier transform to obtain the corresponding frequency domain signal , the formula is:

[0068] ;

[0069] Where, ;

[0070] Step 32: Obtain the spectrum of each signal subband in each received signal by periodogram method , the formula is:

[0071] Xm , r = [ X m ( ( r − 1 ) N 2 R ) X m ( ( r − 1 ) N 2 R + 1 ) … X m ( rN 2 R − 1 ) ] ;

[0072] Where, ;

[0073] Step 33: Obtain the power spectrum of each signal subband in each received signal , the formula is:

[0074] ;

[0075] Step 34: Use the KS test to obtain the KS statistic of each signal subband, and then obtain the maximum value of the KS statistic ,include:

[0076] Step 341: Determine the first sample for the KS test and the second sample :

[0077] ;

[0078] B m , r = [ Y m , 1 Y m , 2 … Y m , r − 1 Y m , r + 1 … Y m , R ] ;

[0079] Step 342: Get Cumulative distribution function of and Cumulative distribution function of , and then calculate the KS statistic , and take Assign the maximum value , the formula is:

[0080] ;

[0081] ;

[0082] Where, For the first sample The number of samples included, For the second sample The number of samples included; For the The current threshold of the received signal; For the The first received signal The assumption that the signal sub-band is a valid signal; For the The first received signal The assumption that the signal subbands are noise signals.

[0083] Step 35: Respond to ;calculate The detection probability of the corresponding signal sub-band is calculated, and the samples contained in the signal sub-band are deleted to obtain The new received signal is composed of signal sub-bands, and endowment , return to step 34; in response to , the loop ends and the detection probability of all remaining signal subbands is calculated.

[0084] It should be noted that each signal sub-band After calculation, it is necessary to compare it with the current threshold For comparison, if ,but established if ,but Established.

[0085] Specifically, in the first calculation, the signal sub-band whose cumulative distribution deviates most seriously and exceeds the current threshold is screened out, the detection probability of the signal sub-band is calculated, and the samples contained in the signal sub-band are removed. In this way, the second calculation can screen out the signal sub-band whose cumulative distribution deviates second seriously and exceeds the current threshold. The calculation is repeated in this way to screen out the signal sub-bands whose cumulative distribution deviates seriously and exceeds the threshold in turn.

[0086] Preferably, The calculation formula is:

[0087] ;

[0088] P f = ψ ( [ N ' + 0 . 12 + 0 . 11 N ' ] η m ) ;

[0089] Where, , is the equivalent sample size, is the false alarm probability.

[0090] It should be noted that in each cycle calculation, since the samples contained in a signal subband are removed in the previous cycle, 、 、 The value of will change.

[0091] Preferably, the detection probability of the signal subband is calculated by The probability of success is obtained, and the calculation formula is:

[0092] ;

[0093] Step 4: Use Fisher combining probability to combine the first The detection probabilities of the signal sub-bands are collaboratively integrated to obtain the The fusion probability of signal sub-bands is:

[0094] ;

[0095] Where, For the The received signal The detection probability of a signal subband.

[0096] Step 5: Make a decision by comparing the fusion probability of each signal sub-band with the preset false alarm probability, including: The fusion probability of the signal sub-band is less than the false alarm probability, then the The signal sub-band is a noise signal; in response to the The fusion probability of the signal sub-band is not less than the false alarm probability, then the signal sub-bands are valid signals.

[0097] Example 2

[0098] This embodiment provides the performance of the method of the present invention in different signal strengths. This embodiment analyzes the detection results of the third signal sub-band (k=3) at different signal strengths and the seventh signal sub-band (k=7) at the same signal strength, and obtains the following: Figure 2The SNR-Pd graph shown in the figure shows that the detection probability of the signal in the 7th signal subband decreases significantly as the signal strength changes from amplitude = 1, amplitude = 0.7, and amplitude = 0.4. However, the detection probability of the signal in the 3rd signal subband is also relatively stable at a stable signal strength, indicating that changes in signal strength have a significant impact on the signal detection results. At the same time, at the same amplitude strength (amplitude = 1), there is no significant difference in the detection probability between the 3rd signal subband and the 7th signal subband.

[0099] In summary, the method of the present invention can accurately detect multiple signals in a wide band and has excellent robustness in a dynamic noise environment.

[0100] Example 3

[0101] This embodiment provides the performance of the method of the present invention when different numbers of signals are included in the broadband frequency band. This embodiment analyzes the detection results of the broadband frequency band containing 1 signal (single), 2 signals (double) and 3 signals (treble), among which the false alarm probability is is 0.05. Figure 3 As shown in the SNR-Pd diagram, it can be seen that: although the signal detection performance decreases with the increase in the number of signals in the frequency band, the decrease is very small. Even when the signal-to-noise ratio is around -9dB, all signals in the broadband frequency band can be basically detected, and multiple sub-band signals under the same broadband conditions will not be missed due to other reasons, which greatly improves the signal detection efficiency.

[0102] Example 4

[0103] This embodiment provides the performance of the method of the present invention when different numbers of detection nodes are used. In this embodiment, when the signal-to-noise ratio is -12dB and the transmitted signal is a dual signal, the number of detection nodes is 1 ( ), 3 ( ) and 5 ( ), the detection results of the 3rd signal sub-band (r=3) and the 7th signal sub-band (r=7) are analyzed to obtain the following Figure 4 As shown in the ROC curve, it can be seen from the figure that: with the increase in the number of detection nodes, the signal detection performance has been significantly improved, but there is a problem that the time cost of collaboration increases due to the increase in the number of detection nodes. Combining the values ​​of the number of detection nodes, it can be found that The collaborative efficiency can be greatly improved while reducing time costs. At the same time, it was found that a false alarm probability of 0.05 can guarantee good performance and provide relatively accurate detection capabilities.

[0104] Example 5

[0105] This embodiment provides the performance of the method of the present invention under different dynamic noise conditions. This embodiment establishes dynamic background noises of different states by setting different noise uncertainties and noise model errors, and obtains the following: Figure 5 As shown in the ROC curve, it can be seen from the figure that the algorithm has strong stability, is not affected by generalized Gaussian noise, and has the advantage of being suitable for broadband multi-target perception.

[0106] Example 6

[0107] According to the second aspect of the present invention, an electronic terminal is also provided, comprising a processor and a memory connected to the processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the multi-target collaborative signal detection method as described in Example 1 is implemented.

[0108] Example 7

[0109] According to a third aspect of the present invention, a computer-readable storage medium is provided in this embodiment, on which a computer program is stored. When the computer program is executed by a processor, the multi-target cooperative signal detection method described in Example 1 is implemented. The storage medium may include, for example, a storage component of a tablet computer, a computer hard disk, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer storage medium may be any combination of one or more computer storage media.

[0110] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-target cooperative signal detection method, characterized in that: include: Get based on the same signal source A received signal, the The received signal is preset by Detection nodes are generated; Determining the number of signal subbands in each received signal and the bandwidth of each signal subband in the same manner; For each received signal, the frequency domain goodness of fit FGoF algorithm is used to perform spectrum sensing on the information between each signal sub-band to obtain the detection probability of each signal sub-band on the received signal; According to the order of each signal subband on each received signal, the detection probabilities of the signal subbands with the same sequence number are collaboratively integrated to obtain the fusion probability of the signal subband with the sequence number; The decision is made by comparing the fusion probability of each signal subband with the preset false alarm probability, including: In response to the fusion probability being less than the false alarm probability, determining that the signal subband is a noise signal; In response to the fusion probability being not less than the false alarm probability, the signal sub-band is determined to be a valid signal.

2. The multi-target cooperative signal detection method according to claim 1, wherein: The determining the number of signal subbands in each received signal and the bandwidth of each signal subband includes: For any received signal, Sample the received signal at a preset sampling rate to obtain the total number of samples contained in the received signal ; According to the total number of samples Set the number of signal subbands and the bandwidth of each signal subband. The formula is: ； Where, is the bandwidth of each signal subband, and is an integer.

3. The multi-target cooperative signal detection method according to claim 2, wherein: The method of using the frequency domain goodness of fit (FGoF) algorithm to perform spectrum sensing on information between each signal sub-band to obtain a detection probability of each signal sub-band on the received signal includes: S1: For each received signal Perform fast Fourier transform to obtain the corresponding frequency domain signal , the formula is: ； Where, ; ; S2: Obtain the spectrum of each signal subband in each received signal. The formula is: ； Where, ; For the The received signal The spectrum of the signal sub-band; S3: Calculate the power spectrum of each signal subband in each received signal. The formula is: ； Where, For the The received signal The power spectrum of the signal sub-band; S4: Use KS test to obtain the KS statistic of each signal subband, and then obtain the maximum value of KS statistic ,include: S41: Determine the first sample of KS test and the second sample : ； ； S42: Acquire Cumulative distribution function of and Cumulative distribution function of , and then calculate the KS statistic , and take Assign the maximum value , the formula is: ； ； Where, For the The received signal KS statistics of the signal subband; For the first sample The number of samples included, For the second sample The number of samples included; For the The current threshold of the received signal; For the The first received signal The assumption that the signal sub-band is a valid signal; For the The first received signal The assumption that the signal subbands are noise signals; S5: In response to ;calculate The detection probability of the corresponding signal sub-band is calculated, and the samples contained in the signal sub-band are deleted to obtain The new received signal is composed of signal sub-bands, and endowment , return to S4; In response to , the loop ends and the detection probability of all remaining signal subbands is calculated.

4. The multi-target cooperative signal detection method according to claim 3, wherein: The calculation formula is: ； ； Where, , is the equivalent sample size, is the false alarm probability.

5. The multi-target cooperative signal detection method according to claim 3, wherein: The detection probability of the signal subband is calculated by The probability of success is obtained, and the calculation formula is: 。 6. The multi-target cooperative signal detection method according to claim 5, wherein: The collaborative fusion of the detection probabilities of the signal sub-bands with the same sequence number includes: using the Fisher combination probability to combine the detection probabilities of the sub-bands of each received signal with the detection probabilities of the sub-bands of the same sequence number. The detection probabilities of the signal sub-bands are collaboratively integrated to obtain the The fusion probability of signal sub-bands is: ； Where, For the The received signal The detection probability of a signal subband.

7. An electronic device, characterized in that: It includes a processor and a memory connected to the processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the multi-target collaborative signal detection method according to any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-target cooperative signal detection method according to any one of claims 1 to 6 is implemented.

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