Signal detection method based on actual noise weak correlation

Abstract

The invention relates to a signal detection method based on actual noise weak correlation, and the method comprises the following steps: S1, setting a sampling rate through a Nyquist sampling theorem according to the bandwidth of a to-be-detected signal; s2, collecting a noise sample and a to-be-tested signal sample according to a set sampling rate; s3, calculating a reference noise signal according to the collected noise sample; s4, setting a delay interval, and calculating a cross-correlation distance between the reference noise signal and the to-be-measured signal sample according to the delay interval; step S5, judging whether the detection threshold can realize detection according to the cross-correlation distance, if so, storing algorithm parameters, and otherwise, returning to the step S1; and step S6, sampling a to-be-detected signal according to the sampling rate stored in the step S5, and performing signal detection on a to-be-detected signal sequence obtained by sampling by using the stored algorithm parameters. According to the invention, the signal detection is completed by using the related information between the signal and the noise, and the probability of signal detection under a low signal-to-noise ratio is improved.

Description

Technical Field

[0001] This invention relates to the field of signal detection technology, and in particular to a signal detection method based on weak correlation of actual noise. Background Technology

[0002] Signal detection is fundamental to subsequent signal processing and plays a crucial role in digital signal processing. Current classic signal detection methods include energy detection and correlation detection. Energy detection algorithms are simple to implement, but their performance deteriorates significantly at low signal-to-noise ratios (SNR). Correlation detection utilizes the uncorrelated nature of noise to achieve signal detection, which can improve detection performance at low SNRs to some extent. However, in real-world environments, background noise is not ideal white Gaussian noise, and noise signals exhibit weak correlations. This means that when the SNR decreases further, correlation detection also fails to meet detection requirements. Summary of the Invention

[0003] To address the technical problems existing in the prior art, the present invention aims to provide a signal detection method based on weak correlation between actual noise, which utilizes the relevant information between signal and noise to complete signal detection, thereby improving the probability of signal detection under low signal-to-noise ratio.

[0004] To achieve the above-mentioned objective, this invention provides a signal detection method based on weak correlation of actual noise, comprising the following steps:

[0005] Step S1: Set the sampling rate according to the bandwidth of the signal to be measured using the Nyquist sampling theorem;

[0006] Step S2: Collect noise samples and the signal to be tested samples according to the set sampling rate;

[0007] Step S3: Calculate the reference noise signal based on the noise sample;

[0008] Step S4: Set a delay interval, and calculate the cross-correlation distance between the reference noise signal and the signal sample under test based on the delay interval;

[0009] Step S5: Based on the calculated cross-correlation distance between the reference noise signal and the signal sample to be tested, select a detection threshold and determine whether the detection threshold can achieve detection. If so, save the sampling rate, the reference noise signal and its sample length, the delay interval and the detection threshold as algorithm parameters; otherwise, return to step S1.

[0010] Step S6: Sample the signal to be tested according to the sampling rate saved in step S5. For the sampled signal sequence, calculate the cross-correlation distance according to the reference noise signal, the sample length and the delay interval saved in step S5. Compare the cross-correlation distance with the detection threshold saved in step S5. If the cross-correlation distance is less than the detection threshold, it indicates that a signal has been detected; otherwise, it indicates that no signal has been detected.

[0011] According to one technical solution of the present invention, step S3 specifically includes:

[0012] According to sample length N s The acquired noise sample sequence N[n] is segmented, and M noise segments are taken to calculate the reference noise signal N. ref [n], the reference noise signal is calculated as follows:

[0013]

[0014] Where M represents the total number of noise segments, k represents the segment number, and N... S Indicates the sample length.

[0015] According to one technical solution of the present invention, in step S4, for any sample sequence X[n] of the signal to be tested, its cross-correlation distance with the reference noise signal is calculated as follows:

[0016]

[0017] Among them, D Cross-Correlation Let X[n] represent the sample sequence of the signal to be tested and N be the reference noise signal. ref The cross-correlation distance of [n]; left and right represent the left and right endpoints of the delay interval, respectively; Let X[n] represent the sample sequence of the signal to be tested and N be the reference noise signal. ref The cross-correlation function of [n] This indicates the complex conjugate operation.

[0018] According to a technical solution of the present invention, in step S4, setting the delay interval specifically includes:

[0019] A cross-correlation operation is performed on the reference noise signal and the test signal sample. The delay interval with the most significant difference in the cross-correlation function between the reference noise signal and the test signal sample is selected to calculate the cross-correlation distance.

[0020] According to one technical solution of the present invention, in step S5, the selection of the detection threshold specifically includes the following steps:

[0021] Step S51: When the signal sample sequence to be tested does not contain burst signals, select multiple signal samples of a preset number of segments from the signal sample sequence to be tested, calculate the cross-correlation distance between the signal sample and the reference noise sample based on the calculated cross-correlation distance value interval [D1, D2].

[0022] Step S52: When the signal sample sequence to be tested contains burst signals, select multiple signal samples of a preset number of segments from the signal sample sequence to be tested, and obtain the cross-correlation distance value range [D3,D4] based on the calculated cross-correlation distance between the signal sample and the reference noise sample.

[0023] Step S53: When the intervals [D1,D2] and [D3,D4] overlap, adjust the parameters, repeat steps S51 and S52, and calculate the new cross-correlation distance range.

[0024] Step S54: When the intervals [D1,D2] and [D3,D4] do not overlap, select the value in the interval (D2,D3) as the detection threshold. The larger the interval interval D3-D2, the better the detection performance.

[0025] According to a technical solution of the present invention, in step S53, the principle of parameter adjustment is as follows: when the cross-correlation distance value ranges overlap, firstly consider increasing the number of noise sample segments used to calculate the reference noise signal, and secondly consider increasing the sample length.

[0026] According to a technical solution of the present invention, in step S5, the selection principle of the algorithm parameters is as follows:

[0027] Choose a smaller sampling rate without causing aliasing;

[0028] Choose an appropriate sample length based on the balance between detection performance and computational load according to actual needs.

[0029] According to one aspect of the present invention, an electronic device includes: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory. When the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform the above-described signal detection method based on weak correlation of actual noise.

[0030] According to one aspect of the present invention, a computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the above-described signal detection method based on weak correlation of actual noise.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] This invention proposes a signal detection method based on the weak correlation of actual noise. Utilizing the weak correlation characteristic of noise, signal detection is achieved by assessing the difference in correlation between noise samples and the target signal sample relative to reference noise. To fully characterize this difference, the concept of cross-correlation distance is introduced. The sum of squares of the cross-correlation function within a specific interval is used as a statistical determinant. This statistical determinant is compared with a threshold to determine whether a signal has been detected, thereby significantly improving the probability of signal detection under low signal-to-noise ratio (SNR) conditions and effectively addressing the problem of poor detection performance of current algorithms at low SNR. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0034] Figure 1 A flowchart illustrating a signal detection method based on weak correlation of actual noise provided in an embodiment of the present invention;

[0035] Figure 2 The flowchart illustrates the acquisition of algorithm parameters in the signal detection method provided in an embodiment of the present invention. Detailed Implementation

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0037] like Figure 1 and Figure 2 As shown, this invention provides a signal detection method based on weak correlation of actual noise, comprising the following steps:

[0038] Step S1: Set the sampling rate according to the bandwidth of the signal to be measured using the Nyquist sampling theorem;

[0039] Step S2: Collect noise samples and the signal to be tested samples according to the set sampling rate;

[0040] The signal sample to be tested is the same signal as the signal to be tested in the final signal detection. The signal sample to be tested can be a segment of the signal to be tested or a separately acquired signal sequence.

[0041] Step S3: Calculate the reference noise signal based on the noise sample;

[0042] Step S3 specifically includes:

[0043] According to sample length N s The acquired noise sample sequence N[n] is segmented, and M noise segments are taken to calculate the reference noise signal N. ref [n], the reference noise signal is calculated as follows:

[0044]

[0045] Where M represents the number of noise segments, k represents the segment number, and N... S Indicates the sample length. The noise sequence N[n] is a discrete time signal, where n is an integer representing the time index of the signal.

[0046] Step S4: Set a delay interval, and calculate the cross-correlation distance between the reference noise signal and the signal sample under test based on the delay interval;

[0047] In step S4, cross-correlation is first performed on the reference noise signal and the sample signal to be tested. The delay interval with the most significant difference between the cross-correlation functions of the reference noise signal and the sample signal to be tested is selected to calculate the cross-correlation distance.

[0048] In step S4, for any sample sequence X[n] of the signal to be tested, its cross-correlation distance with the reference noise is calculated as follows:

[0049]

[0050] Among them, D Cross-Correlation Let X[n] represent the sample sequence of the signal to be tested and N be the reference noise signal. ref The cross-correlation distance of [n]; left and right represent the left and right endpoints of the delay interval, respectively; Let X[n] represent the sample sequence of the signal to be tested and N be the reference noise signal. ref The cross-correlation function of [n] This indicates the complex conjugate operation.

[0051] Step S5: Based on the calculated cross-correlation distance between the reference noise signal and the signal sample to be tested, select the detection threshold and determine whether the detection threshold can achieve detection. If so, save the sampling rate, sample length, reference noise signal, delay interval and detection threshold as algorithm parameters; otherwise, return to step S1.

[0052] In step S5, selecting the detection threshold specifically includes:

[0053] Step S51: When the signal sequence to be tested does not contain burst signals, arbitrarily select multiple signal samples of a preset number of segments (e.g., 1000), calculate their cross-correlation distance with the reference noise, and obtain the cross-correlation distance value range [D1, D2].

[0054] Step S52: When the signal sequence to be tested contains burst signals, calculate the cross-correlation distance between the multiple signal samples of the preset number of segments (e.g., 1000) and the reference noise to obtain the cross-correlation distance value range [D3, D4].

[0055] Step S53: When the intervals [D1,D2] and [D3,D4] overlap, adjust the parameters, repeat steps S51 and S52, and calculate the new cross-correlation distance range.

[0056] The principle for parameter adjustment is as follows: when the cross-correlation distance value intervals overlap, first consider increasing the number of noise sample segments used to calculate the reference noise signal, and secondly consider increasing the sample length. When two cross-correlation distance value intervals overlap, first consider increasing the number of noise sample segments, repeat steps S51 and S52, calculate the cross-correlation distance value intervals, and if overlap still exists, consider increasing the sample length and recalculating.

[0057] Step S54: When the intervals [D1,D2] and [D3,D4] do not overlap, select the value in the interval (D2,D3) as the detection threshold. The larger the interval interval D3-D2, the better the detection performance.

[0058] In step S5, the selection principle for algorithm parameters is as follows:

[0059] a) Choose a smaller sampling rate without causing aliasing;

[0060] When the sample length is the same, a smaller sampling rate corresponds to a longer effective signal duration, and the reference noise signal can contain more noise features, thereby improving detection performance.

[0061] b) When the cross-correlation distance intervals overlap, first consider increasing the number of noise sample segments used to calculate the reference noise signal, and secondly consider increasing the sample length.

[0062] Depending on how the reference noise signal is calculated, increasing the sample length can allow the reference noise signal to contain more noise features, but this will increase the computational load. Another better approach is to increase the number of sample segments used to calculate the reference noise signal, which can achieve the same goal without adding extra computational burden.

[0063] c) Balance detection performance and computational load according to actual needs, and select an appropriate sample length;

[0064] Increasing the sample length can improve detection performance, but the improvement gradually decreases as the sample length increases, and it also leads to an increase in computational load. Therefore, it is necessary to balance detection performance and computational load according to actual needs.

[0065] d) Select the delay interval where the difference in the cross-correlation function between the reference noise signal and the sample under test is most significant, and use it to calculate the cross-correlation distance.

[0066] Excessively long delay intervals can lead to false triggering problems. That is, when the noise contains a small number of non-signal anomalies, it will be mistakenly detected as the signal to be tested. Therefore, the interval with the most significant difference between the cross-correlation functions of the reference noise signal and the sample signal to be tested should be selected for calculating the cross-correlation distance.

[0067] Step S6: Detect the signal to be tested: Sample the signal to be tested according to the sampling rate saved in step S5. For the sampled signal sequence, calculate the cross-correlation distance according to the sample length, reference noise and delay interval saved in step S5. Compare the cross-correlation distance with the detection threshold saved in step S5. If the cross-correlation distance is less than the threshold, it indicates that a signal has been detected; otherwise, it indicates that no signal has been detected.

[0068] This invention proposes the concept of cross-correlation distance based on the weak correlation of noise signals in real-world environments. Signal detection is achieved by considering the difference in cross-correlation distances between noise and the target signal and reference noise signals. For different environmental noises and target signals, different detection thresholds can be obtained by setting the sampling rate, sample length, and delay interval, thereby achieving accurate and effective signal detection under low signal-to-noise ratio conditions.

[0069] According to one aspect of the present invention, an electronic device is provided, comprising: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, and the one or more computer programs are stored in the memory; when the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform a signal detection method based on actual noise weak correlation as described in any of the above technical solutions.

[0070] The processor can be a central processing unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0071] According to one aspect of the present invention, a computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement a signal detection method based on weak correlation of actual noise as described above.

[0072] Computer-readable storage media can include any medium capable of storing or transmitting information. Examples of computer-readable storage media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and so on. Code segments can be downloaded via computer networks such as the Internet and intranets.

[0073] Furthermore, it should be noted that the present invention can be provided as a method, apparatus, or computer program product. Therefore, embodiments of the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0074] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0075] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0076] It should also be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0077] Finally, it should be noted that the above description represents a preferred embodiment of the present invention. It should be pointed out that although preferred embodiments have been described, those skilled in the art, once they understand the basic inventive concept of the present invention, can make various improvements and modifications without departing from the principles described herein. These improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

[0078] It should be noted that the above description represents a preferred embodiment of the present invention. While preferred embodiments have been described, those skilled in the art, upon understanding the basic inventive concept of the present invention, can make various improvements and modifications without departing from the principles described herein. These improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to include both the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

Claims

1. A signal detection method based on weak correlation of actual noise, characterized in that, Includes the following steps: Step S1: Set the sampling rate according to the bandwidth of the signal to be measured using the Nyquist sampling theorem; Step S2: Collect noise samples and the signal to be tested samples according to the set sampling rate; Step S3: Calculate the reference noise signal based on the noise sample; Step S4: Set a delay interval, and calculate the cross-correlation distance between the reference noise signal and the signal sample under test based on the delay interval; Step S5: Based on the calculated cross-correlation distance between the reference noise signal and the signal sample to be tested, select a detection threshold and determine whether the detection threshold can achieve detection. If so, save the sampling rate, the reference noise signal, the sample length, the delay interval, and the detection threshold as algorithm parameters; otherwise, return to step S1. Step S6: Sample the signal to be tested according to the sampling rate saved in step S5. For the sampled signal sequence, calculate the cross-correlation distance according to the reference noise signal, the sample length, and the delay interval saved in step S5. Compare the cross-correlation distance with the detection threshold saved in step S5. If the cross-correlation distance is less than the detection threshold, it indicates that a signal has been detected; otherwise, it indicates that no signal has been detected.

2. The signal detection method based on weak correlation of actual noise according to claim 1, characterized in that, Step S3 specifically includes: According to sample length N s The acquired noise sample sequence N[n] is segmented, and M noise segments are taken to calculate the reference noise signal N. ref [n], the reference noise signal is calculated as follows: Where M represents the number of noise segments, k represents the segment number, and N... S Indicates the sample length.

3. The signal detection method based on weak correlation of actual noise according to claim 2, characterized in that, In step S4, for any sample sequence X[n] of the signal to be tested, its cross-correlation distance with the reference noise signal is calculated as follows: Among them, D Cross-Correlation Let X[n] represent the sample sequence of the signal to be tested and N be the reference noise signal. ref The cross-correlation distance of [n]; left and right represent the left and right endpoints of the delay interval, respectively; Let X[n] represent the sample sequence of the signal to be tested and N be the reference noise signal. ref The cross-correlation function of [n] This indicates the complex conjugate operation.

4. The signal detection method based on weak correlation of actual noise according to claim 3, characterized in that, In step S4, setting the delay interval specifically includes: A cross-correlation operation is performed on the reference noise signal and the test signal sample. The delay interval with the most significant difference in the cross-correlation function between the reference noise signal and the test signal sample is selected to calculate the cross-correlation distance.

5. The signal detection method based on weak correlation of actual noise according to claim 3, characterized in that, In step S5, selecting the detection threshold specifically includes the following steps: Step S51: When the signal sample sequence to be tested does not contain burst signals, select multiple signal samples of a preset number of segments from the signal sample sequence to be tested, and obtain the cross-correlation distance value range [D1,D2] based on the calculated cross-correlation distance between the signal sample and the reference noise sample. Step S52: When the signal sample sequence to be tested contains burst signals, select multiple signal samples of a preset number of segments from the signal sample sequence to be tested, and obtain the cross-correlation distance value range [D3,D4] based on the calculated cross-correlation distance between the signal sample and the reference noise sample. Step S53: When the intervals [D1,D2] and [D3,D4] overlap, adjust the parameters, repeat steps S51 and S52, and calculate the new cross-correlation distance range. Step S54: When the intervals [D1,D2] and [D3,D4] do not overlap, select the value in the interval (D2,D3) as the detection threshold.

6. The signal detection method based on weak correlation of actual noise according to claim 5, characterized in that, In step S53, the principle of parameter adjustment is as follows: when the cross-correlation distance value ranges overlap, first consider increasing the number of noise sample segments used to calculate the reference noise signal, and second consider increasing the sample length.

7. The signal detection method based on weak correlation of actual noise according to claim 5, characterized in that, In step S5, the selection principle for the algorithm parameters is as follows: Choose a smaller sampling rate without causing aliasing; Choose an appropriate sample length based on the balance between detection performance and computational load according to actual needs.

8. An electronic device, characterized in that, include: One or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory to cause the electronic device to perform the signal detection method based on actual noise weak correlation as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, implement the signal detection method based on actual noise weak correlation as described in any one of claims 1 to 7.

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