A multi-antenna receiving beam forming method of GMSK frequency hopping signal

Abstract

The application discloses a multi-antenna receiving beam forming method of GMSK frequency hopping signals, which comprises the following steps: based on a known GMSK training sequence and by an approximate maximum posterior probability method, accurately estimating a real direction of an expected signal, so as to effectively correct a mismatched direction vector; on this basis, further adopting a covariance matrix reconstruction technology, removing an expected signal component from a sampling covariance matrix, effectively suppressing the expected signal cancellation phenomenon, and being able to perceive a spatial interference distribution and form a relatively accurate null in the interference direction, and significantly improving the output signal-to-interference-and-noise ratio performance of the system.

Description

Technical Field

[0001] This invention relates to the field of array antenna signal processing technology, and specifically to a multi-antenna receiving beamforming method for GMSK frequency hopping signals. Background Technology

[0002] Adaptive beamforming, as one of the core technologies of array antennas, has broad application prospects in radar, communication, and antenna fields. Among them, GMSK (Gaussian Minimum Frequency Shift Keying) frequency hopping signals, with their outstanding advantages of constant envelope, narrow spectrum, and strong anti-interference capability, are widely used in shortwave anti-jamming communication, frequency hopping radar, satellite communication, and tactical communication scenarios. These scenarios often face complex electromagnetic interference environments, which place higher demands on the robustness of adaptive beamforming.

[0003] Traditional adaptive beamforming methods generally suffer from insufficient robustness in practical engineering environments, and their performance idealization relies on the accurate prediction of the desired signal's arrival direction. In real-world applications, the direction of the desired signal is often random and difficult to accurately obtain, leading to direction vector mismatch. When the snapshot data contains the desired signal component, the sampling covariance matrix estimated based on the received data will deviate from the actual direction vector. Especially under high interference-to-signal ratio conditions, this can easily cause the desired signal to cancel out, severely weakening the output performance of conventional algorithms and even causing communication system failure. Furthermore, when the desired signal is a GMSK frequency-hopping signal, the frequency of the signal changes periodically over time due to the frequency-hopping mechanism, resulting in time-varying time-frequency characteristics. Existing robust beamforming methods based on covariance matrix reconstruction struggle to adapt to this time-varying characteristic, failing to operate stably and exhibiting significant limitations. Summary of the Invention

[0004] This invention proposes a multi-antenna receiving beamforming method for GMSK frequency hopping signals to solve the problems mentioned in the background.

[0005] To achieve the above objectives, the present invention employs the following technical solution: A multi-antenna receiving beamforming method for GMSK frequency hopping signals according to the present invention includes the following steps: S1. Receive K Beat-hopping frequency GMSK modulation array receives signals; S2. Using a GMSK modulated signal based on a known symbol sequence, perform discrete correlation analysis on the received signal with a length of [length missing]. K The relevant operations are performed to obtain the relevant operation result vector; S3. Estimate the covariance matrix of the received signal and the correlation result vector respectively, and preprocess the obtained covariance matrix using diagonal loading; S4. Based on the covariance matrix, the DOA of the desired signal is obtained through the approximate maximum a posteriori estimation criterion; S5. By calculating the Capon power spectrum, the interference plus noise covariance matrix is ​​reconstructed based on the covariance matrix estimated by the sampling rhythm. S6. Calculate the beamforming weighting vector using the estimated DOA value of the desired signal and the inverse of the interference plus noise covariance matrix reconstructed from the sampling rhythm. S7. The received multi-sampled signals are weighted by a weighting vector to obtain a single-channel beamforming output.

[0006] Preferably, S1 includes: take over K Beat-hopping frequency GMSK modulation array receives signals:

[0007] in, Represents the desired signal direction vector. Indicates the first One interference source direction vector, It is a GMSK modulated signal. It's an interference signal. It is additive complex white Gaussian noise with a mean of 0. K Indicates the number of snapshots; The GMSK modulated signal is:

[0008]

[0009] in, It is the initial phase of an unknown carrier. It is a length of N The value is A known sequence of symbols, with symbol intervals of . T , It is a phase pulse, defined as the integral of the frequency pulse:

[0010] in, It is the convolution of a rectangular pulse with the response of a Gaussian low-pass filter.

[0011] Preferably, S2 includes: Using a sequence of known symbols GMSK modulated signal The received signal is discretely correlated with a length of . N The relevant operations are performed to obtain the resulting vector:

[0012] in , This represents the number of sampling points per GMSK symbol period on the receiving side.

[0013] Preferably, S3 includes: Estimate the covariance matrices of the received signal and the correlation result vector separately, and preprocess the obtained covariance matrices using diagonal loading:

[0014]

[0015] in, and The diagonal loading coefficient is used. It is an identity matrix.

[0016] Preferably, S4 includes: The DOA of the desired signal is obtained by using the approximate maximum a posteriori estimation criterion:

[0017] Among them, the zeroth-order modified Bessel function is used. exist Maximize the monotonically increasing property of the interval. Equivalent to maximizing its independent variable , For the set of prior angles, To pass Signal vector in The interference plus noise covariance matrix for azimuth estimation:

[0018] in, , It is the width of the interval containing the desired signal to be removed.

[0019] Preferably, S5 includes: By calculating the Capon power spectrum, the interference plus noise covariance matrix is ​​reconstructed based on the covariance matrix estimated by the sampling rhythm. :

[0020] in, .

[0021] Preferably, S6 includes: Using the estimated DOA of the desired signal and the inverse of the interference plus noise covariance matrix reconstructed from the sampling rhythm, the beamforming weighting vector is calculated:

[0022] in, The expected signal DOA estimate, It is the inverse of the interference plus noise covariance matrix.

[0023] As can be seen from the above technical solution, the present invention provides a multi-antenna receiving beamforming method for GMSK frequency hopping signals. Compared with the prior art, the present invention has the following advantages: it accurately estimates the true direction of arrival of the desired signal by using the maximum a posteriori probability method, thereby achieving effective correction of the mismatch direction vector; on this basis, it further employs covariance matrix reconstruction technology to remove the desired signal component from the sampled covariance matrix, effectively suppressing the cancellation phenomenon of the desired signal, and can sense the spatial interference distribution and form a relatively accurate null in the interference direction, significantly improving the output signal-to-interference-plus-noise ratio performance of the system. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating a multi-antenna receiving beamforming method for GMSK frequency hopping signals according to the present invention.

[0025] Figure 2 This is a comparison diagram of the beam pattern of the embodiment of the present invention and the MMSE beamforming method.

[0026] Figure 3 This is a comparison chart showing the output signal-to-interference-plus-noise ratio (SNR) of the beamforming MMSE with the input SNR in an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0028] like Figure 1 As shown in this embodiment, a multi-antenna receiving beamforming method for GMSK frequency hopping signals includes the following steps: S1. Receive the signal received by the K-beat frequency hopping GMSK modulation array; S2. The received signal is discretely correlated using a GMSK modulated signal based on a known symbol sequence, with a correlation length of [length missing]. K The relevant operations are performed to obtain the relevant operation result vector; S3. Estimate the covariance matrix of the received signal and the correlation result vector respectively, and preprocess the obtained covariance matrix using diagonal loading; S4. Obtain the DOA of the desired signal using the approximate maximum a posteriori estimation criterion; S5. By calculating the Capon power spectrum, the interference plus noise covariance matrix is ​​reconstructed based on the covariance matrix estimated by the sampling rhythm. S6. Calculate the beamforming weighting vector using the estimated DOA value of the desired signal and the inverse of the interference plus noise covariance matrix reconstructed from the sampling rhythm. S7. The received multi-sampled signals are weighted by a weighting vector to obtain a single-channel beamforming output.

[0029] Furthermore, S1 includes: take over K Beat-hopping frequency GMSK modulation array receives signals:

[0030] in, Represents the desired signal direction vector. Indicates the first One interference source direction vector, It is a GMSK modulated signal. It's an interference signal. It is additive complex white Gaussian noise with a mean of 0. K Indicates the number of snapshots; The GMSK modulated signal is:

[0031]

[0032] in, It is the initial phase of an unknown carrier. It is a length of N The value is A known sequence of symbols, with symbol intervals of . T , It is a phase pulse, defined as the integral of the frequency pulse:

[0033] in, It is the convolution of a rectangular pulse with the response of a Gaussian low-pass filter.

[0034] Furthermore, S2 includes: Using a sequence of known symbols GMSK modulated signal The received signal is discretely correlated with a length of . K The relevant operations are performed to obtain the resulting vector:

[0035] in , This represents the number of sampling points per GMSK symbol period on the receiving side.

[0036] Furthermore, S3 includes: Estimate the covariance matrices of the received signal and the correlation result vector separately, and preprocess the obtained covariance matrices using diagonal loading:

[0037]

[0038] in, and The diagonal loading coefficient is used. It is an identity matrix.

[0039] Furthermore, S4 includes: The DOA of the desired signal is obtained by using the approximate maximum a posteriori estimation criterion:

[0040] Among them, the zeroth-order modified Bessel function is used. exist Maximize the monotonically increasing property of the interval. Equivalent to maximizing its independent variable , For the set of prior angles, To pass Signal vector in The interference plus noise covariance matrix for azimuth estimation:

[0041] in, , It is the width of the interval containing the desired removed signal, typically taken as 3dB of the main lobe width, i.e. , , M , d These are the wavelength, the number of antenna elements, and the spacing between the elements, respectively.

[0042] Furthermore, S5 includes: By calculating the Capon power spectrum, the interference plus noise covariance matrix is ​​reconstructed based on the covariance matrix estimated by the sampling rhythm. :

[0043] in, .

[0044] Furthermore, S6 includes: Using the estimated DOA of the desired signal and the inverse of the interference plus noise covariance matrix reconstructed from the sampling rhythm, the beamforming weighting vector is calculated:

[0045] in, The expected signal DOA estimate, It is the inverse of the interference plus noise covariance matrix.

[0046] To verify the multi-antenna receiving beamforming method for GMSK frequency hopping signals proposed in this invention, two sets of simulation experiments were conducted for analysis: array antenna beamforming pattern analysis and output signal-to-interference-plus-noise ratio (SINR) performance analysis under different input signal-to-noise ratio (SNR) conditions.

[0047] Consider a uniform linear array with four antenna elements spaced 0.5 wavelengths apart. Three independent and non-interfering signal sources propagate to the array at far-field azimuths of 5 degrees, -35 degrees, and 35 degrees. The first azimuth source represents the desired signal, while the other two represent interference signals. In this embodiment, the desired signal is an independently and identically distributed GMSK modulated signal, and the interference signal is a complex Gaussian signal. The interference-to-noise ratio (INR) at the array receiver is 20 dB, and the background noise is Gaussian white noise. The value is 4, and the number of snapshots is 4000.

[0048] The performance of the method of this invention is analyzed below through simulation results. The method used in the simulation employs a non-simplified maximum a posteriori probability metric. The method of this invention is compared with the minimum mean square error (MMSE) beamforming method.

[0049] like Figure 2 As shown, the method of the present invention is no less capable than the traditional MMSE beamforming method in identifying the direction of the desired signal, and has a deeper degree of suppression in the direction of interference. Compared with the MMSE beamforming method, this method also effectively reduces the sidelobe level of the overall space.

[0050] like Figure 3 As shown, across the entire input SNR range, the method of this invention achieves an overall output SINR that is approximately 3 to 4 dB higher than that of the traditional MMSE beamforming method, indicating that this method has superior output SINR performance.

[0051] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk), etc.

[0052] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. 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 apparatus that includes the element.

[0053] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0054] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for multi-antenna receiving beamforming of GMSK frequency hopping signals, characterized in that, Includes the following steps: S1. Receive K Beat-hopping frequency GMSK modulation array receives signals; S2. Using a GMSK modulated signal based on a known symbol sequence, perform discrete correlation analysis on the received signal with a length of [length missing]. K The relevant operations are performed to obtain the relevant operation result vector; S3. Estimate the covariance matrix of the received signal and the correlation result vector respectively, and preprocess the obtained covariance matrix using diagonal loading; S4. Based on the covariance matrix, the DOA of the desired signal is obtained through the approximate maximum a posteriori estimation criterion; S5. Reconstruct the disturbance plus noise covariance matrix based on the estimated covariance matrix; S6. Calculate the beamforming weighting vector using the estimated DOA value of the desired signal and the inverse of the interference plus noise covariance matrix reconstructed from the sampling rhythm. S7. The received multi-sampled signals are weighted by a weighting vector to obtain a single-channel beamforming output.

2. The multi-antenna receiving beamforming method for GMSK frequency hopping signals according to claim 1, characterized in that: S1 includes: take over K Beat-hopping frequency GMSK modulation array receives signals: in, Represents the desired signal direction vector. Indicates the first One interference source direction vector, It is a GMSK modulated signal. It's an interference signal. It is additive complex white Gaussian noise with a mean of 0. K Indicates the number of snapshots; The GMSK modulated signal is: in, It is the initial phase of an unknown carrier. It is a length of N The value is A known sequence of symbols, with symbol intervals of . T , It is a phase pulse, defined as the integral of the frequency pulse: in, It is the convolution of a rectangular pulse with the response of a Gaussian low-pass filter.

3. The multi-antenna receiving beamforming method for GMSK frequency hopping signals according to claim 2, characterized in that: S2 includes: Using a sequence of known symbols GMSK modulated signal The received signal is discretely correlated with a length of . K The relevant operations are performed to obtain the resulting vector: in , This represents the number of sampling points per GMSK symbol period on the receiving side.

4. The multi-antenna receiving beamforming method for GMSK frequency hopping signals according to claim 3, characterized in that: S3 includes: Estimate the covariance matrices of the received signal and the correlation result vector separately, and preprocess the obtained covariance matrices using diagonal loading: in, and The diagonal loading coefficient is used. It is an identity matrix.

5. The multi-antenna receiving beamforming method for GMSK frequency hopping signals according to claim 4, characterized in that: S4 includes: The DOA of the desired signal is obtained by using the approximate maximum a posteriori estimation criterion: Among them, the zeroth-order modified Bessel function is used. exist Maximize the monotonically increasing property of the interval. Equivalent to maximizing its independent variable , For the set of prior angles, To pass Signal vector in The interference plus noise covariance matrix for azimuth estimation: in, , It is the width of the interval containing the desired signal to be removed.

6. The multi-antenna receiving beamforming method for GMSK frequency hopping signals according to claim 5, characterized in that: S5 includes: By calculating the Capon power spectrum, the interference plus noise covariance matrix is ​​reconstructed based on the covariance matrix estimated by the sampling rhythm. : in, .

7. The multi-antenna receiving beamforming method for GMSK frequency hopping signals according to claim 6, characterized in that: S6 includes: Using the estimated DOA of the desired signal and the inverse of the interference plus noise covariance matrix reconstructed from the sampling rhythm, the beamforming weighting vector is calculated: in, The expected signal DOA estimate, It is the inverse of the interference plus noise covariance matrix.

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