A verification system and method for underdetermined blind source separation algorithm of communication signal based on Yagi antenna.

Abstract

This embodiment provides a verification system and method for an underdetermined blind source separation algorithm based on a Yagi antenna. A blind source signal acquisition system is designed using a Yagi antenna, a superheterodyne receiver, and data acquisition software. Standard signal sources and walkie-talkies are used as experimental blind source signals. This blind source signal acquisition system has two acquisition channels. By setting the signal source and receiving frequency, it can simultaneously acquire intermediate frequency (IF) data from multiple source signals within the real-time bandwidth. The sampled IF data is used as input data for the algorithm to compare and verify the source number estimation and source signal recovery effects of various underdetermined blind source separation algorithms.

Description

Technical Field

[0001] This embodiment relates to the field of communication technology, specifically to a verification system and method for an underdetermined blind source separation algorithm for communication signals based on a Yagi antenna. Background Technology

[0002] Electromagnetic environments contain a wide variety of non-cooperative signals, as well as in-band interference and even co-channel interference signals that are difficult to fully grasp with prior knowledge. Finding the desired signal quickly and accurately from this complex array of signals is typically very difficult, posing significant challenges to the identification and denial of illegal radio stations in secure wireless communication, the location and separation of interference sources in electromagnetic compatibility testing, situational awareness and feature extraction in electromagnetic environment detection, and interference detection and cognitive access in electromagnetic spectrum management.

[0003] The diverse electromagnetic signals in the electromagnetic environment are typically emitted from multiple radiation sources. Because the specific characteristics of these source signals are unknown beforehand, they are called "blind source signals." Furthermore, in most cases, their specific propagation characteristics in the channel are also unknown. Therefore, "blind source signals" involve both "blind" sources and "blind" paths or channels. The process of finding the "desired" or "target" signal from complex mixed signals is commonly called "blind source separation" (BSS). BSS technology requires little prior knowledge and can quickly and accurately sort, identify, and estimate source signals based on the statistical characteristics of the observed (received) signals from the "blind sources," even when the characteristics of the source signals and the channel are unknown. Blind source separation (BSS) is a technique for extracting the original signal from multiple mixed signals without prior knowledge of the signal source or mixing process. BSS technology has broad application prospects in fields such as communication, audio processing, and biomedical signal processing. However, when the number of mixed signals exceeds the number of sensors, it becomes the Underdetermined Blind Source Separation (UBSS) problem, which is much more difficult to solve. With the development of neural networks and other technologies, UBSS algorithms have gradually matured. However, the verification of most UBSS algorithms mainly relies on simulation data. Simulated signals and matrices are used to simulate mixed channels and mixed signals. Simulation experiments are conducted in a fully controlled environment, allowing for precise setting of signal parameters, mixing matrices, noise levels, etc., without external interference. However, this cannot fully reflect the complexity and uncertainty of the real environment.

[0004] Existing technologies for underdetermined blind source separation algorithms for communication signals face the challenge of collecting mixed communication signals from various blind sources. In reality, this involves numerous noise and interference sources, and the mixing matrix is ​​unknown. Current underdetermined blind source separation algorithms for communication or radar signals typically utilize MATLAB to simulate signals and randomly generate matrices to model the mixing matrix. However, due to the idealized conditions, this approach is only suitable for algorithm development and preliminary verification. Therefore, to conduct more in-depth verification of the algorithm, it is necessary to collect actual mixed signals in a practical algorithm verification system. However, collecting mixed signals in reality presents two problems: firstly, the lack of prior knowledge may cause the underdetermined blind source separation algorithm to fail; secondly, it is impossible to accurately obtain the source signals, making it impossible to compare the separated signals with the source signals to analyze the algorithm's performance. Summary of the Invention

[0005] In view of this, the purpose of this embodiment is to provide a verification test system for an underdetermined blind source separation algorithm for communication or radar signals. First, a blind source signal acquisition system is constructed using a Yagi antenna and a superheterodyne receiver. Then, standard signal sources, walkie-talkies, and other equipment are used as blind source signal transmitters. Next, the mixed signal collected by the signal acquisition system is processed using an underdetermined blind source separation algorithm, compared with the source signal, and the performance of the algorithm is analyzed. Finally, the effectiveness of the underdetermined blind source separation algorithm under actual conditions can be verified.

[0006] The first aspect of this embodiment provides a method for verifying an underdetermined blind source separation algorithm for communication signals, including a data acquisition step and an algorithm verification step;

[0007] The data acquisition step, when performing data sampling, sets the walkie-talkie as signal source S1, and the two standard signal sources as standard signal S2 and standard signal S3; wherein, data sampling includes:

[0008] Step (1): Transmit S1, S2, and S3 respectively, and receive 10 sets of single-source dual-channel sampling data for each signal as the source signal;

[0009] Step (2): Simultaneously transmit mixed signals S1+S2, S1+S3, and S2+S3 respectively, and receive 10 sets of dual-source dual-channel sampling data for each mixed signal;

[0010] Step (3): Simultaneously transmit the mixed signal S1+S2+S3 and receive 10 sets of three-source dual-channel sampling data;

[0011] Step (4): Adjust the direction of the receiving or transmitting antenna, and simultaneously transmit S1+S2+S3 and receive 10 sets of three-source dual-channel sampling data as observation signals;

[0012] The algorithm verification steps include:

[0013] Acquire the observed signal in step (4) of the data sampling process, and acquire the source signal in step (1);

[0014] For the observed signal, a hybrid matrix is ​​first obtained by combining density-based spatial clustering with noise and probability density estimation. Then, compressed sensing technology based on orthogonal matching pursuit algorithm is used to train the sample data to obtain the learning dictionary needed for reconstruction. Finally, the learning dictionary and the hybrid matrix are used to reconstruct the signal and recover the source signal.

[0015] The 10 sets of data in the data sampling step (4) are processed according to the blind source separation algorithm to be verified, the signals are separated, 10 sets of separated blind source signals are obtained, and the performance of the algorithm is numerically expressed using the algorithm performance evaluation criteria.

[0016] Furthermore, the signal source S1 is an AM signal; the standard signal S2 is an FM signal; and the standard signal S3 is an AM signal.

[0017] Furthermore, the signal source S1, standard signal S2, and standard signal S3 are respectively represented as follows:

[0018] S1:

[0019] S2:

[0020] S3: ;

[0021] Where t represents time, A1, A2, and A3 are the amplitudes of the signals corresponding to S1, S2, and S3, respectively, X1(t), X2(τ), and X3(t) are the carrier signals corresponding to S1, S2, and S3, respectively, and K... FM Indicates frequency modulation sensitivity

[0022] Furthermore, the received signal source S1, standard signal S2, and standard signal S3 are output via intermediate frequency; represented as:

[0023] S1-IF:

[0024] S2-IF:

[0025] S3-IF: .

[0026] Furthermore, the method of using algorithm performance evaluation criteria to numerically demonstrate the performance of the algorithm includes: determining whether the algorithm has successfully separated in-band underdetermined blind source signals by calculating the average correlation coefficient and signal-to-interference ratio.

[0027] Secondly, the present invention implements a verification system for an underdetermined blind source separation algorithm for communication signals based on a Yagi antenna, the system comprising a blind source signal acquisition system and an algorithm verification system;

[0028] The blind source signal acquisition system includes two Yagi antennas, a dual-channel superheterodyne receiver, and a control computer; wherein, the control computer is used to execute the data acquisition software program, and the data acquisition software program is used to execute the data acquisition steps; the algorithm verification system is used to execute the algorithm verification steps.

[0029] Furthermore, in the blind source signal acquisition system, two Yagi antennas are placed inside the microwave anechoic chamber, while the superheterodyne receiver and control computer are placed outside the microwave anechoic chamber. The receiving antennas and the receiver are connected by an RF adapter on the wall of the microwave anechoic chamber via RF cables.

[0030] Furthermore, during verification, standard signal sources and walkie-talkies were used as experimental blind source signal transmitters; the experimental blind source signal transmitters were placed in a microwave anechoic chamber and simultaneously transmitted 2-3 signals; both the standard signal source and the walkie-talkie were connected to the Yagi antenna via radio frequency cables and transmitted signals in different directions.

[0031] Furthermore, a third aspect of the present invention provides an electronic device comprising: one or more processors, and a memory for storing one or more computer programs; characterized in that the computer programs are configured to be executed by the one or more processors, and the programs include steps for performing the communication signal underdetermined blind source separation algorithm verification method as described in the first aspect above.

[0032] Furthermore, a fourth aspect of the present invention provides a storage medium storing a computer program; the program is loaded and executed by a processor to implement the steps of the communication signal underdetermined blind source separation algorithm verification method as described in the first aspect above.

[0033] In this embodiment, a blind source signal acquisition system is designed using a Yagi antenna, a superheterodyne receiver, and data acquisition software. Standard signal sources and walkie-talkies are then used as experimental blind source signals. This blind source signal acquisition system has two acquisition channels. By setting the signal source and receiving frequency, it can simultaneously acquire intermediate frequency (IF) data from multiple source signals within the real-time bandwidth. The sampled IF data serves as input data for algorithms, used to compare and verify the source number estimation and source signal recovery effects of various underdetermined blind source separation algorithms. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of this embodiment, the accompanying drawings used in the embodiment will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this embodiment and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a schematic diagram of the blind source signal acquisition system disclosed in this embodiment;

[0036] Figure 2 This is a schematic diagram of the equipment connection in the test scenario disclosed in this embodiment;

[0037] Figure 3 This is a schematic diagram of the actual layout of the test scenario disclosed in this embodiment;

[0038] Figure 4 This is a schematic diagram of the equipment connection for an experimental scenario showing the distribution relationship of the signal center frequency within the intermediate frequency bandwidth disclosed in this embodiment;

[0039] Figure 5 This is a schematic diagram of the time difference between the two channels of the receiver disclosed in this embodiment;

[0040] Figure 6 This is a schematic diagram of one of the ten sets of experimental sampling data disclosed in this embodiment;

[0041] Figure 7 This is a schematic diagram of the time-domain waveforms of the three source signals in the experiment disclosed in this embodiment;

[0042] Figure 8 This is a schematic diagram of the source signal recovered from the reconstruction result obtained by the algorithm disclosed in this embodiment. Detailed Implementation

[0043] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0044] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, systems, steps, etc., can be employed. In other instances, well-known methods, systems, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0045] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor systems and / or microcontroller systems.

[0046] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0047] It should be noted that "multiple" as mentioned in this article refers to two or more.

[0048] Blind source separation is a method for handling signal mixing problems. Its goal is to recover the original signal by analyzing and processing the observed signal without prior knowledge. In blind source separation, only the observed mixed signal is known, while the original signal and the mixing method are unknown. Therefore, blind source separation requires analyzing the characteristics and statistical properties of the observed signal to infer the characteristics of the original signal and the mixing method to separate them. The "blind" aspect is reflected in two aspects: first, the original signal and the number of signals are unknown; second, the mixing method (mixing matrix) of the source signals is unknown. The system principle block diagram of blind source separation is shown below. Figure 1 As shown, the operating mode and receiving parameter settings of the multi-channel receiver will affect the observed signal.

[0049] Two important components of a blind source separation system are the hybrid system model and the separation system model. Individual source signal Transmission in an unknown mixed channel, assuming a random mixed channel The middle element is additive white Gaussian noise. , For time indexing. Then, by Each receiving channel receives Road observation signal The mathematical expression for the blind source separation model is:

[0050]

[0051] In the underdetermined blind source separation problem, .

[0052] The common approach to solving the underdetermined blind source separation problem is the "two-step method," which involves first estimating the mixing matrix. The source signal is then recovered using a reconstruction algorithm. The performance evaluation criteria for the underdetermined blind source separation algorithm are mainly measured by the signal-to-interference ratio (SIR) and the correlation coefficient.

[0053] The signal-to-interference ratio (SIR) refers to the ratio of interference between the reconstructed signal components and their corresponding source signals. Its expression is:

[0054]

[0055] In the formula, As the source signal, To reconstruct the signal. When evaluating the algorithm, The larger the value, the better the signal separation effect. It is generally believed that... The reconstructed signal yielded a fairly satisfactory result.

[0056] The expression for the correlation coefficient is:

[0057]

[0058] In the formula, Indicates the first One source signal, The reconstructed first Individual source signals. When the value is 1, it indicates that the reconstructed source signal is completely identical to the original signal, but due to noise and other factors, this is almost impossible to achieve. It is generally considered that when... At that time, the recovery effect of the source signal was relatively good.

[0059] In this embodiment, firstly, a blind source signal acquisition system is constructed using a Yagi antenna and a superheterodyne receiver. Then, standard signal sources, walkie-talkies, and other equipment are used as blind source signal transmitters. Next, an underdetermined blind source separation algorithm is used to process the mixed signal collected by the signal acquisition system. The algorithm's performance is analyzed by comparing it with the source signal. Finally, the effectiveness of the underdetermined blind source separation algorithm under actual conditions can be verified.

[0060] The implementation details of the technical solutions in the embodiments of this application are described in detail below:

[0061] 1. Construction of a blind source signal acquisition system

[0062] The blind source signal acquisition system constructed in this embodiment mainly consists of two Yagi antennas, one dual-channel superheterodyne receiver, a control computer, and data acquisition software, etc. Figure 1 As shown.

[0063] The main performance indicators of the Yagi antenna in the system are as follows:

[0064] (1) Frequency range: 400MHz-470MHz;

[0065] (2) Gain: Not less than 12dB;

[0066] (3) Impedance: 50 ohms;

[0067] (4) In-band voltage standing wave ratio: not greater than 1.2;

[0068] (5) Beamwidth: less than 60°.

[0069] The system uses a common software-defined radio dual-channel superheterodyne receiver. This receiver can simultaneously receive two signals and, after down-conversion, directly sample the intermediate frequency (IF) data of both signals to obtain IF data. Its main performance specifications are as follows:

[0070] (1) Operating frequency: DC-6GHz;

[0071] (2) Intermediate frequency bandwidth: can be set by software (1Hz-10MHz);

[0072] (3) Noise figure: 8dB;

[0073] (4) Sampling rate: 800 MS / s;

[0074] (5) Local oscillator accuracy: 2.5ppm;

[0075] (6) Local oscillator suppression: 50dB;

[0076] (7) Third-order cutoff point: 0dB;

[0077] (8) DAC resolution: 14 bits.

[0078] MATLAB code was written on the control computer to complete the receiver initialization, parameter configuration, and data acquisition. The acquired intermediate frequency data was transmitted to the control computer for analysis and processing via a data interface.

[0079] 2. Experimental Scenario Setup

[0080] Using standard signal generators and walkie-talkies as blind source signal transmitters, these devices were placed in a microwave anechoic chamber and simultaneously transmitted 2-3 signals. The standard signal generator and walkie-talkies (with modified interfaces) were connected to transmitting antennas (also Yagi antennas) via RF cables, transmitting signals in different directions. The two receiving antennas (Yagi antennas) of the blind source signal acquisition system were placed inside the microwave anechoic chamber, while the superheterodyne receiver and control computer were placed outside. The receiving antennas and receiver were connected via RF cables through an RF adapter on the wall of the microwave anechoic chamber. The equipment connections in the above experimental scenario are as follows: Figure 2The actual layout in the experiment is as follows Figure 3 As shown.

[0081] 3. Data Collection

[0082] In the experiment, the walkie-talkie was set as the signal source S1 (AM signal), and the two standard signal sources were S2 (FM signal) and S3 (AM signal).

[0083] Based on their carrier frequencies, these radio frequency signals can be represented as follows:

[0084] S1:

[0085] S2:

[0086] S3: ;

[0087] Where t represents time, A1, A2, and A3 are the amplitudes of the signals corresponding to S1, S2, and S3, respectively, X1(t), X2(τ), and X3(t) are the carrier signals corresponding to S1, S2, and S3, respectively, and K... FM This indicates the frequency modulation sensitivity.

[0088] The tuning frequency of the superheterodyne receiver is set to 435.05MHz. The signal is received at the receiver's final intermediate frequency (IF) (256kHz). Therefore, the IF outputs corresponding to S1, S2, and S3 can be expressed as follows:

[0089] S1-IF:

[0090] S2-IF:

[0091] S3-IF: .

[0092] The distribution relationship of the center frequencies of signal source S1 (AM signal) and two standard signal sources S2 (FM signal) and S3 (AM signal) within the intermediate frequency bandwidth is as follows: Figure 4 As shown.

[0093] The first aspect of this embodiment provides a method for verifying an underdetermined blind source separation algorithm for communication signals, including a data acquisition step and an algorithm verification step;

[0094] The data acquisition step, when performing data sampling, sets the walkie-talkie as signal source S1, and the two standard signal sources as standard signal S2 and standard signal S3; wherein, data sampling includes:

[0095] Step (1): Transmit S1, S2, and S3 respectively, and receive 10 sets of single-source dual-channel sampling data for each signal as the source signal;

[0096] Step (2): Simultaneously transmit mixed signals S1+S2, S1+S3, and S2+S3 respectively, and receive 10 sets of dual-source dual-channel sampling data for each mixed signal;

[0097] Step (3): Simultaneously transmit the mixed signal S1+S2+S3 and receive 10 sets of three-source dual-channel sampling data;

[0098] Step (4): Adjust the direction of the receiving or transmitting antenna, and simultaneously transmit S1+S2+S3 and receive 10 sets of three-source dual-channel sampling data as observation signals;

[0099] When sampling data, it is necessary to implement the following four cases respectively: (1) Transmit S1, S2 and S3 respectively, and receive 10 sets (30 sets in total) of single-source dual-channel sampling data for each signal; (2) Transmit S1+S2, S1+S3 and S2+S3 at the same time, and receive 10 sets (30 sets in total) of dual-source dual-channel sampling data for each signal; (3) Transmit S1+S2+S3 at the same time and receive 10 sets of three-source dual-channel sampling data; (4) Adjust the direction of the receiving (or transmitting) antenna, and transmit S1+S2+S3 at the same time and receive 10 sets of three-source dual-channel sampling data.

[0100] Among them, 70 sets of data obtained in cases (1), (2), and (3) were used as samples for training, and 10 sets of data in case (4) were used as observation signals for blind source separation experiments.

[0101] To reduce receiver channel phase error, the experiment first used sampling data of the same single-frequency signal to estimate the time difference between the two receiver channels (e.g., ...). Figure 5 As shown in the figure, the average time difference between channel 2 and channel 1 is approximately 30.69 microseconds, and channel 1 receives stable data before channel 2. When compensating for the time difference of the sampled data, the channel time difference is converted into the corresponding number of data sampling points (approximately 123 points) according to the sampling frequency (4MHz), and data shift compensation is performed on channel 2.

[0102] In this embodiment, the compensation method is sampling point shift compensation. For example, if the compensation is calculated to be 123 sampling points, assuming that both channels sample 2000 points, then channel 1 samples points 4001-6000, a total of 2000 points, while channel 2 samples points 4124 (4124=4001+123) to 6123, a total of 2000 points. Therefore, it is called shift compensation.

[0103] The following is an example of a verification algorithm:

[0104] Step 1: Obtain the observed signal from step (4) of the data sampling and the source signal from step (1).

[0105] Specifically, in this embodiment, the observed mixed signal is obtained according to step (4) in the data sampling, and the source signal is obtained according to step (1). Figure 6 This is one set of observation signals from the 10 sets of three-source experimental signals in step (4) above, that is, one set of the ten sets of experimental sampling data. After channel time difference compensation for the 10 sets of data, the obtained observation signals are used to verify the underdetermined blind source separation algorithm proposed in reference [1].

[0106] In this embodiment, the waveforms of the source signals are known, namely the time-domain waveforms of the three source signals in the experiment, as shown below. Figure 7 As shown.

[0107] Step 2: For the observed signal, firstly, a hybrid matrix is ​​obtained by combining density-based spatial clustering with noise and probability density estimation. Then, compressed sensing technology based on orthogonal matching pursuit algorithm is used to train the sample data to obtain the learning dictionary needed for reconstruction. Finally, the signal is reconstructed using the learning dictionary and the hybrid matrix to recover the source signal.

[0108] Specifically, in this embodiment, according to the algorithm in reference [1] (Zhang Yu, Yang Qishan, Jia Maoshen. Estimation of underdetermined blind source separation hybrid matrix using DBSCAN and probability density estimation [J]. Signal Processing, ,2023,39(04):708-718.), for the observed signal in the above figure, the hybrid matrix is ​​first obtained by combining density-based spatial clustering of application with noise (DBSCAN) and probability density estimation. Then, the above 70 sets of sample data are trained using compressed sensing technology based on orthogonal matching pursuit algorithm to obtain the learning dictionary needed for reconstruction. Finally, the signal is reconstructed using the learning dictionary and the hybrid matrix to recover the source signal. The source signal recovered by the reconstruction result obtained by applying the algorithm in reference [1] is as follows. Figure 8 As shown.

[0109] Step 3: Process the 10 sets of data in the data sampling step (4) according to the blind source separation algorithm to be verified, separate the signals, obtain 10 sets of separated blind source signals, and use the algorithm performance evaluation criteria to numerically demonstrate the performance of the algorithm.

[0110] Furthermore, the signal source S1 is an AM signal; the standard signal S2 is an FM signal; and the standard signal S3 is an AM signal. Furthermore, the signal source S1, the standard signal S2, and the standard signal S3 are respectively represented as follows:

[0111] S1:

[0112] S2:

[0113] S3: .

[0114] Where t represents time, A1, A2, and A3 are the amplitudes of the signals corresponding to S1, S2, and S3, respectively, and X1(t), X2(τ), and X3(t) are the carrier signals corresponding to S1, S2, and S3, respectively. K FM This indicates the frequency modulation sensitivity.

[0115] Furthermore, the received signal source S1, standard signal S2, and standard signal S3 are output via intermediate frequency; represented as:

[0116] S1-IF:

[0117] S2-IF:

[0118] S3-IF: .

[0119] Furthermore, the method of using algorithm performance evaluation criteria to numerically demonstrate the performance of the algorithm includes: determining whether the algorithm has successfully separated in-band underdetermined blind source signals by calculating the average correlation coefficient and signal-to-interference ratio.

[0120] Specifically, in this embodiment, the 10 sets of data in the data sampling step (4) are processed according to the blind source separation algorithm to be verified, the signals are separated, and 10 sets of separated blind source signals are obtained. The performance of the algorithm is numerically expressed using the algorithm performance evaluation criteria. Reference [1] (Zhang Yu, Yang Qishan, Jia Maoshen. Estimation of the hybrid matrix for underdetermined blind source separation using DBSCAN and probability density estimation [J]. Signal Processing, ,2023,39(04):708-718.) The results of the algorithm calculating the average correlation coefficient and signal-to-interference ratio of the 10 sets of collected data are shown in Table 1. The correlation coefficients obtained by the algorithm are all greater than 0.8, and the signal-to-interference ratios are all greater than 10dB, indicating that the algorithm can successfully separate the in-band underdetermined blind source signals. If you want to compare the performance of other algorithms, you only need to compare the average correlation coefficient and the signal-to-interference ratio.

[0121] Table 1. Relevant calculation results of signal separation algorithm in reference [1]

[0122] Source signal Correlation coefficient Signal-to-Dryness Ratio (dB) S1 0.9274 12.1483 S2 0.8983 10.0143 S3 0.9226 11.8452 average value 0.9161 11.6693

[0123] Secondly, this embodiment provides a verification system for an underdetermined blind source separation algorithm for communication signals based on a Yagi antenna. The system includes a blind source signal acquisition system and an algorithm verification system.

[0124] The blind source signal acquisition system includes two Yagi antennas, a dual-channel superheterodyne receiver, and a control computer; wherein, the control computer is used to execute a data acquisition software program, which is used to execute the data acquisition steps described in the first aspect; the algorithm verification system is used to execute the algorithm verification steps described in the first aspect.

[0125] Furthermore, in the blind source signal acquisition system, two Yagi antennas are placed inside the microwave anechoic chamber, while the superheterodyne receiver and control computer are placed outside the microwave anechoic chamber. The receiving antennas and the receiver are connected by an RF adapter on the wall of the microwave anechoic chamber via RF cables.

[0126] Furthermore, during verification, standard signal sources and walkie-talkies were used as experimental blind source signal transmitters; the experimental blind source signal transmitters were placed in a microwave anechoic chamber and simultaneously transmitted 2-3 signals; both the standard signal source and the walkie-talkie were connected to the Yagi antenna via radio frequency cables and transmitted signals in different directions.

[0127] Thirdly, this embodiment provides an electronic device comprising: one or more processors, and a memory for storing one or more computer programs; characterized in that the computer programs are configured to be executed by the one or more processors, and the programs include steps for performing the communication signal underdetermined blind source separation algorithm verification method as described in the first aspect.

[0128] Fourthly, this embodiment provides a storage medium storing a computer program; the program is loaded and executed by a processor to implement the steps of the communication signal underdetermined blind source separation algorithm verification method as described in the first aspect.

[0129] Therefore, in this embodiment, the design of an underdetermined blind source separation algorithm verification system using a directional Yagi antenna and a dual-channel receiver is the key and crucial aspect of this embodiment. This embodiment uses the intermediate frequency (IF) data acquired by the system as the input data for the algorithm to verify the performance of various underdetermined blind source separation algorithms. This embodiment employs a dual-channel design, with each channel independently configured with a Yagi antenna and a superheterodyne receiver, enabling synchronous acquisition of IF data from multiple source signals. By calculating the time difference between the two channels and performing time compensation, the consistency and synchronization of the data from both channels are ensured, avoiding data distortion caused by time deviations.

[0130] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this embodiment.

Claims

1. A verification method for an underdetermined blind source separation algorithm in communication signals, characterized in that, This includes data acquisition steps and algorithm verification steps; The data acquisition step, when performing data sampling, sets the walkie-talkie as signal source S1, and the two standard signal sources as standard signal S2 and standard signal S3; wherein, data sampling includes: Step (1): Transmit S1, S2, and S3 respectively, and receive 10 sets of single-source dual-channel sampling data for each signal as the source signal; Step (2): Simultaneously transmit mixed signals S1+S2, S1+S3, and S2+S3 respectively, and receive 10 sets of dual-source dual-channel sampling data for each mixed signal; Step (3): Simultaneously transmit the mixed signal S1+S2+S3 and receive 10 sets of three-source dual-channel sampling data; Step (4): Adjust the direction of the receiving or transmitting antenna, and simultaneously transmit S1+S2+S3 and receive 10 sets of three-source dual-channel sampling data as observation signals; The algorithm verification steps include: The observed signal obtained in step (4) is used to conduct a blind source separation experiment. The 70 sets of data obtained in steps (1) to (3) are used as sample data for training. For the observed signal, a hybrid matrix is ​​first obtained by combining density-based spatial clustering with noise and probability density estimation. Then, compressed sensing technology based on orthogonal matching pursuit algorithm is used to train the sample data to obtain the learning dictionary needed for reconstruction. Finally, the learning dictionary and the hybrid matrix are used to reconstruct the signal and recover the source signal. The 10 sets of data in the data sampling step (4) are processed according to the blind source separation algorithm to be verified, the signals are separated, 10 sets of separated blind source signals are obtained, and the performance of the algorithm is numerically expressed using the algorithm performance evaluation criteria.

2. The verification method for underdetermined blind source separation algorithm of communication signals according to claim 1, characterized in that, The signal source S1 is an AM signal; the standard signal S2 is an FM signal; and the standard signal S3 is an AM signal.

3. The verification method for underdetermined blind source separation algorithm of communication signals according to claim 2, characterized in that, The signal source S1, standard signal S2, and standard signal S3 are respectively represented as follows: S1： ; S2： ; S3： ； Where t represents time, A1, A2, and A3 are the amplitudes of the signals corresponding to S1, S2, and S3, respectively, X1(t), X2(τ), and X3(t) are the carrier signals corresponding to S1, S2, and S3, respectively, and K... FM This indicates the frequency modulation sensitivity.

4. The verification method for underdetermined blind source separation algorithm of communication signals according to claim 3, characterized in that, The received signal source S1, standard signal S2, and standard signal S3 are output through intermediate frequency; represented as: S1-IF: ; S2-IF: ; S3-IF: 。 5. The verification method for the underdetermined blind source separation algorithm of communication signals according to claim 4, wherein the step of using algorithm performance evaluation criteria to numerically represent the performance of the algorithm includes: By calculating the average correlation coefficient and signal-to-interference ratio, it can be determined whether the algorithm has successfully separated the in-band underdetermined blind source signal.

6. A verification system for an underdetermined blind source separation algorithm for communication signals based on a Yagi antenna, characterized in that, The system includes a blind source signal acquisition system and an algorithm verification system; The blind source signal acquisition system includes two Yagi antennas, a dual-channel superheterodyne receiver, and a control computer; wherein, the control computer is used to execute a data acquisition software program, which is used to execute the data acquisition steps described in claim 1; the algorithm verification system is used to execute the algorithm verification steps described in claim 1.

7. The verification system for underdetermined blind source separation algorithm of communication signal based on Yagi antenna according to claim 6, characterized in that, In the blind source signal acquisition system, two Yagi antennas are placed in a microwave anechoic chamber, while the superheterodyne receiver and control computer are placed outside the microwave anechoic chamber. The receiving antennas and the receiver are connected by an RF adapter on the wall of the microwave anechoic chamber via RF cables.

8. The verification system for the underdetermined blind source separation algorithm of communication signals based on a Yagi antenna according to claim 6, wherein, During verification, standard signal sources and walkie-talkies were used as experimental blind source signal transmitters. The experimental blind source signal transmitters were placed in a microwave anechoic chamber and simultaneously transmitted 2-3 signals. The standard signal source and walkie-talkies were connected to the Yagi antenna via radio frequency cables and transmitted signals in different directions.

9. An electronic device, the electronic device comprising: One or more processors, a memory for storing one or more computer programs; characterized in that the computer programs are configured to be executed by the one or more processors, the programs including steps for performing the verification method for underdetermined blind source separation algorithm of communication signals as described in any one of claims 1-5.

10. A storage medium storing a computer program; the program being loaded and executed by a processor to implement the steps of the communication signal underdetermined blind source separation algorithm verification method as described in any one of claims 1-5.

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