A method for enhancing underwater acoustic image super-resolution based on space-time two-dimensional deconvolution

Abstract

The present application relates to the technical field of underwater acoustic image processing, in particular to a kind of underwater acoustic image super-resolution enhancement method based on space-time two-dimensional deconvolution, the method is first to simulation signal and received signal do matching filtering, then by FFT conversion to frequency domain, in azimuth-frequency domain search generates two-dimensional CBF result, by iFFT obtains time delay-azimuth two-dimensional spectrum, utilizes R-L algorithm to calculate two-dimensional deconvolution and obtains high-resolution spectrum.This method can be combined with circular synthetic aperture technology for underwater target high-resolution imaging, and can also be applied to multi-beam inclined sonar imaging.Lake test and pool experiment verification can effectively improve time delay and azimuth resolution, suppress sidelobe, provide high-quality image information for underwater target detection and identification, and have important application value.

Description

Technical Field

[0001] This invention relates to the field of underwater acoustic image processing technology, specifically to an underwater acoustic image super-resolution enhancement method based on spatiotemporal two-dimensional deconvolution, which can be used to improve the resolution and clarity of underwater target imaging, providing more accurate image information for the detection, identification and analysis of underwater targets. Background Technology

[0002] In the field of active underwater acoustic target imaging, traditional matched filtering (MF) and conventional beamforming (CBF) methods have certain limitations. The temporal resolution of MF depends primarily on the signal bandwidth; when the bandwidth is limited, its temporal resolution is insufficient for high-precision underwater target detection. Furthermore, the spatial spectrum obtained by the CBF algorithm has a wide main lobe and high side lobes, which can lead to target blurring, affecting accurate target identification and localization, and failing to meet the high-resolution imaging requirements of modern underwater detection. Therefore, a new method is needed to improve the resolution and quality of underwater acoustic images. Summary of the Invention

[0003] This invention aims to provide a super-resolution enhancement method for underwater acoustic images based on spatiotemporal two-dimensional deconvolution. By overcoming the limitations of traditional imaging algorithms in terms of time delay and azimuth resolution, it effectively suppresses sidelobes and significantly improves the clarity and accuracy of underwater acoustic images, providing more reliable image data support for the detection, identification and analysis of underwater targets.

[0004] The technical solution adopted by this invention to solve its technical problem is: a super-resolution enhancement method for underwater acoustic images based on spatiotemporal two-dimensional deconvolution, comprising the following steps:

[0005] Matched filtering is performed on both the simulated signal and the received signal.

[0006] Perform a Fast Fourier Transform (FFT) operation on the matched filter result to convert it to the frequency domain;

[0007] A search is performed in the azimuth-frequency domain, and beamforming technology is used to generate two-dimensional conventional beamforming (CBF) results.

[0008] The power spectrum of a certain azimuth on a two-dimensional CBF is subjected to inverse fast Fourier transform (iFFT) to obtain the time delay-azimuth two-dimensional spectrum. The time delay-azimuth two-dimensional spectrum obtained by simulated signal processing is used as the point spread function (PSF), and the time delay-azimuth two-dimensional spectrum obtained by the actual received signal is used as the output energy distribution.

[0009] The two-dimensional deconvolution of the PSF and output energy distribution is calculated using the Richardson-Lucas (RL) algorithm to obtain a high-resolution time-delay-azimuth two-dimensional spectrum.

[0010] Specifically, when combining with circular synthetic aperture technology to achieve high-resolution imaging of underwater targets, the following steps are also included:

[0011] The echoes received in 360 directions in the circular synthetic aperture are equivalent to signals received by 360 elements, and the element spacing is calculated based on the rotation radius and rotation angle.

[0012] The received signal is divided into several subarrays, each subarray contains 20 azimuths, and adjacent subarrays overlap by 10 azimuths;

[0013] A two-dimensional PSF is constructed based on the transmitted signal parameters, array motion parameters, and environmental parameters.

[0014] For each subarray signal, a two-dimensional time-delay-azimuth CBF estimation is performed, and the two-dimensional CBF result and the two-dimensional PSF are deconvolved to obtain a high-resolution time-delay-azimuth spectrum.

[0015] Specifically, when applied to high-resolution imaging of multi-beam squint sonar, the following steps are also included:

[0016] Based on the operating frequency, number of primitives, and primitive spacing parameters of the multibeam squint sonar, a two-dimensional PSF is obtained by spatiotemporal two-dimensional deconvolution.

[0017] The signals received by the multibeam squint sonar are processed, and the time delay-azimuth spectrum estimation results are obtained by two-dimensional CBF and spatiotemporal two-dimensional deconvolution, respectively.

[0018] Specifically, the transmitted signal is a linear frequency modulated (LFM) signal, whose parameters include start frequency, cutoff frequency, pulse width, repetition period, start phase, and sampling rate.

[0019] Specifically, in the circumferential synthetic aperture technology, the sonar array rotates 360° around the target with a specific radius, transmitting a signal once every 1° of rotation and receiving echo signals.

[0020] Specifically, the multibeam squint sonar operates at a frequency of 1.2 MHz and contains 96 elements with a spacing of 1 mm between the elements.

[0021] Specifically, the RL algorithm iteratively solves the problem based on the two-dimensional convolution relationship of signal energy distribution in the deconvolution imaging principle when calculating two-dimensional deconvolution.

[0022] Specifically, by combining the spatiotemporal two-dimensional deconvolution imaging algorithm with the circular synthetic aperture technology, the pulse width can be compressed by about 40 times and the azimuth resolution can be improved by 5 times.

[0023] Specifically, when the spatiotemporal two-dimensional deconvolution imaging algorithm is applied to multibeam squint sonar imaging, it can simultaneously improve the resolution of time delay and azimuth, and suppress sidelobes.

[0024] The beneficial effects of this invention are:

[0025] The underwater acoustic image super-resolution enhancement method based on spatiotemporal two-dimensional deconvolution of the present invention has significant beneficial effects. By using spatiotemporal two-dimensional deconvolution processing, it effectively breaks through the resolution bottleneck of traditional imaging algorithms and greatly improves the quality of underwater acoustic images.

[0026] In applications combined with circular synthetic aperture technology, compared with traditional imaging methods, this invention can compress the pulse width by about 40 times and improve the azimuth resolution by 5 times, making the imaging of underwater targets clearer and more accurate. It can more accurately locate the position and outline of the target, providing stronger support for the detection and identification of underwater targets.

[0027] In multibeam squint sonar imaging applications, this method also performs exceptionally well, simultaneously improving both time delay and azimuth resolution while effectively suppressing sidelobes. This enables multibeam squint sonar to capture target information more clearly in complex underwater environments, reducing the impact of interference and noise, and enhancing the detection capability and identification accuracy of underwater targets.

[0028] In summary, the method of this invention provides clearer and more accurate image information for the detection, identification, and analysis of underwater targets, and has significant practical application value and broad market prospects. Attached Figure Description

[0029] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0030] Figure 1 This describes the spatiotemporal two-dimensional deconvolution imaging operation process.

[0031] This figure illustrates the complete processing steps of the method of the present invention, from input of simulated and received signals, through matched filtering, FFT operation, azimuth-frequency domain search to generate two-dimensional CBF results, to obtaining time delay-azimuth two-dimensional spectrum through iFFT, and finally using RL algorithm to perform two-dimensional deconvolution to obtain high-resolution time delay-azimuth two-dimensional spectrum. It clearly presents the operation steps and data flow of the method of the present invention.

[0032] Figure 2 A schematic diagram of the division of the circular composite aperture subarray;

[0033] This figure illustrates how, in a circular synthetic aperture (SAP) system, the echoes received from 360 azimuths are equivalent to signals received by 360 primitives, and how these signals are divided into several subarrays, each containing 20 azimuths with 10 overlapping azimuths between adjacent subarrays. This helps in understanding how data is processed and organized in SAP technology.

[0034] Figure 3 For spatiotemporal two-dimensional deconvolution of PSF;

[0035] This figure presents the two-dimensional PSF result constructed based on the transmitted signal parameters, array motion parameters, and environmental parameters. It intuitively demonstrates the shape and characteristics of the PSF, providing a basis for subsequent deconvolution calculation using the RL algorithm, and also reflects the imaging characteristics of the system.

[0036] Figure 4 A comparison of the time delay-azimuth spectrum estimation results of 2D-CBF and dCv2;

[0037] This figure compares the results of the traditional 2D-CBF method and the spatiotemporal two-dimensional deconvolution method (dCv2) of this invention in terms of time delay-azimuth spectrum estimation. It clearly shows the advantages of the method of this invention in improving resolution and suppressing sidelobes. The comparison can more intuitively show the improvement effect of the method of this invention on the quality of underwater acoustic images.

[0038] Figure 5 A comparison of the time delay and azimuth domain estimation results between 2D-CBF and dCv2;

[0039] This figure further compares and analyzes the estimation results of the 2D-CBF method and the dCv2 method from the two dimensions of time delay and orientation, and shows in more detail the improvement of resolution and the suppression of sidelobes of the method of the present invention in different dimensions, which helps to understand the performance characteristics of the method of the present invention in depth.

[0040] Figure 6 PSF for multibeam slant sonar pool experiment;

[0041] The figure shows the two-dimensional PSF obtained by spatiotemporal two-dimensional deconvolution in the multibeam angled sonar pool experiment scenario. It reflects the imaging characteristics of the system in this experimental scenario and provides key data for deconvolution calculation and image enhancement in this scenario.

[0042] Figure 7 Comparison of time delay-azimuth spectrum estimation results for 2D-CBF and dCv2 of squint sonar;

[0043] This figure compares the time delay-azimuth spectrum estimation results of the 2D-CBF method and the dCv2 method in squint sonar imaging, further demonstrating that the method of the present invention can effectively improve resolution and suppress sidelobes in multibeam squint sonar imaging, providing more reliable image information for underwater target detection in this application scenario. Detailed Implementation

[0044] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0045] like Figures 1-7 As shown, the present invention includes the following technical solutions:

[0046] Among them, the spatiotemporal two-dimensional deconvolution imaging principle

[0047] The most commonly used algorithm for active target imaging is to first estimate the target's time delay and azimuth parameters, then use the time delay and azimuth information to locate the target, and finally achieve target imaging. Among the algorithms for estimating target time delay and azimuth, matched filtering (MF) and conventional beamforming (CBF) are the most commonly used, but their resolution is usually limited by signal bandwidth and array aperture. To improve time delay and azimuth resolution, a two-dimensional deconvolution algorithm can be used, which can be represented as dCv2.

[0048] In an ideal, noise-free environment, when a signal x(t) is input to a time-invariant system h(t), the output y(t) can be expressed as: The asterisk (*) represents a one-dimensional convolution operation. If we describe the signal in terms of energy, then we have... Here I y =|y| 2 I 2 =|x| 2 I h =|h| 2 , while I h It's the point spread function (PSF). Using the Richardson-Lucas (RL) deconvolution algorithm, I can be obtained iteratively. x Furthermore, this solution is unique. The solution for the m-th iteration is...

[0049] For a CBF with an N-element uniform linear array, its beam response H(sin φ,f) at frequency f has a convolution relationship with the sound source distribution X(sin φ,f).

[0050] Specifically, it can be expressed as H(si nφ,f)=N sin c(2πNdf sinφ / c)

[0051] and The convolutional form.

[0052] Where d represents the element spacing, φ m and a m Let represent the direction of arrival and complex amplitude of the m-th signal, respectively, and δ(·) be the one-dimensional Dirac function.

[0053] When the sound sources are uncorrelated or coherent, the CBF beam output energy can be represented by the convolution of the beam pattern and the signal strength, i.e. in It is a PSF, where M uncorrelated signals in the beam domain can be represented as

[0054] In real-world active sensing scenarios, beamforming (MF) and beamwidth-convolution (CBF) are commonly used to estimate and display the sinφ-τ plane of the echo signal. However, the temporal resolution of MF is limited by the signal bandwidth, and the spatial spectrum obtained by the CBF algorithm suffers from a wide main lobe and high sidelobes. The deconvolution algorithm (dCv) can effectively improve beam resolution and suppress sidelobes. For each data segment (the i-th segment), the dCv beam distribution of all frequency beam outputs is I. i (sinφ)=∑ f S i (sinφ,f).

[0055] To simultaneously improve both temporal and spatial resolution, the outputs of MF and CBF are re-represented in the spatiotemporal domain, i.e., through specific two-dimensional convolution operations.

[0056] Represent it as I MF-CBF (sinφ,t)=|h MF-CBF (sinφ,t)**a m δ(sinφ-sinφ m ,t-τ m )| 2 This involves the two-dimensional Dirac function δ(·,·) and the two-dimensional convolution operation "**". Simultaneously, for the two-dimensional h... MF-CBF (sinφ,t) is explicitly defined as follows: Where e(t) is the transmitted signal waveform, H represents the complex transpose, and its frequency domain expression is: Here, E(f) represents the signal spectrum.

[0057] In summary, the output in the spatiotemporal domain is: I MF-CBF (sinφ,t)=Bp2(sinφ,t)**S2(sinφ,t), and the signal energy distribution obtained by solving the above equation using the RL algorithm is as follows: Among them, I MF-CBF (sinφ,t) represents the beam time series intensity, and Bp2(sinφ,t) is a two-dimensional PSF. The deconvolution result of S2(sinφ,t) is I. dCv2 (sinφ,t) is the high-resolution representation we expect.

[0058] Among them, the spatiotemporal two-dimensional deconvolution imaging operation process

[0059] The procedure for obtaining high-resolution time-delay-orientation results using a spatiotemporal two-dimensional deconvolution algorithm is attached. Figure 1 As shown, the simulated signal and the received signal follow the same processing steps throughout the entire imaging operation. The simulated signal received by each element of the sonar array is generated based on the parameters of the transmitted signal, and the reception delay is uniformly set to 0. First, matched filtering is performed on both the simulated signal and the actual received signal. Matched filtering enhances the matching degree between the signal and the target features, improving the accuracy of subsequent processing. Next, frequency domain analysis is performed on the result of matched filtering, that is, a Fast Fourier Transform (FFT) operation is performed to convert the time-domain signal to the frequency domain, so as to perform more in-depth analysis and processing in the frequency dimension.

[0060] Then, a search is performed in the azimuth-frequency domain, and beamforming technology is used to generate a two-dimensional conventional beamforming (CBF) result. The beamforming process can weight the received signal according to the array geometry and the direction of arrival of the signal, thereby enhancing the signal strength in a specific direction and achieving a preliminary estimate of the target's azimuth. Finally, an inverse fast Fourier transform (iFFT) is performed on the power spectrum of a certain azimuth in the two-dimensional CBF to convert the frequency domain information back to the time domain, thus obtaining the time-delay-azimuth two-dimensional spectrum. In this process, the time-delay-azimuth two-dimensional spectrum obtained through simulated signal processing serves as the PSF, reflecting the imaging characteristics of the system; while the time-delay-azimuth two-dimensional spectrum obtained from the actual received signal serves as the output energy distribution in the time-delay-azimuth domain. By using the RL algorithm to calculate the two-dimensional deconvolution of these two spectra, a high-resolution time-delay-azimuth two-dimensional spectrum can be obtained, providing accurate data support for subsequent target localization and imaging.

[0061] Among them, the application of spatiotemporal two-dimensional deconvolution imaging

[0062] The spatiotemporal two-dimensional deconvolution imaging algorithm of this invention has broad application prospects in many fields.

[0063] 1) Combined with circumferential synthetic aperture technology

[0064] Combined with circular synthetic aperture technology, high-resolution imaging of underwater targets can be achieved. During the lake trial, based on experimental requirements and underwater acoustic propagation characteristics, the transmitted signal was set to a linear frequency modulated (LFM) signal with a starting frequency of 70 kHz and a cutoff frequency of 100 kHz. This frequency range effectively covers the target detection frequency band and adapts to the underwater acoustic channel transmission characteristics. The pulse width was 4 ms, and the repetition period was 50 ms, optimized to balance signal energy and detection accuracy. The initial phase of 90° was set for easy signal synchronization and processing. The sampling rate of 2 MHz satisfies the signal sampling theorem, enabling accurate acquisition of echo signals. The sonar array rotates 360° around the target with a radius of 7.9920 m, transmitting a signal every 1° of rotation, and beamforming the echo signals to output a time-domain beam sequence. In this way, target echo signals from 360 azimuths, after spatial filtering, can be acquired.

[0065] In the processing of circular synthetic aperture radar (SAP) signals, the echoes received from 360 azimuths are equivalently considered as signals received by 360 primitives. The element spacing is accurately calculated based on the rotation radius and rotation angle. To facilitate subsequent processing, the signals received by the 360 ​​"primitives" are divided into several subarrays. In practice, 20 azimuths are typically divided into one subarray, with each subarray overlapping by 10 azimuths, as shown in the attached diagram. Figure 2 As shown. This subarray partitioning method can make full use of redundant information in the data while reducing computational complexity.

[0066] For each subarray, a two-dimensional spatiotemporal deconvolution algorithm is used to accurately calculate the time delay and azimuth of the target echo. First, a two-dimensional PSF is constructed based on the transmitted signal parameters, array motion parameters, and environmental parameters, as shown in the attached figure. Figure 3 As shown in the figure. The PSF result graph shows that it satisfies the time-invariant property, which provides the necessary condition for subsequent calculations using the RL algorithm. Then, time-delay-azimuth two-dimensional CBF estimation is performed on each subarray signal, and the two-dimensional CBF result and the two-dimensional PSF are used for deconvolution operation, finally successfully obtaining the high-resolution time-delay-azimuth spectrum. (See attached figure.) Figure 4 and attached Figure 5 As shown, by comparing with the traditional 2D-CBF method, it is clear that the two-dimensional deconvolution algorithm has significant advantages in resolution improvement and sidelobe suppression. It can compress the pulse width by about 40 times and improve the azimuth resolution by 5 times, which greatly improves the quality of underwater target imaging.

[0067] 2) Applied to multibeam squint sonar

[0068] Spatiotemporal two-dimensional deconvolution imaging algorithms can also be applied to high-resolution imaging of multibeam squint sonar. Taking a pool experiment as an example, the squint sonar operates at a frequency of 1.2 MHz, which is the optimized operating frequency for underwater target detection, achieving good resolution and detection range. The number of primitives is 96, determined according to the equipment design specifications; the primitive spacing is 1 mm, precisely measured and calibrated to ensure effective signal acquisition and processing by the sonar array. In this experimental scenario, a specific two-dimensional PSF is obtained using spatiotemporal two-dimensional deconvolution. Through processing the experimental data, time delay-azimuth spectrum estimation results are obtained using 2D-CBF and spatiotemporal two-dimensional deconvolution, as shown in the attached figure. Figure 6 As shown in the attached figure. The comparison results show that... Figure 7 As shown, in squint sonar imaging, the spatiotemporal two-dimensional deconvolution algorithm can also effectively improve the resolution of both time delay and azimuth at the same time, and can suppress sidelobes well, providing stronger technical support for the application of multibeam squint sonar in underwater target detection.

[0069] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of protection claimed by the present invention. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A super-resolution enhancement method for underwater acoustic images based on spatiotemporal two-dimensional deconvolution, characterized in that, Includes the following steps: Matched filtering is performed on both the simulated signal and the received signal. Perform a Fast Fourier Transform on the matched filter result to convert it to the frequency domain; A search is performed in the azimuth-frequency domain, and beamforming technology is used to generate two-dimensional conventional beamforming results. An inverse fast Fourier transform is performed on the power spectrum of a certain azimuth in a two-dimensional conventional beamforming to obtain a time-delay-azimuth two-dimensional spectrum. The time-delay-azimuth two-dimensional spectrum obtained by simulated signal processing is used as the point spread function, and the time-delay-azimuth two-dimensional spectrum obtained by the actual received signal is used as the output energy distribution. The point spread function and the two-dimensional deconvolution of the output energy distribution are calculated using the Richardson-Lucas algorithm to obtain a high-resolution time-delay-azimuth two-dimensional spectrum.

2. The underwater acoustic image super-resolution enhancement method based on spatiotemporal two-dimensional deconvolution according to claim 1, characterized in that: The spatiotemporal two-dimensional deconvolution method for underwater acoustic image super-resolution enhancement, when combined with circular synthetic aperture technology to achieve high-resolution imaging of underwater targets, also includes the following steps: The echoes received in 360 directions in the circular synthetic aperture are equivalent to signals received by 360 elements, and the element spacing is calculated based on the rotation radius and rotation angle. The received signal is divided into several subarrays, each subarray contains 20 azimuths, and adjacent subarrays overlap by 10 azimuths; A two-dimensional spectrum is constructed as a point spread function based on the transmitted signal parameters, array motion parameters, and environmental parameters. For each subarray signal, time-delay-azimuth two-dimensional conventional beamforming estimation is performed, and the two-dimensional conventional beamforming result and the two-dimensional spectrum are used as point spread functions for deconvolution operation to obtain a high-resolution time-delay-azimuth spectrum.

3. The underwater acoustic image super-resolution enhancement method based on spatiotemporal two-dimensional deconvolution according to claim 1, characterized in that: When applied to high-resolution imaging of multi-beam squint sonar, the following steps are also included: Based on the operating frequency, number of elements, and element spacing parameters of the multibeam squint sonar, a two-dimensional spectrum is obtained by spatiotemporal two-dimensional deconvolution as a point spread function. The signals received by the multibeam squint sonar are processed, and the time delay-azimuth spectrum estimation results are obtained by two-dimensional conventional beamforming and spatiotemporal two-dimensional deconvolution, respectively.

4. The underwater acoustic image super-resolution enhancement method based on spatiotemporal two-dimensional deconvolution according to claim 2, characterized in that: The transmitted signal is a linear frequency modulated signal, and its parameters include start frequency, cutoff frequency, pulse width, repetition period, start phase, and sampling rate.

5. The underwater acoustic image super-resolution enhancement method based on spatiotemporal two-dimensional deconvolution according to claim 2, characterized in that: In the circumferential synthetic aperture technology, the sonar array rotates 360° around the target with a specific radius, emitting a signal once every 1° of rotation and receiving the echo signal.

6. The underwater acoustic image super-resolution enhancement method based on spatiotemporal two-dimensional deconvolution according to claim 3, characterized in that: The multibeam squint sonar operates at a frequency of 1.2 MHz and contains 96 elements with a spacing of 1 mm between elements.

7. The underwater acoustic image super-resolution enhancement method based on spatiotemporal two-dimensional deconvolution according to claim 1, characterized in that: The Richardson-Lucas algorithm iteratively solves the two-dimensional deconvolution problem based on the two-dimensional convolution relationship of signal energy distribution in the deconvolution imaging principle.

8. The underwater acoustic image super-resolution enhancement method based on spatiotemporal two-dimensional deconvolution according to claim 2, characterized in that: By combining a spatiotemporal two-dimensional deconvolution underwater acoustic image super-resolution enhancement method with circular synthetic aperture technology, the pulse width can be compressed by 40 times and the azimuth resolution can be improved by 5 times.

9. The underwater acoustic image super-resolution enhancement method based on spatiotemporal two-dimensional deconvolution according to claim 3, characterized in that: The spatiotemporal two-dimensional deconvolution underwater acoustic image super-resolution enhancement method, when applied to multibeam squint sonar imaging, can simultaneously improve the resolution of temporal delay and azimuth, and suppress sidelobes.

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