Broadband communication signal extraction method of underwater acoustic mobile platform under strong noise background

Abstract

The invention discloses a broadband communication signal extraction method of an underwater acoustic mobile platform under a strong noise background, and belongs to the field of underwater wireless communication and array signal processing thereof. The problems that under the background of underwater sound long-time broadband signals and a small-aperture array, a traditional signal extraction method is high in complexity, and it cannot be guaranteed that information is not distorted under strong interference are solved. The method comprises the following steps of: preprocessing communication signals under array elements corresponding to channels, and performing FFT (Fast Fourier Transform) on each array element signal; and sub-band division constraint is carried out, bandwidth weight vectors of the sub-bands are constructed according to the center frequencies of the sub-bands, the bandwidth weight vectors of the sub-bands are utilized to carry out weighted combination on the frequency domain signals of all the array elements, the frequency domain signals of all the sub-bands are spliced and then are converted back to the time domain through IFFT, and signal extraction is completed. The method is mainly used for underwater communication signal extraction.

Description

Technical Field

[0001] This invention belongs to the field of underwater wireless communication and array signal processing. Background Technology

[0002] With the development of underwater acoustic communication technology, direct-sequence spread spectrum (DSSS) communication signals have been widely used due to their advantages such as multipath resistance and low interception rate. Due to the unique characteristics of underwater acoustic channels, these signals are characterized by long frame times and large bandwidths. Unlike conventional radar or sonar pulse signals, the ultimate goal of communication signals is to recover the transmitted bit information without distortion. Therefore, any intermediate technique used for communication signal processing, especially spatial filtering (beamforming), must meet a core and stringent prerequisite: it must not introduce distortion sufficient to cause bit errors. This means that filtering algorithms must, while suppressing noise and interference, maintain the temporal waveform and phase integrity of the signal to the greatest extent possible. However, this constraint, crucial in broadband communication systems, has not been fully discussed and resolved in current spatial filtering research.

[0003] Traditional spatial filtering algorithms, such as Bartlett beamformer (BNF) and minimum variance distortionless response (MVDR) beamformer, face dual challenges when applied to this scenario: ① Computational complexity challenge: Broadband signal processing requires high-resolution computation within a large bandwidth. BNF needs to calculate the covariance matrix at each frequency point in the frequency domain, while MVDR involves complex matrix inversion operations. This computational complexity is too high for the limited processing capabilities of underwater mobile platforms, making it difficult to meet the needs of real-time communication. ② Physical and performance challenges: Especially for underwater acoustic mobile platforms, small-aperture arrays (few elements) are often used due to platform size limitations. When the signal frequency is high and the wavelength is short, the element spacing often fails to satisfy the half-wavelength theorem, leading to severe spatial aliasing. Under these conditions, the performance of traditional algorithms (especially high-resolution MVDR) deteriorates sharply. Not only is it unable to effectively suppress interference, but its weight mismatch can also severely distort the desired signal, fatally impacting the subsequent demodulation and decoding process, i.e., directly interfering with communication bits. To address the complexity issue, existing technologies have proposed several methods to reduce complexity, such as subband decomposition and adaptive weighted dimensionality reduction. However, most of these methods originate from radar or speech processing fields, and their designs do not consider the requirements of bit integrity in communication signals. Simple downsampling or independent subband processing may disrupt the phase continuity of the signal, while coarse approximations may introduce nonlinear distortion. These distortions are unacceptable in communication systems because they directly lead to increased bit error rate (BER), causing communication link failures.

[0004] A literature search revealed the following documents, all of which studied spatial filtering techniques for underwater acoustic broadband communication:

[0005] Yang Yixin, Sun Chao, Ma Yuanliang. Broadband low sidelobe temporal beamforming [J]. Acta Acustica, 2003, 28(4): 331-338. (hereinafter referred to as Reference 1);

[0006] Gao Shan, Zhang Weiyu, Guo Lianghao, Ren Suiling, Qu Songyue, He Chengzhen. A method for estimating the direction of arrival of a surface array by combining step beamforming and deconvolution [J]. Acta Acustica, 2025. (hereinafter referred to as Reference 2);

[0007] Xu Guang, Zhou Shengzeng. Research progress of MVDR adaptive beamforming technology in underwater acoustics [J]. Acoustic Technology, 2014, 33(6): 554-558. (hereinafter referred to as Reference 3);

[0008] Reference 1 proposes a low-sidelobe beamforming method based on an FIR time-domain filter for broadband signals. Through time delay compensation and weighted superposition, it achieves a constant beamwidth and low sidelobe level across the entire frequency band, improving the spatial resolution of broadband signals. Theoretically, this method can well maintain the broadband characteristics of the signal. However, due to the high order of the FIR filter, the complexity of real-time implementation, and the lack of consideration for signal phase continuity constraints, it is prone to introducing group delay mismatch and phase distortion when applied to underwater acoustic communication systems, affecting the accuracy of subsequent bit recovery.

[0009] Reference 2 proposes a dimensionality reduction method combining stepwise beamforming and deconvolution. By processing high-dimensional array data stepwise and performing deconvolution estimation in a low-dimensional subspace, the computational complexity of beamforming is effectively reduced, achieving good spatial resolution performance in area array orientation estimation. This method can improve processing efficiency to some extent, but its stepwise dimensionality reduction strategy is mainly geared towards target detection and localization scenarios, without considering the fidelity of communication signal waveforms as an optimization objective. Under conditions of strong noise and Doppler shift, the consistency of time-domain waveforms is difficult to guarantee.

[0010] Reference 3 reviews the research progress of MVDR adaptive beamforming and its robust improvement methods in the field of underwater acoustics. By introducing mechanisms such as constraint optimization and diagonal loading, the robustness against array errors and interference is improved, and good interference suppression performance can be maintained in complex marine environments. However, this type of method has high computational complexity, and the "distortion-free" constraint mainly focuses on spatial directional gain, without specifically considering the time domain and phase integrity of the signal in the communication system. When the array aperture is small or the steering vector is mismatched, it can easily lead to distortion of the desired signal waveform, thereby affecting the bit error rate performance of the communication system.

[0011] Furthermore, the methods in the three aforementioned documents are all sensitive to environmental non-stationarity and the broadband characteristics of signals, making it difficult to balance robustness and real-time performance, and none of them specifically analyze communication signals. Therefore, how to achieve efficient strong interference suppression and reduce algorithm complexity in broadband communication systems and weak signal target scenarios under small aperture and spatial aliasing (insufficient array element number, array spacing exceeding half a wavelength) environments is one of the key issues that urgently needs to be addressed. Summary of the Invention

[0012] The purpose of this invention is to address the problems of high complexity and inability to guarantee information integrity under strong interference in traditional signal extraction methods for long-duration broadband underwater acoustic signals and small-aperture arrays. This invention provides a method for extracting broadband communication signals from a mobile underwater acoustic platform under strong noise conditions. Strong noise refers to noise levels that are 15-20 dB higher than the received signal.

[0013] A method for extracting broadband communication signals from a hydroacoustic mobile platform under high noise conditions, the method comprising:

[0014] Step 1: Based on the multi-channel array elements of the small aperture array, the same underwater communication signal is received simultaneously, and the communication signal under the corresponding array element of each channel is preprocessed.

[0015] Step Two, Matchup Received time-domain communication signals at all frequency points after discrete processing Perform FFT transformation to obtain array elements frequency domain signal ; , The total number of array elements. , For frequency point index, This represents the total number of frequency points.

[0016] Step 3: Utilize constraint factors Subband division constraints are applied to the communication bandwidth of the pair to determine each subband. The bandwidth of all subbands is the same, and all are... ; , For sub-band index, This represents the total number of sub-bands.

[0017] Step 4: Obtain sub-bands center frequency and according to Construct sub-band bandwidth weight vector ;

[0018] Step 5, in the sub-band Within the bandwidth, utilize The frequency domain signals of all array elements are weighted and combined to obtain the sub-band. Frequency domain signal ;

[0019] Step 6: Concatenate the frequency domain signals from all sub-bands according to the sub-band division order to form the frequency domain signal. frequency domain signal The overall time-domain signal under the small aperture array is obtained by IFFT transformation back to the time domain. Signal extraction is completed.

[0020] Preferably, the preprocessing of the communication signals under the corresponding array elements of each channel includes bandpass filtering and downsampling.

[0021] Preferably, ;

[0022] in, This is the lower cutoff frequency of the signal.

[0023] Preferably, in step four, according to Construct sub-band bandwidth weight vector The implementation method is as follows:

[0024] Step 41, according to ,Sure ; For small aperture arrays at the center frequency The response vector to the target's orientation;

[0025] Step 42, according to Determine sub-band bandwidth weight vector .

[0026] Preferably,

[0027] ;

[0028] ;

[0029] ;

[0030] in, For the interference projection matrix, It is the identity matrix. This is the response vector matrix at the direction of the interference. To interfere with the direction The response vector, , The total number of interference directions. This is the conjugate transpose.

[0031] Preferably, ;

[0032] in, For transpose, Indicates the direction of the incoming wave from the target. unit vector, For array element The three-dimensional position coordinates, For imaginary units, The speed of sound in water, It is a natural constant.

[0033] Preferably, in step five, sub-bands are obtained. Frequency domain signal The implementation methods include:

[0034] Step 51: Obtain all array elements in the sub-band Internal frequency domain signal vector ;

[0035] Step 52, according to and Determine sub-band Frequency domain signal .

[0036] Preferably,

[0037] in, Indicates array element In sub-band Frequency domain signals within.

[0038] Preferably, .

[0039] Preferably, , ; The total bandwidth of the received signal. This is the lower cutoff frequency of the signal. This is a constraint factor.

[0040] The beneficial effects of this invention are:

[0041] The broadband communication signal extraction method for underwater acoustic mobile platforms under strong noise backgrounds, as described in this invention, achieves robust signal extraction and interference suppression for broadband communication systems under complex interference and weak signal conditions through simple and efficient processing methods. Specific advantages are as follows:

[0042] 1) This invention extends to scenarios with multiple interference sources and broadband communication, achieving wide-area adaptation and robust operation in complex interference environments, and significantly improving the system's communication performance and interference suppression capabilities;

[0043] 2) This invention, through subband division constraints targeting the characteristics of communication signals, significantly reduces the complexity of broadband signal processing while maintaining the consistency of phase response in the direction of the main signal component, thereby ensuring accurate recovery of communication bits and robust system performance.

[0044] 3) The method of the present invention has a simple processing procedure, relies on a simple structure with low computational overhead, and takes into account both real-time performance and robustness, which can meet the practical application needs in complex marine environments. Attached Figure Description

[0045] Figure 1 This is a schematic diagram illustrating the principle of the broadband communication signal extraction method for underwater acoustic mobile platform under strong noise background described in this invention.

[0046] Figure 2 A schematic diagram illustrating the decoding performance of a communication system under different constraint factors;

[0047] Figure 3 This is a schematic diagram illustrating the decoding performance of the method of the present invention in a simulation environment under a uniform six-element array.

[0048] Figure 4 A schematic diagram illustrating the sea trial data decoding performance of the method of the present invention under a uniform six-element array;

[0049] Figure 5 This is a schematic diagram illustrating the decoding performance of the CBF method for sea trial data under a uniform six-element array. Detailed Implementation

[0050] 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 only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0052] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0053] Specific Implementation Method 1: Combination Figure 1 This embodiment describes a method for extracting broadband communication signals from a hydroacoustic mobile platform under strong noise conditions. The method includes:

[0054] Step 1: Based on the multi-channel array elements of the small aperture array, the same underwater communication signal is received simultaneously, and the communication signal under the corresponding array element of each channel is preprocessed.

[0055] Step Two, Matchup Received time-domain communication signals at all frequency points after discrete processing Perform FFT transformation to obtain array elements frequency domain signal ; , The total number of array elements. , For frequency point index, This represents the total number of frequency points.

[0056] Step 3: Utilize constraint factors Subband division constraints are applied to the communication bandwidth of the pair to determine each subband. The bandwidth of all subbands is the same, and all are... ; , For sub-band index, This represents the total number of sub-bands.

[0057] , ;

[0058] The total bandwidth of the received signal. This is the lower cutoff frequency of the signal. Constraint factors;

[0059] Step 4: Obtain sub-bands center frequency and according to Construct sub-band bandwidth weight vector ;

[0060] Step 5, in the sub-band Within the bandwidth, utilize The frequency domain signals of all array elements are weighted and combined to obtain the sub-band. Frequency domain signal ;

[0061] Step 6: Concatenate the frequency domain signals from all sub-bands according to the sub-band division order to form the frequency domain signal. frequency domain signal The overall time-domain signal under the small aperture array is obtained by IFFT transformation back to the time domain. Complete signal extraction;

[0062] , This is an IFFT transform.

[0063] This invention decomposes broadband processing into multiple low-dimensional narrowband problems by using physical constraint-based subband partitioning and structured weighted merging, thereby significantly reducing computational complexity. At the same time, it uses weight vectors with consistent amplitude within subbands to suppress strong noise and interference, ensuring that the reconstructed broadband signal is not distorted, thus achieving robust communication signal extraction in a strong interference environment for mobile platforms.

[0064] By utilizing subband division constraints tailored to the characteristics of communication signals, the complexity of broadband signal processing is reduced while maintaining the consistency of phase response in the direction of the main signal component, thereby ensuring accurate recovery of communication bits and system robustness.

[0065] Furthermore, the preprocessing methods for the communication signals under the corresponding array elements of each channel include bandpass filtering and downsampling.

[0066] Furthermore, ;

[0067] in, This is the lower cutoff frequency of the signal.

[0068] Furthermore, in step four, according to Construct sub-band bandwidth weight vector The implementation method is as follows:

[0069] Step 41, according to ,Sure ; For small aperture arrays at the center frequency The response vector to the target's orientation;

[0070] Step 42, according to Determine sub-band bandwidth weight vector Specifically,

[0071] ;

[0072] ;

[0073] ;

[0074] ;

[0075] in, For the interference projection matrix, It is the identity matrix. This is the response vector matrix at the direction of the interference. To interfere with the direction The response vector, , The total number of interference directions. For conjugate transpose For transpose, Indicates the direction of the incoming wave from the target. unit vector, For array element The three-dimensional position coordinates, For imaginary units, The speed of sound in water, It is a natural constant.

[0076] This preferred method describes the construction of the bandwidth weight vector. In this method, the bandwidth weight vector is directly constructed from the sub-band center frequency and the array physical characteristics, without the need for high-dimensional covariance matrix estimation or iterative optimization, making the weighting within each sub-band consistent and stable in amplitude and phase. Therefore, it not only significantly reduces the computational complexity, but also maintains the coherent gain and structural integrity of the desired signal under strong noise and strong interference background, thereby effectively improving the robustness and distortion-free capability of broadband signal extraction.

[0077] In this preferred approach, the algorithm is extended to multiple interference sources and broadband communication scenarios, enabling wide-area adaptation and robust operation in complex interference environments, and significantly improving communication performance and interference suppression capabilities.

[0078] Furthermore, in step five, the sub-band is obtained. Frequency domain signal The implementation methods include:

[0079] Step 51: Obtain all array elements in the sub-band Internal frequency domain signal vector ;in,

[0080] ;

[0081] Indicates array element In sub-band Frequency domain signals within;

[0082] Step 52, according to and Determine sub-band Frequency domain signal ;in,

[0083] .

[0084] This preferred embodiment constructs an overall frequency domain signal by performing weighted merging within each sub-band and then splicing them together in sub-band order, so that each spectrum segment has robust amplitude and phase consistency, avoiding energy leakage and inter-band distortion caused by processing directly in the full bandwidth.

[0085] Example:

[0086] The broadband communication signal extraction method for a hydroacoustic mobile platform under strong noise background proposed in this invention was verified by simulation. The simulation was performed using a uniform 15-element linear array. The basic signal parameters are shown in Table 1, and the basic array parameters are shown in Table 2.

[0087] Table 1 Simulation signal parameters

[0088]

[0089] Table 2. Array Parameters of Uniform Linear Array

[0090]

[0091] The array spacing is chosen to be half the wavelength corresponding to the highest frequency, ensuring that no grating lobes appear in the array beam within the signal band. Under the above signal parameters and array configuration simulation conditions, different constraint factors are discussed. In this context, the complexity and communication performance of the proposed method are compared. As shown in Table 3, the smaller the constraint factor, the lower the complexity. Figure 2 To evaluate the bit error rate (BER) performance of communication systems under different constraint factors, Monte Carlo simulations were performed 500 times for each signal-to-noise ratio. Figure 2 It was found that when the constraint factor is less than 200, the algorithm's performance begins to decline sharply, and it may even fail. Therefore, based on experience, the constraint factor should be set to 200 as the optimal value. At this value, the system not only reduces the complexity of broadband signal processing but also maintains the consistency of the phase response in the direction of the principal components of the signal.

[0092] Table 3. Algorithm complexity for different constraint factor sizes

[0093] The simulations focused on the performance of the proposed method under an ultra-short six-element array configuration. The array element parameters are shown in Table 4 below, and the sea trial data were configured in the same way.

[0094] Table 4. Uniform Six-Element Array Parameters

[0095]

[0096] Simulation results are as follows Figure 3 As shown, the algorithm's communication BER processing performance in strong interference environments is the same as that of AWGN. The improved BNF filtering technique can effectively filter out strong interference, while the CBF method fails. The complexity is reduced by 64 times, and it does not affect the receiver's communication signal performance.

[0097] Finally, the broadband communication signal extraction method for underwater acoustic mobile platforms under strong noise backgrounds proposed in this invention was verified through field data processing. The field test and simulation used the same signal parameters and receiver array type. A frame of DSSS-QPSK signal was selected, and the method of this invention and the CBF method were compared. The processed decoding constellation diagrams are shown below. Figure 4 and Figure 5 As shown, by calculating the output signal-to-noise ratio using a constellation diagram, the method of this invention improves the signal-to-noise ratio by 3 dB compared to the CBF method, proving that the performance of the method of this invention is twice that of the traditional technique.

[0098] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. A method for extracting broadband communication signals from a hydroacoustic mobile platform under strong noise background, characterized in that, The method includes: Step 1: Based on the multi-channel array elements of the small aperture array, the same underwater communication signal is received simultaneously, and the communication signal under the corresponding array element of each channel is preprocessed. Step Two, Matchup Received time-domain communication signals at all frequency points after discrete processing Perform FFT transformation to obtain array elements frequency domain signal ; , The total number of array elements. , For frequency point index, This represents the total number of frequency points. Step 3: Utilize constraint factors Subband division constraints are applied to the communication bandwidth of the pair to determine each subband. The bandwidth of all subbands is the same, and all are... ; , For sub-band index, This represents the total number of sub-bands. Step 4: Obtain sub-bands center frequency and according to Construct sub-band bandwidth weight vector ; Step 5, in the sub-band Within the bandwidth, utilize The frequency domain signals of all array elements are weighted and combined to obtain the sub-band. Frequency domain signal ; Step 6: Concatenate the frequency domain signals from all sub-bands according to the sub-band division order to form the frequency domain signal. frequency domain signal The overall time-domain signal under the small aperture array is obtained by IFFT transformation back to the time domain. Signal extraction is completed.

2. The method for extracting broadband communication signals from a hydroacoustic mobile platform under strong noise background according to claim 1, characterized in that, The methods for preprocessing the communication signals under the corresponding array elements of each channel include bandpass filtering and downsampling.

3. The method for extracting broadband communication signals from a hydroacoustic mobile platform under strong noise background according to claim 1, characterized in that, ； in, This is the lower cutoff frequency of the signal.

4. The method for extracting broadband communication signals from a hydroacoustic mobile platform under strong noise background according to claim 1, characterized in that, In step four, according to Construct sub-band bandwidth weight vector The implementation method is as follows: Step 41, according to ,Sure ; For small aperture arrays at the center frequency The response vector to the target's orientation; Step 42, according to Determine sub-band bandwidth weight vector .

5. The method for extracting broadband communication signals from a hydroacoustic mobile platform under strong noise background according to claim 4, characterized in that, ； ； ； in, For the interference projection matrix, It is the identity matrix. This is the response vector matrix at the direction of the interference. To interfere with the direction The response vector, , The total number of interference directions. This is the conjugate transpose.

6. The method for extracting broadband communication signals from a hydroacoustic mobile platform under strong noise background according to claim 4, characterized in that, ； in, For transpose, Indicates the direction of the incoming wave from the target. unit vector, For array element The three-dimensional position coordinates, For imaginary units, The speed of sound in water, It is a natural constant.

7. The method for extracting broadband communication signals from a hydroacoustic mobile platform under strong noise background according to claim 1, characterized in that, In step five, the sub-band is obtained. Frequency domain signal The implementation methods include: Step 51: Obtain all array elements in the sub-band Internal frequency domain signal vector ; Step 52, according to and Determine sub-band Frequency domain signal .

8. The method for extracting broadband communication signals from a hydroacoustic mobile platform under strong noise background according to claim 7, characterized in that, in, Indicates array element In sub-band Frequency domain signals within.

9. The method for extracting broadband communication signals from a hydroacoustic mobile platform under strong noise background according to claim 1, characterized in that, 。 10. The method for extracting broadband communication signals from a hydroacoustic mobile platform under strong noise background according to claim 1, characterized in that, , ; The total bandwidth of the received signal. This is the lower cutoff frequency of the signal. This is a constraint factor.

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