An acoustic monitoring system and method based on multi-frequency measurement and grating lobe rejection

Abstract

The present invention relates to the field of underwater acoustic detection technology, and more specifically to an acoustic monitoring system and method based on multi-frequency measurement and grating lobe rejection. The system primarily comprises a transmitting module, a receiving array, and a signal processing module. By integrating multi-frequency measurement, grating lobe rejection, sparse array design, and a weighted splicing algorithm, the present invention achieves high-precision, wide-angle acoustic monitoring with ≤32 channels. The system also offers advantages such as low cost, high flexibility, strong anti-interference capabilities, strong real-time performance, high resolution, and adaptability to complex environments, promising promising engineering applications.

Description

Technical Field

[0001] The present invention relates to the field of underwater acoustic detection technology, and specifically to an acoustic monitoring system and method based on multi-frequency measurement and grating lobe rejection, and especially to a low-cost, high-precision acoustic monitoring system and method that achieves wide-angle coverage through multi-frequency measurement and grating lobe rejection. Background Art

[0002] In underwater sonar, security monitoring, underwater acoustic communications, and other fields, the angular resolution and anti-interference capabilities of acoustic array systems directly impact target identification and positioning accuracy. When the number of channels in traditional uniform linear arrays is limited (e.g., ≤32 channels), the element spacing often exceeds half a wavelength, resulting in the appearance of grating lobes in the radiation pattern. These false angles mimic the energy response of real targets and can easily lead to misjudgment. To address this issue, existing technologies typically increase the number of array channels, employ non-uniform arrays, or introduce super-resolution algorithms. However, increasing the number of channels significantly increases system cost. While non-uniform arrays can suppress grating lobes, the stability of angle estimation decreases. Super-resolution algorithms can improve resolution, but they require a high signal-to-noise ratio (SNR) and are computationally complex, making them difficult to deploy in real time. Therefore, there is an urgent need for a low-cost acoustic monitoring system that can effectively identify and eliminate grating lobe interference, achieve high accuracy, and achieve wide-angle coverage under limited channel count conditions. Summary of the Invention

[0003] The present invention aims to provide an acoustic monitoring system that achieves wide-angle coverage and high-precision direction-of-arrival estimation under a limited number of channels (e.g., ≤32 channels). Frequency diversity is achieved through multi-frequency measurement, and combined with a grating lobe rejection algorithm, it effectively suppresses grating lobe interference caused by sparse non-uniform arrays, thereby improving the system's monitoring reliability and resolution in complex acoustic environments.

[0004] According to a first aspect of the present invention, an acoustic monitoring system based on multi-frequency measurement and grating lobe rejection is provided, comprising: a transmitting module, the transmitting module comprising at least one broadband transducer for sequentially transmitting acoustic wave signals at multiple frequency points; a receiving array module, the receiving array module being a 32-channel broadband sparse receiving array for receiving acoustic wave signals reflected or transmitted by a target when the array field of view covers an angle of 0° to 180°; a signal processing module for performing beamforming operations on each transmitting frequency, outputting a directional pattern, obtaining candidate grating lobe angles in combination with the principle of spatial frequency periodicity, marking grating lobes through multi-frequency commonality analysis according to a grating lobe rejection algorithm, deleting grating lobe angles from an initial angle detection set, and performing weighted splicing on the powers corresponding to the detection angles verified by physical constraints based on a valid angle verification strategy and a weighted splicing algorithm to obtain a fused directional pattern.

[0005] Optionally, the broadband transducer adopts a frequency diversity measurement strategy, and the transmission signal form includes a continuous wave, a pulse wave or a coded signal.

[0006] Optionally, each channel in the 32-channel broadband sparse receiving array is configured with a hydrophone, and array elements are arranged with non-uniform spacing.

[0007] Optionally, the non-uniform spacing arrangement is a logarithmic periodic arrangement to ensure the stability of the directivity pattern at multiple frequencies and reduce the probability of occurrence of grating lobes.

[0008] Optionally, the signal processing module includes: a multi-frequency beamformer, used to perform beamforming operations on each transmission frequency and output a radiation pattern; a grating lobe position real-time calculation module, used to obtain candidate grating lobe angles based on the principle of spatial frequency periodicity; a data fusion processor, used to mark the grating lobes through multi-frequency commonality analysis according to a grating lobe rejection algorithm, delete the grating lobe angles from the initial angle detection set, and perform weighted splicing on the powers corresponding to the verified valid angles based on the effective angle verification strategy and the weighted splicing algorithm to obtain a fused radiation pattern.

[0009] Optionally, the grating lobe position real-time calculation module includes: an array pattern calculation submodule, which is used to scan the angle space using a beamforming algorithm to obtain a pattern; a peak position detection submodule, which is used to screen all local peak angles and exclude the main lobe according to the pattern, and use the remaining peak angles as grating lobe candidate angles; a periodicity verification submodule, which is used to determine whether the grating lobe candidate angles satisfy the periodic relationship of the theoretical grating lobe, and output the grating lobe candidate angles that satisfy the periodic relationship of the theoretical grating lobe.

[0010] Optionally, the data fusion processor includes: a grating lobe rejection submodule, which is used to mark the grating lobes through multi-frequency commonality analysis according to the grating lobe rejection algorithm, and delete the angles of the grating lobes from the initial angle detection set; an effective angle verification and weighted splicing submodule, which is used to obtain the verified effective angles based on the effective angle set and the effective angle verification strategy, and combine the weighted splicing algorithm to perform weighted splicing on the powers corresponding to the verified effective angles to obtain a fusion pattern.

[0011] Optionally, the grating lobe rejection submodule includes: an initial angle detection submodule, which is used to obtain an initial angle detection set and corresponding power using a super-resolution algorithm; a grating lobe identification and marking submodule, which is used to detect the initial angle detection set separately in different frequency bands, retain the common angles, and mark them as grating lobe angles; a data rejection submodule, which is used to delete the angles marked as grating lobes from the initial angle detection set to obtain a processing angle set; and an effective angle optimization submodule, which is used to locally optimize the remaining angles in the processing angle set using a combination of spatial smoothing and cluster analysis technology to obtain an effective angle set.

[0012] Optionally, the effective angle verification and weighted splicing submodule includes: a subarray data segmentation submodule, used to divide the 32-channel sparse array into multiple subarrays, and perform beamforming processing on each subarray separately; an adaptive weighting submodule, used to calculate the signal-to-noise ratio and sidelobe level of the output signal of each subarray, and determine whether the detection angle of each subarray and the signal-to-noise ratio of the output signal of each subarray meet the effective angle verification strategy, wherein the effective angle verification strategy is a physical constraint verification mechanism with angle rationality and a set signal-to-noise ratio threshold as constraints, and adaptively allocates weights to the power corresponding to the detection angle of each subarray that has passed the verification based on the signal-to-noise ratio and sidelobe suppression capability; a fusion output submodule, used to perform weighted splicing on the power corresponding to the verified effective angle based on the weighted splicing algorithm to obtain a fusion direction map.

[0013] According to a second aspect of the present invention, an acoustic monitoring method based on multi-frequency measurement and grating lobe rejection is provided, which is applied to the acoustic monitoring system based on multi-frequency measurement and grating lobe rejection, and includes the following steps: transmitting acoustic wave signals in sequence at multiple frequency points through a transmitting module, wherein the transmitting module includes at least one broadband transducer; receiving acoustic wave signals reflected or transmitted by a target under an array field of view covering 0° to 180° through a receiving array module, wherein the receiving array module is a 32-channel broadband sparse receiving array; performing beamforming operations on each transmitting frequency through a signal processing module, outputting a directional pattern, obtaining candidate grating lobe angles in combination with the principle of spatial frequency periodicity, marking grating lobes through multi-frequency commonality analysis according to a grating lobe rejection algorithm, deleting the grating lobe angles from the initial angle detection set, and performing weighted splicing on the powers corresponding to the detection angles verified by physical constraints based on a valid angle verification strategy and a weighted splicing algorithm to obtain a fused directional pattern.

[0014] The present invention provides an acoustic monitoring system and method based on multi-frequency measurement and grating lobe rejection, which have the following beneficial effects: 1. Low cost: Compared with the large number of receiving channels of a multi-beam system, the use of a 32-channel broadband sparse receiving array can greatly reduce the hardware complexity and cost; 2. High flexibility: Through the combination of grating lobe rejection algorithm, effective angle verification strategy and weighted splicing algorithm, it can support multi-frequency measurement and various beamforming algorithms, and can be adapted to different application scenarios (such as rivers and reservoirs); 3. Strong anti-interference ability: Through multi-frequency commonality analysis and SNR verification mechanism, grating lobe interference can be effectively suppressed; 4. Strong real-time performance: supports parallel computing and weighted splicing algorithm, suitable for real-time monitoring scenarios; 5. High resolution: combined with super-resolution algorithm and local optimization, it can improve the angle estimation accuracy; 6. Good environmental adaptability: with physical constraints and signal-to-noise ratio verification mechanism, it is suitable for complex acoustic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a structural block diagram of an acoustic monitoring system based on multi-frequency measurement and grating lobe removal according to an embodiment of the present invention.

[0016] Figure 2 4 is a structural block diagram of a signal processing module according to an embodiment of the present invention.

[0017] Figure 3 4 is a structural block diagram of a real-time calculation module for grating lobe positions according to an embodiment of the present invention.

[0018] Figure 4 4 is a structural block diagram of a data fusion processor according to an embodiment of the present invention.

[0019] Figure 5 4 is a structural block diagram of a grating lobe removal submodule according to an embodiment of the present invention.

[0020] Figure 6 This is a structural block diagram of the effective angle verification and weighted stitching submodule of an embodiment of the present invention.

[0021] Figure 7 This is a flow chart of an acoustic monitoring method based on multi-frequency measurement and grating lobe rejection according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and are not to be construed as limitations on the present application. In addition, the following embodiments and features in the embodiments may be combined with each other unless there is a conflict. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0023] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0024] Example 1

[0025] Reference Attachment Figure 1-6The present application provides an acoustic monitoring system based on multi-frequency measurement and grating lobe rejection. The system mainly includes a transmitting module, a receiving array, and a signal processing module. The system realizes low-cost, high-precision, and wide-angle acoustic monitoring capabilities through multi-band transmission and reception, combined with beamforming, grating lobe detection and rejection, and weighted splicing algorithms.

[0026] In some optional implementations of the present application, the transmitting module includes at least one broadband transducer for sequentially transmitting acoustic wave signals at multiple frequency points. The operating frequency range of the broadband transducer is 20 kHz to 120 kHz, and it supports a programmable frequency source and has good frequency response flatness. Its working mode adopts a frequency diversity measurement strategy, that is, acoustic wave signals are sequentially transmitted at multiple frequency points (such as 20kHz, 50kHz, and 100kHz) to enhance the robustness of angle estimation. In addition, the broadband transducer is arranged on a fixed or mobile platform, and the transmitted signal can be a continuous wave, a pulse wave, or a coded signal. The specific form is flexibly selected according to the application scenario.

[0027] In some optional implementations of the present application, the receiving array module is a 32-channel broadband sparse receiving array, which is used to receive acoustic wave signals reflected or transmitted by the target when the array field of view covers 0° to 180°.

[0028] In some optional implementations of the present application, each channel in the 32-channel broadband sparse receiving array is configured with a hydrophone, and the array element spacing is arranged in a non-uniform spacing arrangement. Preferably, the non-uniform spacing arrangement is a logarithmic periodic arrangement to ensure the stability of the radiation pattern at multiple frequencies and reduce the probability of the occurrence of grating lobes.

[0029] It should be noted that when designing the array element spacing of the 32-channel broadband sparse receiving array involved in this application, it is necessary to take into account the stability of the directional pattern at different frequencies. In addition, each channel is equipped with a hydrophone, which has the advantages of high sensitivity and wide dynamic range, and supports effective signal reception in complex acoustic environments.

[0030] In some optional implementations of the present application, the signal processing module is used to perform beamforming operations on each transmission frequency separately, output a directional pattern, obtain candidate grating lobe angles in combination with the principle of spatial frequency periodicity, and mark the grating lobes through multi-frequency commonality analysis based on the grating lobe rejection algorithm, delete the grating lobe angles from the initial angle detection set, and based on the effective angle verification strategy and the weighted stitching algorithm, perform weighted stitching on the powers corresponding to the detection angles verified by physical constraints to obtain a fused directional pattern.

[0031] In some optional implementations of this application, please refer to the attached Figure 2The signal processing module is the core processing module, which includes the following sub-modules: a multi-frequency beamformer, which is used to perform beamforming operations on each transmission frequency and output a radiation pattern; a grating lobe position real-time calculation module, which is used to obtain candidate grating lobe angles based on the principle of spatial frequency periodicity; a data fusion processor, which is used to mark the grating lobes through multi-frequency commonality analysis according to the grating lobe rejection algorithm, delete the grating lobe angles from the initial angle detection set, and perform weighted splicing on the powers corresponding to the verified valid angles based on the valid angle verification strategy and weighted splicing algorithm to obtain a fused radiation pattern.

[0032] It should be noted that the beamforming output is the complete beamforming output, which is used to describe the beamforming output of subarray i at all angles. The response power under , including the main lobe and side lobe information, the power (Power Spectrum) is the power value corresponding to a specific effective angle, that is, the local power sampling of the pattern at that angle.

[0033] In some optional implementations of the present application, the multi-frequency beamformer of the present application receives digital signals from each channel and performs beamforming operations for each transmit frequency. The beamforming algorithm may utilize delay-and-sum (DSS) or Capon beamforming, outputting a directional pattern at each frequency for subsequent angle detection and grating lobe identification.

[0034] In some optional implementations of this application, please refer to the attached Figure 3 The grating lobe position real-time calculation module includes: an array pattern calculation submodule, which is used to use a beamforming algorithm (such as a delay-sum algorithm and a Capon beamforming algorithm) to scan the angle space to obtain a pattern; a peak position detection submodule, which is used to screen all local peak angles and exclude the main lobe according to the pattern, and use the remaining peak angles as candidate grating lobe angles; a periodicity verification submodule, which is used to determine whether the candidate grating lobe angles satisfy the periodic relationship of the theoretical grating lobe, and output the candidate grating lobe angles that satisfy the periodic relationship of the theoretical grating lobe.

[0035] In some optional implementations of the present application, the grating lobe position real-time calculation module is used to identify potential grating lobe positions during the received signal processing process. The input parameters include the speed of sound, the current frequency, and the spacing between array elements. For a uniform linear array, the grating lobe position satisfies the spatial frequency periodicity relationship, and the formula is: , where d is the array element spacing, λ is the wavelength, and n is an integer. For non-uniform or sparse arrays, the grating lobe position can be estimated by numerical simulation or table lookup, and the candidate grating lobe angles can be output.

[0036] In some optional implementations of this application, please refer to the attached Figure 4 The data fusion processor includes: a grating lobe rejection submodule, which is used to mark the grating lobes through multi-frequency commonality analysis according to the grating lobe rejection algorithm, and delete the angles of the grating lobes from the initial angle detection set; an effective angle verification and weighted splicing submodule, which is used to obtain the verified effective angles based on the effective angle set and the effective angle verification strategy, and combine the weighted splicing algorithm to perform weighted splicing on the powers corresponding to the verified effective angles to obtain a fusion pattern.

[0037] In some optional implementations of this application, please refer to the attached Figure 5 The grating lobe rejection submodule includes: an initial angle detection submodule, which is used to obtain the initial angle detection set and the corresponding power by adopting a super-resolution algorithm; a grating lobe identification and marking submodule, which is used to detect the initial angle detection set respectively in different frequency bands, retain the common angles, and mark them as grating lobe angles; a data rejection submodule, which is used to delete the angles marked as grating lobes from the initial angle detection set to obtain a processing angle set; and an effective angle optimization submodule, which is used to locally optimize the remaining angles in the processing angle set by adopting a combination of spatial smoothing and cluster analysis technology to obtain a valid angle set.

[0038] In some optional implementations of the present application, during the initial angle detection and grating lobe identification process, an initial angle detection submodule and a grating lobe identification and labeling submodule are provided, and a super-resolution algorithm (such as MUSIC, Capon, or compressed sensing) is used to perform DOA estimation on multi-band signals to obtain an initial angle detection set and its corresponding power. Angle sets are detected separately at different frequencies, and angles that appear at multiple frequencies are retained as grating lobe candidates. This initial angle detection submodule utilizes the commonality of grating lobes across multiple frequencies to improve identification accuracy.

[0039] In some optional implementations of the present application, during the data removal and effective angle optimization process, a data removal submodule and an effective angle optimization submodule are provided to remove angles marked as grating lobes from the initial angle detection set. The weights corresponding to the grating lobe directions are ignored during the beamforming weighted summation process. Local optimization (e.g., Newton iteration) is performed on the remaining angles to improve estimation accuracy. If multiple similar angles exist (e.g., within ±1°), they are clustered and merged into a single target to form the final effective angle set.

[0040] In some optional implementations of this application, please refer to the attached Figure 6The effective angle verification and weighted splicing submodule includes: a subarray data segmentation submodule, which is used to divide the 32-channel sparse array into multiple subarrays and perform beamforming processing on each subarray separately; an adaptive weighting submodule, which is used to calculate the signal-to-noise ratio and sidelobe level of the output signal of each subarray, and determine whether the detection angle of each subarray and the signal-to-noise ratio of the output signal of each subarray meet the effective angle verification strategy, wherein the effective angle verification strategy is a physical constraint verification mechanism with angle rationality and a set signal-to-noise ratio threshold as constraints, and adaptively allocates weights to the power corresponding to the detection angle of each verified subarray based on the signal-to-noise ratio and sidelobe suppression capability; a fusion output submodule, which is used to perform weighted splicing on the power corresponding to the verified effective angle based on the weighted splicing algorithm to obtain a fusion direction map.

[0041] In some optional implementations of the present application, a valid angle verification strategy is set to verify the detected valid angles to ensure that the angle rationality and signal-to-noise ratio meet the requirements. It is a physical constraint verification mechanism with angle rationality and set signal-to-noise ratio threshold as constraints, specifically including: (1) angle rationality verification, eliminating angles that exceed the array field of view (such as θ> 180°) or do not conform to the acoustic propagation model (such as the target speed-angle correlation in sonar does not correspond), (2) signal-to-noise ratio (SNR) threshold verification, only retaining detection results with SNR > 10 dB, to ensure that the angle estimation is within the valid signal-to-noise ratio range.

[0042] In some optional implementations of the present application, during the weighted splicing process, the output results of multiple sub-arrays are weightedly spliced ​​to improve the angular resolution and system stability. The specific implementation steps include: (1) sub-array data segmentation: dividing the 32-channel sparse array into multiple sub-arrays (such as 4 sub-arrays, each with 8 channels), and performing beamforming operations on each sub-array; (2) adaptive weighting: calculating the SNR and sidelobe level of the output signal of each sub-array, and adaptively allocating weights based on the signal-to-noise ratio and sidelobe suppression capability; (3) fusion output: weighted merging of the outputs of each sub-array to achieve equivalent multi-beam coverage, thereby improving the angular resolution and monitoring stability.

[0043] In some optional implementations of this application, during the subarray partitioning and beamforming process, a 32-channel sparse array is divided into multiple subarrays (e.g., four subarrays, each with eight channels). Each subarray is designed with non-uniform spacing (i.e., logarithmic periodic arrangement) to reduce grating lobe interference. Beamforming is performed separately for each subarray. The beamforming algorithm can adopt a delay-sum or Capon algorithm to output a directivity pattern at the corresponding frequency.

[0044] In some optional implementations of the present application, during the signal-to-noise ratio (SNR) calculation process, the signal-to-noise ratio of the output signal of each sub-array is calculated, and the formula is: ,in, is the signal-to-noise ratio of subarray i, is the main lobe signal power of subarray i, is the background noise power (which can be estimated from the idle period or sidelobe area). During the sidelobe suppression capability evaluation, the maximum sidelobe level of subarray i is calculated through pattern analysis. (Unit: dB), used as a quantitative indicator of sidelobe suppression capability.

[0045] In some optional implementations of the present application, in the adaptive weight allocation process, the weight is determined by the signal-to-noise ratio and the sidelobe suppression capability, and the formula is: ,in, is the output signal of subarray i, γ is the suppression coefficient, is the ratio of the peak power of the highest side lobe of subarray i to the peak power of the main lobe, The weight of each sub-array is adaptively allocated. The logic of weight allocation is that the higher the SNR, the stronger the sub-array signal reliability, and the higher the weight allocation. At the same time, the lower the sidelobe level (the stronger the suppression capability), the better the sub-array's suppression effect on false angles, and the higher the weight allocation.

[0046] In some optional implementations of the present application, during the weighted fusion output process, the input fusion object is: weighted splicing of the powers corresponding to the verified valid angles, and the weighted splicing formula is: ,in, is the output power of subarray i at angle θ, In order to fuse the directional pattern, that is, the fusion directional pattern when the array field of view covers 0° to 180°, the weighted fusion output method is adopted, which has the following effects: (1) Main lobe enhancement: the main lobe characteristics of the high-weight sub-array (such as sub-array 3) are more significant in the fusion result, improving the angular resolution; (2) Side lobe suppression: the side lobe interference of the low-weight sub-array (such as sub-array 4) is effectively suppressed, reducing the probability of false angle misjudgment.

[0047] It should be noted that the effective angle verification and data fusion process involved in the present invention form a closed-loop optimization mechanism. The former is used to ensure the reliability of the input data, and the latter improves the angle estimation accuracy through algorithm fusion. The two work together to achieve low-cost and high-precision monitoring. The core relationship is as follows: 1. Effective angle verification provides reliable input for data fusion, specifically: (1) Physical constraint screening: eliminate angles that exceed the array field of view (such as θ> 180°) or do not conform to the acoustic propagation model (such as the target speed-angle correlation in sonar) to avoid false target interference; (2) Signal-to-noise ratio (SNR) threshold screening: only retain detection results with SNR > 10 dB to ensure that the subsequent fusion process is based on high signal-to-noise ratio data and reduce the impact of noise on angle estimation; (3) Result after grating lobe removal: mark and eliminate grating lobe angles through multi-frequency commonality analysis, retain the true target angle, and avoid false angles from contaminating the fusion result. Its role is: the verified effective angle set provides "clean" input for data fusion, reducing redundant calculations and the risk of misjudgment. 2. The data fusion process relies on the effective angle verification results, specifically: (1) Weight distribution basis: In the weighted splicing algorithm of data fusion, the weight The calculation of the effective angle depends on the SNR and sidelobe suppression capability of the sub-array output. The effective angle verification ensures that the SNR of the input data is greater than 10 dB, so that the weight distribution is more focused on the high signal-to-noise ratio angle to avoid low-quality data interfering with the weight calculation; (2) Fusion object optimization: The weighted summation object of data fusion is the power corresponding to the effective angle that has passed the verification. For example, in a certain sub-array, if a certain detection angle θ fails to pass the angle rationality verification (such as θ> 180°), then its power value It does not participate in the fusion calculation; (3) Dynamic adjustment mechanism: The weight allocation of data fusion needs to update the SNR and sidelobe level in real time, and the effective angle verification ensures that the input SNR data is true and reliable. For example, in multi-frequency measurement, if a certain detection angle at a certain frequency is eliminated due to SNR < 10dB, the weight allocation at this frequency is only based on the SNR value of the remaining effective angle; 3. The technical effect of collaborative optimization is as follows: (1) Suppressing the propagation of false angles: After the effective angle verification eliminates the grating lobe and low signal-to-noise ratio angle, the data fusion only performs weighted splicing on the real target angle to avoid the superposition and amplification of false angles in the fusion result. For example, if a sub-array detects the angle θ = 170∘ (SNR = 8 dB), this angle is eliminated due to SNR < 10 dB, and the data fusion does not include it in the calculation, thereby avoiding low-quality data interfering with the final result; (2) Improving angular resolution: Data fusion enhances the resolution of the effective angle through the mainlobe characteristics of the high-weight sub-array, and the effective angle verification ensures that the mainlobe characteristics of the input data are true and reliable; (3) Formula support: Based on the weights in the weighted splicing formula , which is calculated only based on verified sub-array data to ensure the accuracy of mainlobe enhancement; (4) Enhanced system stability: Through angle rationality verification and SNR screening, the data fusion process avoids calculation fluctuations caused by invalid data, improving the robustness of the system in complex environments.

[0048] The acoustic monitoring system based on multi-frequency measurement and grating lobe rejection provided in this application has the advantages and performance indicators shown in Table 1 below.

[0049] Table 1 Advantages and performance indicators of this system

[0050]

[0051] The acoustic monitoring system based on multi-frequency measurement and grating lobe rejection provided in this application can be widely used in the following scenarios: (1) Underwater acoustic monitoring: used to monitor the position of underwater targets (such as submarines, fish schools, underwater robots); (2) Small sonar system: suitable for low-cost platforms such as unmanned underwater vehicles and underwater drones; (3) Underwater acoustic communication system: used to identify the direction of the communication source and improve communication efficiency; (4) Port security system: used to detect illegal intrusion targets and improve the safety level of the port; (5) Industrial acoustic detection: used for sound source positioning and monitoring in complex industrial environments.

[0052] Example 2

[0053] This embodiment provides an acoustic monitoring method based on multi-frequency measurement and grating lobe rejection on the basis of the above embodiment 1. Figure 7 , an acoustic monitoring system based on multi-frequency measurement and grating lobe rejection applied to Example 1 includes the following steps.

[0054] S1: transmitting acoustic wave signals at multiple frequency points in sequence through a transmitting module, wherein the transmitting module includes at least one broadband transducer.

[0055] S2: Receive the acoustic wave signal reflected or transmitted by the target through the receiving array module with an array field of view covering 0° to 180°. The receiving array module is a 32-channel broadband sparse receiving array.

[0056] S3: The signal processing module performs beamforming operations on each transmission frequency and outputs a directional pattern. The candidate grating lobe angles are obtained by combining the principle of spatial frequency periodicity. The grating lobe is marked by multi-frequency commonality analysis based on the grating lobe rejection algorithm, and the grating lobe angles are deleted from the initial angle detection set. Based on the effective angle verification strategy and weighted stitching algorithm, the power corresponding to the detection angles verified by physical constraints is weightedly spliced ​​to obtain a fused directional pattern.

[0057] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0058] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0059] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0060] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. An acoustic monitoring system based on multi-frequency measurement and grating lobe rejection, characterized in that: include: A transmitting module, comprising at least one broadband transducer, configured to sequentially transmit acoustic wave signals at multiple frequency points; A receiving array module, which is a 32-channel broadband sparse receiving array, is used to receive acoustic wave signals reflected or transmitted by the target with an array field of view covering 0° to 180°; The signal processing module is used to perform beamforming operations on each transmission frequency separately, output a directional pattern, obtain candidate grating lobe angles based on the principle of spatial frequency periodicity, mark grating lobes through multi-frequency commonality analysis according to the grating lobe rejection algorithm, delete the grating lobe angles from the initial angle detection set, and based on the effective angle verification strategy and weighted stitching algorithm, perform weighted stitching on the powers corresponding to the detection angles verified by physical constraints to obtain a fused directional pattern.

2. The acoustic monitoring system based on multi-frequency measurement and grating lobe rejection according to claim 1, characterized in that: The broadband transducer adopts a frequency diversity measurement strategy, and the transmission signal form includes continuous wave, pulse wave or coded signal.

3. The acoustic monitoring system based on multi-frequency measurement and grating lobe rejection according to claim 1, characterized in that: Each channel in the 32-channel broadband sparse receiving array is equipped with a hydrophone, and array elements are arranged in a non-uniform spacing.

4. The acoustic monitoring system based on multi-frequency measurement and grating lobe rejection according to claim 3, characterized in that: The non-uniform spacing arrangement is a logarithmic periodic arrangement, which is used to ensure the stability of the directivity pattern at multiple frequencies and reduce the probability of the occurrence of grating lobes.

5. The acoustic monitoring system based on multi-frequency measurement and grating lobe rejection according to claim 1, characterized in that: The signal processing module includes: A multi-frequency beamformer, configured to perform beamforming operations on each transmit frequency and output a directional pattern; A real-time calculation module for grating lobe positions is used to obtain candidate grating lobe angles based on the principle of spatial frequency periodicity; The data fusion processor is used to mark the grating lobes through multi-frequency commonality analysis according to the grating lobe rejection algorithm, delete the angles of the grating lobes from the initial angle detection set, and perform weighted splicing on the powers corresponding to the verified effective angles based on the effective angle verification strategy and weighted splicing algorithm to obtain a fused radiation pattern.

6. The acoustic monitoring system based on multi-frequency measurement and grating lobe rejection according to claim 5, characterized in that: The grating lobe position real-time calculation module includes: The array pattern calculation submodule is used to scan the angle space using a beamforming algorithm to obtain a pattern; The peak position detection submodule is used to screen all local peak angles according to the radiation pattern and exclude the main lobe, and use the remaining peak angles as candidate grating lobe angles; The periodicity verification submodule is used to determine whether the candidate grating lobe angles satisfy the periodic relationship of the theoretical grating lobe, and output the candidate grating lobe angles that satisfy the periodic relationship of the theoretical grating lobe.

7. The acoustic monitoring system based on multi-frequency measurement and grating lobe rejection according to claim 5, characterized in that: The data fusion processor includes: a grating lobe rejection submodule, configured to mark grating lobes through multi-frequency commonality analysis according to a grating lobe rejection algorithm and remove the angles of the grating lobes from the initial angle detection set; The effective angle verification and weighted splicing submodule is used to obtain the verified effective angles based on the effective angle set and the effective angle verification strategy, and combine the weighted splicing algorithm to perform weighted splicing on the powers corresponding to the verified effective angles to obtain a fused directional diagram.

8. The acoustic monitoring system based on multi-frequency measurement and grating lobe rejection according to claim 7, characterized in that: The grating lobe rejection submodule includes: The initial angle detection submodule is used to obtain the initial angle detection set and corresponding power using a super-resolution algorithm; The grating lobe identification and marking submodule is used to detect the initial angle detection set in different frequency bands, retain the common angles, and mark them as grating lobe angles; A data removal submodule is used to remove angles marked as grating lobes from the initial angle detection set to obtain a processed angle set; The effective angle optimization submodule is used to locally optimize the remaining angles in the processed angle set by combining spatial smoothing and cluster analysis technology to obtain the effective angle set.

9. The acoustic monitoring system based on multi-frequency measurement and grating lobe rejection according to claim 7, characterized in that: The effective angle verification and weighted splicing submodule includes: The subarray data segmentation submodule is used to divide the 32-channel sparse array into multiple subarrays and perform beamforming processing on each subarray separately; An adaptive weighting submodule, configured to calculate the signal-to-noise ratio (SNR) and sidelobe level of each subarray's output signal, and determine whether the detection angle and SNR of each subarray's output signal satisfy a valid angle verification strategy. The valid angle verification strategy is a physical constraint verification mechanism that uses angle rationality and a set SNR threshold as constraints. The strategy also adaptively weights the power corresponding to each verified subarray's detection angle based on the SNR and sidelobe suppression capability. The fusion output submodule is used to perform weighted splicing on the powers corresponding to the verified effective angles based on a weighted splicing algorithm to obtain a fusion directional pattern.

10. An acoustic monitoring method based on multi-frequency measurement and grating lobe rejection, applied to an acoustic monitoring system based on multi-frequency measurement and grating lobe rejection as claimed in any one of claims 1 to 9, characterized in that: The steps include: transmitting acoustic wave signals in sequence at multiple frequency points through a transmitting module, wherein the transmitting module comprises at least one broadband transducer; Receive the acoustic wave signal reflected or transmitted by the target through the receiving array module with an array field of view covering 0° to 180°. The receiving array module is a 32-channel broadband sparse receiving array; The signal processing module performs beamforming operations on each transmission frequency and outputs a directional pattern. The principle of spatial frequency periodicity is combined to obtain candidate grating lobe angles. Based on the grating lobe rejection algorithm, the grating lobes are marked through multi-frequency commonality analysis, and the grating lobe angles are deleted from the initial angle detection set. Based on the effective angle verification strategy and the weighted stitching algorithm, the powers corresponding to the detection angles verified by physical constraints are weightedly spliced ​​to obtain a fused directional pattern.

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