Communication signal underdetermined blind source separation algorithm verification system and method based on yagi antenna

Abstract

The embodiment of the invention provides a communication signal underdetermined blind source separation algorithm verification system and method based on a yagi antenna. A blind source signal acquisition system is designed by using a yagi antenna, a superheterodyne receiver, data acquisition software and the like. And a standard signal source, an interphone and other signal sources are used as experimental blind source signals. The blind source signal acquisition system is provided with two acquisition channels, and intermediate frequency (IF) data of multiple paths of source signals can be simultaneously acquired in a real-time bandwidth by setting a signal source and a receiving frequency. Sampled intermediate frequency data is used as input data of the algorithm and is used for comparing and verifying source number estimation and source signal recovery effects of various underdetermined blind source separation algorithms.

Description

Technical Field

[0001] The present embodiment relates to the field of communication technology, and in particular to a system and method for verifying an underdetermined blind source separation algorithm for communication signals based on a Yagi antenna. Background Art

[0002] There are various non-cooperative signals in the electromagnetic environment, or in-band interference or even co-frequency interference signals that are difficult to fully grasp with prior knowledge. It is usually very difficult to quickly and accurately find the "desired" signal from these complex signals, which brings great challenges to the identification and rejection of illegal radio stations in secure wireless communications, the location and separation of interference sources in electromagnetic compatibility testing, the situational awareness and feature extraction of electromagnetic environment detection, and the interference search and cognitive access in electromagnetic spectrum management and control.

[0003] Various electromagnetic signals in the electromagnetic environment are usually emitted by multiple radiation sources. Because the specific characteristics of these source signals are unknown in advance, they are called "blind source signals"; and 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. Then, the process of finding the "expected" signal or "target" signal from a complex mixed signal is usually called "blind source separation" (BSS). BSS technology does not require high prior knowledge, and can quickly and accurately sort and identify source signals based on the statistical characteristics of the "blind source" observation signal (received signal) when the source signal characteristics and channel characteristics are unknown, and estimate each source signal. Blind source separation (BSS) is a technology that extracts the original signal from multiple mixed signals without knowing the source or mixing process of the signal in advance. In the fields of communication, audio processing, biomedical signal processing, etc., BSS technology has broad application prospects. However, when the number of mixed signals exceeds the number of sensors, it is the Underdetermined Blind Source Separation (UBSS) problem, which is more difficult to solve. With the development of neural networks, UBSS algorithms have gradually matured. However, the verification of most UBSS algorithms mainly relies on simulation data, using simulated signals and matrices to simulate mixed channels and mixed signals. Simulation experiments are carried out in a fully controlled environment, and signal parameters, mixing matrices, noise levels, etc. can be accurately set without external interference, but they cannot fully reflect the complexity and uncertainty in the real environment.

[0004] For the underdetermined blind source separation algorithm of communication signals in the prior art, collecting blind source mixed communication signals is a complex task. In practice, there are many kinds of noise and interference, and the mixing matrix is ​​unknown. The existing underdetermined blind source separation algorithm for communication signals or radar signals usually uses MATLAB simulation signals and randomly generated matrices to simulate the mixing matrix, but because the conditions are too ideal, it can only be used for algorithm development and preliminary verification. Therefore, to verify the algorithm more deeply, it is necessary to collect actual mixed signals in the actual algorithm verification system for verification. If mixed signals are collected in practice, firstly, the lack of corresponding prior knowledge may cause the underdetermined blind source separation algorithm to fail; secondly, the source signal cannot be accurately obtained, and the signal separated by the underdetermined blind source separation algorithm cannot be compared with the source signal to analyze the performance of the algorithm. Summary of the invention

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

[0006] A first aspect of the present embodiment provides a communication signal underdetermined blind source separation algorithm verification method, including a data collection step and an algorithm verification step;

[0007] The data acquisition step is used to perform data sampling, wherein the intercom is set as the signal source S1, and the two standard signal sources are respectively the standard signal S2 and the standard signal S3; wherein the data sampling includes:

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

[0009] Step (2), respectively and simultaneously transmitting mixed signal S1+S2, mixed signal S1+S3, and mixed signal S2+S3, and receiving 10 sets of dual-source dual-channel sampling data for each mixed signal;

[0010] Step (3), simultaneously transmitting the mixed signal S1+S2+S3, and receiving 10 sets of three-source dual-channel sampling data;

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

[0012] The algorithm verification step includes:

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

[0014] For the observed signal, a mixing matrix is ​​first obtained by using a mixing matrix estimation algorithm combining density-based spatial clustering with noise and probability density estimation, and then a compressed sensing technology based on an orthogonal matching pursuit algorithm is used to train the sample data to obtain a learning dictionary required for reconstruction, and finally the learning dictionary and the mixing matrix are used to reconstruct the signal and restore the source signal;

[0015] The 10 groups of data in the data sampling step (4) are all processed according to the blind source separation algorithm to be verified, the signals are separated to obtain 10 groups of separated blind source signals, and the algorithm performance evaluation criteria are used to numerically indicate the performance of the algorithm.

[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] Further, the signal source S1, the standard signal S2, and the standard signal S3 are respectively expressed as:

[0018] S1:x RF1 (t)=[A1+X1(t)]×cos(2π×435.1×10 6 ×t)

[0019] S2:

[0020] S3:x RF3 (t)=[A3+X3(t)]×cos(2π×435.0×10 6 ×t);

[0021] Among them, t represents time, A1, A2, A3 are the amplitudes of the signals corresponding to S1, S2, S3, X1(), X2(), X3() are the carrier signals corresponding to S1, S2, S3, K FM Indicates FM sensitivity

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

[0023] S1-IF:x IF1 (t)=[A1+X1(t)]×cos(2π×206×10 3 ×t)

[0024] S2-IF:

[0025] S3-IF:x IF3(t)=[A3+X3(t)]×cos(2π×306×10 3 × t).

[0026] Furthermore, the algorithm performance evaluation criteria are used to numerically indicate the performance of the algorithm, including: determining whether the algorithm successfully separates the in-band underdetermined blind source signal by calculating the average correlation coefficient and the signal-to-interference ratio.

[0027] In a second aspect, the present invention implements a communication signal underdetermined blind source separation algorithm verification system based on 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 a data acquisition software program, and the data acquisition software program 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.

[0029] Furthermore, in the blind source signal acquisition system, two Yagi antennas are placed in a microwave darkroom, a superheterodyne receiver and a control computer are placed outside the microwave darkroom, and the receiving antenna and the receiver are connected by a radio frequency line through a radio frequency adapter on the wall of the microwave darkroom.

[0030] Furthermore, during verification, standard signal sources, walkie-talkies and other signal sources are used as experimental blind source signal transmission sources; the experimental blind source signal transmission source is placed in a microwave darkroom to simultaneously transmit 2-3 signals; the standard signal source and the walkie-talkie are connected to the Yagi antenna via a radio frequency cable to transmit signals in different directions.

[0031] In addition, the third aspect of the present invention provides an electronic device, which includes: one or more processors, and a memory, wherein the memory is used to store one or more computer programs; it is characterized in that the computer program is configured to be executed by the one or more processors, and the program includes steps for executing the communication signal underdetermined blind source separation algorithm verification method as described in the first aspect above.

[0032] In addition, the 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 the scheme of this embodiment, a blind source signal acquisition system is designed using Yagi antenna, superheterodyne receiver, data acquisition software, etc.; and then a standard signal source, walkie-talkie and other signal sources are used as experimental blind source signals. The blind source signal acquisition system has two acquisition channels. By setting the signal source and receiving frequency, the intermediate frequency (IF) data of multiple source signals can be simultaneously acquired within the real-time bandwidth. The sampled intermediate frequency data is used as the input data of the algorithm to compare and verify the source number estimation and source signal recovery effects of various underdetermined blind source separation algorithms. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the drawings required for use in the embodiment will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

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

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

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

[0038] Figure 4 It is a schematic diagram of the equipment connection in the test scenario of the distribution relationship of the signal center frequency within the intermediate frequency bandwidth disclosed in the embodiment of this embodiment;

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

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

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

[0042] Figure 8 It is a schematic diagram of a source signal restored from a reconstruction result obtained by the algorithm disclosed in the embodiment of this invention. DETAILED DESCRIPTION

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

[0044] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, systems, steps, etc. can be adopted. In other cases, known methods, systems, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the 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 may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor systems and / or microcontroller systems.

[0046] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0047] It should be noted that the “plurality” mentioned in this article refers to two or more.

[0048] Blind source separation is a method for dealing with signal mixing problems. The goal is to restore 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 needs to analyze the characteristics and statistical properties of the observed signal to infer the characteristics of the original signal and separate their mixing methods. "Blind" is reflected in two aspects: one is that the original signal and the number of signals in the system are unknown; the other is that the mixing method (mixing matrix) of the source signal is unknown. The system principle block diagram of blind source separation is shown below. Figure 1 As shown, the working mode and receiving parameter settings of the multi-channel receiver will affect the observed signal.

[0049] The two important parts of the blind source separation system are the mixing system model and the separation system model. There are N source signals s(t) = [s1(t), s2(t), ..., sN (t)] T Transmitting in an unknown mixed channel, assuming a random mixed channel A = (a ij ) M×N where is additive white Gaussian noise N(t)=[n1(t), n2(t),…, n M (t)] T , t is the time index. Then, M receiving channels receive M observation signals X(t) = [x1(t), x2(t), ..., x M (t)] T The mathematical expression of the blind source separation model is

[0050]

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

[0052] The usual method to solve the underdetermined blind source separation problem is the "two-step method", that is, first estimate the mixing matrix A, and then use the reconstruction algorithm to restore the source signal. The performance evaluation criteria for verifying the underdetermined blind source separation algorithm are mainly measured by the signal-to-interference ratio (SIR) and the correlation coefficient (Correlation Coefficient).

[0053] The signal-to-interference ratio refers to the interference ratio of the reconstructed signal component to its corresponding source signal, and its expression is:

[0054]

[0055] Where s is the source signal, is the reconstructed signal. When evaluating the algorithm, the larger the SIR, the better the signal separation effect. It is generally believed that when SIR ≥ 10dB, the effect of reconstructing the signal is relatively ideal.

[0056] The expression of the correlation coefficient is

[0057]

[0058] In the formula, s i (t) represents the i-th source signal, represents the reconstructed i-th source signal. When is 1, it indicates that the reconstructed source signal is completely consistent with the original signal, but due to noise and other conditions, it is almost impossible to achieve. It is generally believed that when When the source signal is restored, the effect of source signal recovery is better.

[0059] In this embodiment, first, a blind source signal acquisition system is constructed using a Yagi antenna and a superheterodyne receiver, and then a standard signal source, a walkie-talkie and other equipment are used as blind source signal transmission sources. The mixed signal collected by the signal acquisition system is processed using an underdetermined blind source separation algorithm, and compared with the source signal, the performance of the algorithm is analyzed, and finally the effect of the underdetermined blind source separation algorithm under actual conditions can be verified.

[0060] The implementation details of the technical solution of the embodiment of the present application are described in detail below:

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

[0062] The blind source signal acquisition system constructed in this embodiment is mainly composed of two Yagi antennas, a dual-channel superheterodyne receiver, a control computer and data acquisition software, such as Figure 1 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 more than 1.2;

[0068] (5) Beam width: less than 60°.

[0069] The dual-channel superheterodyne receiver in the system uses a common software radio dual-channel superheterodyne receiver. This receiver can receive two signals at the same time, and can directly sample the intermediate frequency to obtain the intermediate frequency sampling data of the two signals after down-conversion. Its main performance indicators are as follows:

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

[0071] (2) IF bandwidth: software configurable (1Hz-10MHz);

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

[0073] (4) Sampling rate: 800MS / 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 is written on the control computer to complete the initialization, parameter configuration and data acquisition of the receiver. The collected intermediate frequency data is transmitted to the control computer through the data interface for analysis and processing.

[0079] 2. Experimental scene setting

[0080] Use standard signal sources, walkie-talkies and other equipment as the blind source signal transmission sources for the experiment, place them in a microwave darkroom and transmit 2-3 signals at the same time. The standard signal source and walkie-talkie (after the interface is modified) are connected to the transmitting antenna (also a Yagi antenna) through RF cables to transmit signals in different directions. The two receiving antennas (Yagi antennas) of the blind source signal acquisition system are placed in the microwave darkroom, and the superheterodyne receiver and control computer are placed outside the microwave darkroom. The receiving antenna and the receiver are connected by an RF cable through the RF adapter on the wall of the microwave darkroom. The equipment connection relationship in the above experimental scenario is as follows: Figure 2 The actual layout in the experiment is as follows Figure 3 shown.

[0081] 3. Data Collection

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

[0083] According to their carrier frequencies, these RF signals can be expressed as:

[0084] S1:x RF1 (t)=[A1+X1(t)]×cos(2π×435.1×10 6 ×t)

[0085] S2:

[0086] S3:x RF3 (t)=[A3+X3(t)]×cos(2π×435.0×10 6 ×t);

[0087] Among them, t represents time, A1, A2, A3 are the amplitudes of the signals corresponding to S1, S2, S3, X1(), X2(), X3() are the carrier signals corresponding to S1, S2, S3, K FM Indicates FM sensitivity.

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

[0089] S1-IF:x IF1 (t)=[A1+X1(t)]×cos(2π×206×10 3 ×t)

[0090] S2-IF:

[0091] S3-IF:x IF3 (t)=[A3+X3(t)]×cos(2π×306×10 3 × t).

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

[0093] A first aspect of the present embodiment provides a communication signal underdetermined blind source separation algorithm verification method, including a data collection step and an algorithm verification step;

[0094] The data acquisition step is used to perform data sampling, wherein the intercom is set as the signal source S1, and the two standard signal sources are respectively the standard signal S2 and the standard signal S3; wherein the data sampling includes:

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

[0096] Step (2), respectively and simultaneously transmitting mixed signal S1+S2, mixed signal S1+S3, and mixed signal S2+S3, and receiving 10 sets of dual-source dual-channel sampling data for each mixed signal;

[0097] Step (3), simultaneously transmitting the mixed signal S1+S2+S3, and receiving 10 sets of three-source dual-channel sampling data;

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

[0099] During data sampling, it is necessary to implement the following four situations separately: (1) transmit S1, S2, and S3 separately, and receive 10 groups (30 groups in total) of single-source dual-channel sampling data for each signal; (2) transmit S1+S2, S1+S3, and S2+S3 simultaneously, and receive 10 groups (30 groups in total) of dual-source dual-channel sampling data for each signal; (3) transmit S1+S2+S3 simultaneously to receive 10 groups of three-source dual-channel sampling data; (4) adjust the direction of the receiving (or transmitting) antenna, and transmit S1+S2+S3 simultaneously to receive 10 groups of three-source dual-channel sampling data.

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

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

[0102] Among them, in this embodiment, the compensation method is sampling point shift compensation. For example, the calculation is to compensate 123 sampling points. Assuming that both channels sample 2000 points, then channel 1 samples 2000 points from 4001 to 6000, while channel 2 samples 2000 points from 4124 (4124=4001+123) to 6123, so it is called shift compensation.

[0103] An example of a verification algorithm is given below:

[0104] Step 1: Obtain the observation signal of step (4) in data sampling, and obtain the source signal of step (1).

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

[0106] In this embodiment, the waveform diagram of the known source signal, that is, the time domain waveforms of the three source signals in the experiment are as follows Figure 7 shown.

[0107] Step 2: For the observed signal, first use a mixed matrix estimation algorithm that combines density-based spatial clustering with noise and probability density estimation to obtain a mixed matrix, then use a compressed sensing technology based on an orthogonal matching pursuit algorithm to train the sample data to obtain the learning dictionary required for reconstruction, and finally use the learning dictionary and the mixed matrix to reconstruct the signal and restore the source signal.

[0108] Specifically, in this embodiment, according to the algorithm in the literature [1] (Zhang Yu, Yang Qishan, Jia Maokun. Mixed matrix estimation for underdetermined blind source separation using DBSCAN and probability density estimation [J]. Signal Processing, 2023, 39(04): 708-718.), for the observed signal in the above figure, the mixed matrix is ​​first obtained by using the mixed matrix estimation algorithm combining density-based spatial clustering of application with noise (DBSCAN) and probability density estimation, and then the compressed sensing technology based on the orthogonal matching pursuit algorithm is used to train the above 70 groups of sample data to obtain the learning dictionary required for reconstruction, and finally the learning dictionary and the mixed matrix are used to reconstruct the signal and restore the source signal. The source signal restored by the reconstruction result obtained by applying the algorithm in the literature [1] is as follows. Figure 8 shown.

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

[0111] S1:x RF1 (t)=[A1+X1(t)]×cos(2π×435.1×10 6 ×t)

[0112] S2:

[0113] S3:x RF3 (t)=[A3+X3(t)]×cos(2π×435.0×10 6 × t).

[0114] Among them, t represents time, A1, A2, A3 are the amplitudes of the signals corresponding to S1, S2, S3, X1(), X2(), X3() are the carrier signals corresponding to S1, S2, S3, K FM Indicates FM sensitivity.

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

[0116] S1-IF:x IF1 (t)=[A1+X1(t)]×cos(2π×206×10 3 ×t)

[0117] S2-IF:

[0118] S3-IF:x IF3 (t)=[A3+X3(t)]×cos(2π×306×10 3 × t).

[0119] Furthermore, the algorithm performance evaluation criteria are used to numerically indicate the performance of the algorithm, including: determining whether the algorithm successfully separates the in-band underdetermined blind source signal by calculating the average correlation coefficient and the signal-to-interference ratio.

[0120] Specifically, in this embodiment, the 10 groups 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 groups of separated blind source signals are obtained, and the algorithm performance evaluation criteria are used to numerically indicate the performance of the algorithm. Reference [1] (Zhang Yu, Yang Qishan, Jia Maokun. Estimation of mixed matrix for underdetermined blind source separation using DBSCAN and probability density estimation [J]. Signal Processing, 2023, 39(04): 708-718.) The algorithm calculates the average correlation coefficient and signal-to-interference ratio for the 10 groups of collected data, and the results 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 signal. If you want to compare the performance of other algorithms, you only need to compare the average correlation coefficient and signal-to-interference ratio.

[0121] Table 1 Related calculation results of signal separation using the algorithm in reference [1]

[0122]

[0123]

[0124] In a second aspect, this embodiment provides a communication signal underdetermined blind source separation algorithm verification system based on Yagi antenna, the system comprising a blind source signal acquisition system and an algorithm verification system;

[0125] 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, and the data acquisition software program 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.

[0126] Furthermore, in the blind source signal acquisition system, two Yagi antennas are placed in a microwave darkroom, a superheterodyne receiver and a control computer are placed outside the microwave darkroom, and the receiving antenna and the receiver are connected by a radio frequency line through a radio frequency adapter on the wall of the microwave darkroom.

[0127] Furthermore, during verification, standard signal sources, walkie-talkies and other signal sources are used as experimental blind source signal transmission sources; the experimental blind source signal transmission source is placed in a microwave darkroom to simultaneously transmit 2-3 signals; the standard signal source and the walkie-talkie are connected to the Yagi antenna via a radio frequency cable to transmit signals in different directions.

[0128] In a third aspect, this embodiment provides an electronic device, comprising: one or more processors, and a memory, wherein the memory is used to store one or more computer programs; characterized in that the computer program is configured to be executed by the one or more processors, and the program includes steps for executing the communication signal underdetermined blind source separation algorithm verification method as described in the first aspect.

[0129] In a fourth aspect, 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.

[0130] Based on this, in the scheme of the present embodiment, the underdetermined blind source separation algorithm verification system is designed by using a directional Yagi antenna and a dual-channel receiver, which is the key point and protection point of the present embodiment. The present embodiment uses the intermediate frequency data collected by the system as the input data of the algorithm to verify the performance of multiple underdetermined blind source separation algorithms. The present embodiment adopts a dual-channel design, and each channel is independently configured with a Yagi antenna and a superheterodyne receiver to achieve synchronous collection of intermediate frequency data of multi-path source signals. By calculating the time difference of the two channels and performing time compensation, the consistency and synchronization of the two channel data are ensured to avoid data distortion caused by time deviation.

[0131] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this embodiment.

[0132] In addition, each functional unit in each embodiment of the present embodiment may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0133] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this embodiment is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a grid device, etc.) to perform all or part of the steps of the method described in each embodiment of this embodiment. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0134] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of this embodiment. It should be understood that the above description is only the specific implementation method of this embodiment and is not used to limit the protection scope of this embodiment. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this embodiment should be included in the protection scope of this embodiment.

Claims

1. A method for verifying an underdetermined blind source separation algorithm for communication signals, characterized in that: Includes data collection steps and algorithm verification steps; The data acquisition step is used to perform data sampling, wherein the intercom is set as the signal source S1, and the two standard signal sources are respectively the standard signal S2 and the standard signal S3; wherein the data sampling includes: Step (1), transmitting S1, S2, and S3 respectively, and receiving 10 sets of single-source dual-channel sampling data for each signal as the source signal; Step (2), respectively and simultaneously transmitting mixed signal S1+S2, mixed signal S1+S3, and mixed signal S2+S3, and receiving 10 sets of dual-source dual-channel sampling data for each mixed signal; Step (3), simultaneously transmitting the mixed signal S1+S2+S3, and receiving 10 sets of three-source dual-channel sampling data; Step (4), adjust the direction of the receiving or transmitting antenna, transmit S1+S2+S3 simultaneously, and receive 10 groups of three-source dual-channel sampling data as observation signals; The algorithm verification step includes: Acquire the observation signal of step (4) in data sampling, and acquire the source signal of step (1); For the observed signal, a mixing matrix is ​​first obtained by using a mixing matrix estimation algorithm combining density-based spatial clustering with noise and probability density estimation, and then a compressed sensing technology based on an orthogonal matching pursuit algorithm is used to train the sample data to obtain a learning dictionary required for reconstruction, and finally the learning dictionary and the mixing matrix are used to reconstruct the signal and restore the source signal; The 10 groups of data in the data sampling step (4) are all processed according to the blind source separation algorithm to be verified, the signals are separated to obtain 10 groups of separated blind source signals, and the algorithm performance evaluation criteria are used to numerically indicate the performance of the algorithm.

2. The method for verifying the underdetermined blind source separation algorithm for 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 method for verifying the underdetermined blind source separation algorithm for communication signals according to claim 2, characterized in that: The signal source S1, standard signal S2, and standard signal S3 are respectively expressed as: S1:x RF1 (t)=[A1+X1(t)]×cos(2π×435.1×10 6 ×t) S3:x RF3 (t)=[A3+X3(t)]×cos(2π×435.0×10 6 ×t); Among them, t represents time, A1, A2, A3 are the amplitudes of the signals corresponding to S1, S2, S3, X1(), X2(), X3() are the carrier signals corresponding to S1, S2, S3, K FM Indicates FM sensitivity.

4. The method for verifying the underdetermined blind source separation algorithm for communication signals according to claim 3, characterized in that: The received signal source S1, standard signal S2, and standard signal S3 are output through the intermediate frequency; they are expressed as: S1-IF:x IF1 (t)=[A1+X1(t)]×cos(2π×206×10 3 ×t) S3-IF:x IF3 (t)=[A3+X3(t)]×cos(2π×306×10 3 ×t)。 5. The method for verifying an underdetermined blind source separation algorithm for communication signals according to claim 4, wherein the method of using an algorithm performance evaluation criterion to numerically indicate the performance of the algorithm comprises: By calculating the average correlation coefficient and signal-to-interference ratio, it is determined whether the algorithm successfully separates the in-band underdetermined blind source signal.

6. A verification system for underdetermined blind source separation algorithm of communication signals based on 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, and the data acquisition software program 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 5, characterized in that: In the blind source signal acquisition system, two Yagi antennas are placed in a microwave darkroom, the superheterodyne receiver and the control computer are placed outside the microwave darkroom, and the receiving antenna and the receiver are connected by a radio frequency line through the radio frequency adapter on the wall of the microwave darkroom.

8. The verification system for underdetermined blind source separation algorithm of communication signal based on Yagi antenna according to claim 6, wherein: During verification, standard signal sources, walkie-talkies and other signal sources are used as experimental blind source signal transmission sources; the experimental blind source signal transmission sources are placed in a microwave darkroom to simultaneously transmit 2-3 signals; the standard signal source and the walkie-talkie are connected to the Yagi antenna through a radio frequency cable to transmit signals in different directions.

9. An electronic device, comprising: One or more processors, a memory, wherein the memory is used to store one or more computer programs; characterized in that the computer program is configured to be executed by the one or more processors, and the program includes steps for executing the communication signal underdetermined blind source separation algorithm verification method as described in any one of claims 1-5.

10. 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 according to any one of claims 1 to 5.

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