Multiple-input Multiple-output Sonar Research Articles

A High-Resolution Imaging Method for Multiple-Input Multiple-Output Sonar Based on Deterministic Compressed Sensing.

Differences between conventional sonar and Multiple-Input Multiple-Output (MIMO) sonar systems arise in achieving high angular and range resolution. MIMO sonar uses Matched Filtering (MF) with well-correlated transmitted signals to enhance spatial resolution by obtaining virtual arrays. However, imperfect correlation characteristics yield high sidelobe values, which hinder accurate target localization in underwater imagery. To address this, a Compressed Sensing (CS) method is proposed by reconstructing echo signals to suppress correlation noise between orthogonal waveforms. A shifted dictionary matrix and a deterministic Discrete Fourier Transform (DFT) measurement matrix are used to multiply received echo signals to yield compressed measurements. A sparse recovery algorithm is applied to optimize signal reconstruction before joint transmit-receive beamforming forms a 2D sonar image in the angle-range domain. Numerical simulations and lake experimental results confirm the effectiveness of the proposed method, by obtaining a lower sidelobe sonar image under sub-Nyquist sampling rates as compared with other approaches.

High‐angular‐ and range‐resolution imaging using MIMO sonar with two‐dimensional deconvolution

AbstractA real‐aperture high‐resolution imaging method utilising a multiple‐input multiple‐output (MIMO) sonar with two‐dimensional (2‐D) deconvolution processing is proposed. Via derivation, the authors show that the conventional beamformer (CBF) output of the MIMO sonar can be approximately expressed as the 2‐D convolution between the original angular and range distributions of scatterers and a 2‐D point spread function (PSF). Hence, to improve the imaging performance degraded by the convolution effect, the 2‐D Richardson‐Lucy (R‐L) algorithm is used to deconvolve the CBF output of the MIMO sonar. Specifically, the authors give the iteration formula of the 2‐D R‐L algorithm attached to the MIMO sonar, and take the absolute values of the CBF outputs of the virtual array as the deconvolution inputs. The analytical expression of the 2‐D PSF is also given, which is designed as the angular‐ and range‐domain (amplitude) responses of a far‐field ideal scatterer located in the normal direction of the virtual array. Meanwhile, the authors point out that the mismatch made by the approximation may degrade the imaging performance, and suggest that a small number of iterations in the 2‐D R‐L algorithm can be used to alleviate the mismatch problem. Via numerical simulations and a tank experiment, the authors show that the proposed method can simultaneously increase the angular and range resolutions and suppress the sidelobes, when compared to the existing MIMO sonar imaging method.

Multiple‐input multiple‐output sonar adaptive beamforming using transmission diversity smoothing and backward processing

AbstractBenefit from the transmission diversity smoothing (TDS) effect upon coherent targets decorrelation, the kind of adaptive beamformers can be directly applied for multiple‐input multiple‐output (MIMO) sonar applications. Increasing the number of transmitted signals helps improve the TDS‐based decorrelation performance. However, a large number of transmitters is not feasible for those sonar systems mounted on small platforms with limited space. To address the lack of TDS‐based degrees of freedom for smoothing, an improved TDS decorrelation method is proposed for MIMO sonar in this study. With the conjugated MIMO data covariance matrix being left‐multiplied and right‐multiplied, respectively, by the exchange matrix, the authors obtain a backward‐smoothed data covariance matrix. Afterwards, the authors average it with the original MIMO receiving data covariance matrix to reconstruct a new data covariance matrix. Compared with TDS, the improved TDS strengthens the decorrelation performance by increasing the number of covariance matrix for smoothing and thus enables the new rank restored signal covariance matrix to have better non‐singularity. Without increasing any aperture loss and system complexity, a lower correlation between echoes is obtained, and the performance of the minimum variance distortionless response beamformer is hence improved in the presence of coherent targets. The effectiveness of the proposed decorrelation method is verified by numerical simulations and real data from a water tank experiment.

Using deconvolution to suppress range sidelobes for MIMO sonar imaging

A multiple- input multiple-output (MIMO) sonar can produce a large-aperture virtual array with the use of a small size array and a limited number of elements, which is very useful when applied to underwater acoustic imaging. However, the range sidelobes (SLs) in the MIMO sonar imaging result is high due to the non-ideal orthogonality of transmitting waveforms. In this paper, we propose a deconvolution method to suppress the range SLs. We consider that the range SLs in the imaging result are composed of cross-correlation functions (CCFs) and SLs of auto-correlation functions (ACFs) of transmitting waveforms. And they can be treated as the “noise” term in the output of the convolution between the original distributions of scatterers and a Dirac-like point spread function (PSF). Accordingly, we use the deconvolution method (i.e., the Richardson-Lucy algorithm) to suppress these range SLs. We give the basic processing procedure of the proposed deconvolution method. Simultaneously, we give a simple searching method to find the best PSF corresponding to the lowest range SL level after deconvolution. Numerical simulations and a tank experiment show the proposed deconvolution method can suppress the range SLs effectively.

High-resolution two-dimensional imaging using MIMO sonar with limited physical size

For an imaging sonar mounted onto a small sonar carrier (e.g., unmanned underwater vehicles), the angular resolution is strictly restricted by the limited physical size. To solve the problem, we propose a high-resolution imaging approach utilizing a multiple-input multiple-output (MIMO) sonar. The MIMO sonar is composed of two transmitting transducers and a receiving uniform linear array (ULA). The two transmitting transducers and the receiving ULA are located on two neighboring parallel straight lines, respectively. And the spacing between the two transmitting transducers is equal to the product of the hydrophone number and the inter-hydrophone spacing of the receiving ULA. Simultaneously, a pair of up- and down-chirp linear frequency modulation (LFM) pulses with the same frequency band (producing the same auto-correlation function for multibeam processing) and a large time-bandwidth product (ensuring low cross-correlation functions for range sidelobe suppression) are employed as transmitting waveforms. Based on the designed array layout and the transmitting waveforms, an improved hybrid beamformer combining the subarray processing, the phase shift and the shifted sideband beamformers together, is given, which ensures a fine imaging resolution and a low computation load. Via theoretical analysis, numerical simulations and a tank experiment, we show that the angular resolution of the proposed MIMO sonar imaging method doubles that of the traditional single-input multiple-output (SIMO) imaging method using the same array physical size and the same working frequency band.

Improved high‐precision approach based on deconvolution in MIMO sonar imagery

Multiple-input multiple-output (MIMO) sonar enables high-resolution imaging, which is extremely useful in underwater acoustic high-precision imagery. However, the first peak sidelobe levels and beamwidth performance degrade significantly when conventional tapering is applied due to the non-uniformity of the spanned virtual linear array, which prevents MIMO sonar from obtaining high-precision images with high resolution and high definition. Therefore, an improved high-precision approach in MIMO sonar imagery is proposed based on deconvolution to realise sidelobe suppression and to narrow the mainlobe width. The relationship between the target's beam power function and the pattern of the actual transmitting–receiving arrays is derived first, and design criteria are presented for MIMO sonar arrays to enhance the resolution. Subsequently, according to the orthogonality of the transmitting signals, the echo is separated by matched filtering, and then joint beamforming is employed to attain the beam data. Finally, the focused images are processed by deconvolution to enhance the clarity of the image and simultaneously obtain higher resolution. Numerical simulations and lake experiments are presented to demonstrate that high-precision images in MIMO sonar are achieved utilising the proposed approach, which also involves a lower computation load.

MIMO Sonar DOA Estimation with Joint Matched-Filtering Based on Transmission Diversity Smoothing

There is a class of methods based on transmission diversity smoothing by multiple-input multiple-output(MIMO) sonar called MIMO-TDS which is considered as one of the most effective methods for estimation of direction-of-arrival(DOA) using MIMO sonar systems. MIMO-TDS produced by orthogonal signal transmission for active sonar can be immediately implemented with high resolution algorithms such as MVDR to estimate the direction of received signals. However, the orthogonal transmission mode of MIMO-TDS is doomed to a loss of transmission array gain indirectly leading to the problem that the echoes are not equipped with as high SNR as enough for an accurate target localization, especially in scenarios in which the targets are far away from array. In order to solving the "low SNR" problem, a solution using all transmitted signals simultaneously to design a joint matched-filter intended for received signal is proposed to improve the performance of MIMO-TDS, which is inspired by the match-filtering concept of "MIMO sonar virtual array method" simplified as MIMO-VA. And accordingly, the unit impulse response function of proposed joint matched-filter is the equivalent of linear sum of all orthogonal transmitted signals and the modified MIMO-TDS is named as "joint matched-filtering MIMO sonar transmission diversity smoothing DOA estimation method", which could be simplified as MIMO-TDS-MF. The characteristic of proposed method is analyzed theoretically and compared to MIMO-TDS and MIMO-VA in this paper:Compared with MIMO-TDS, the proposed method not only retains the advantage of transmission diversity smoothing but also improves the SNR by joint match-filtering; What's more, compared with MIMO-VA, MIMO-TDS-MF is equipped with substantially less computation than the former due to an employment of much fewer matched-filters and is in possession of a superior robustness to that of MIMO-VA. Numerical experiments demonstrate the efficiency of proposed MIMO-TDS-MF.

Noise Suppression for Direction of Arrival Estimation in Co-located MIMO Sonar.

Noise suppression capacity in multiple-input multiple-output (MIMO) sonar signal processing is derived under the assumption of white Gaussian noise. However, underwater noise mainly includes white Gaussian noise and colored noise. There exists a certain correlation between the noise signals received by each MIMO sonar array element. The performance of traditional direction-of-arrival (DOA) estimation methods decreases obviously in complex marine noise. In this paper, we propose a marine environment noise suppression method for MIMO applied to multiple targets’ DOA estimation. The noise field can be decomposed into a symmetric noise component and an asymmetric noise component. We use the covariance matrix imaginary component to pre-estimate the signal sources, then use the dimension reduction transformation to reconstruct the real component of the covariance matrix. The Toeplitz technique is utilized to reduce the correlation of the reconstructed covariance matrix. Thus, the subspace decomposition-based techniques such as multiple signal classification (MUSIC) can be used for multiple targets’ DOA estimation. To reduce the computational complexity of the methods, search-free direction-finding techniques such as the estimation of signal parameters via rotational invariance techniques (ESPRIT) can be utilized. As a result, the proposed methods can achieve better direction-finding performance in the condition of limited snapshots with lower computational cost. The corresponding Cramer-Rao bound (CRB) is deduced and the signal-to-noise ratio (SNR) gain obtained by dimension reduction processing is discussed. Simulation results also show the superiority of the proposed method over the existing methods.

Hybrid phase shift and shifted sideband beamforming for large‐aperture MIMO sonar imaging

A multiple-input multiple-output (MIMO) sonar can form a large-aperture virtual uniform linear array (ULA) from a small number of physical elements. However, the large-aperture MIMO sonar encounters a dilemma between the computation load and the imaging performance. Since the traditional phase-shift beamformer (PSBF) and shifted sideband beamformer (SSBF) cannot solve the dilemma individually, the authors proposed a hybrid beamformer (HBF) which combines PSBF and SSBF together. Specifically, the proposed HBF contains the off- and on-line processing. In the off-line processing SSBF is applied to the transmitting ULA to process transmitting waveforms. In the on-line processing PSBF is applied to the receiving ULA, and outputs of off-line SSBFs are utilised to matched filter outputs of PSBFs. Thus, in the proposed HBF the time delays of SSBF is calculated off-line; and the multi-beam processing is simplified as PSBFs on the receiving ULA. Consequently, the proposed HBF can reduce the computation load of the large-aperture MIMO sonar effectively while obtaining a fine imaging performance. Numerical simulations show the effectiveness of the proposed HBF.

Spatially Distributed MIMO Sonar Systems: Principles and Capabilities

Multiple-input–multiple-output (MIMO) sonar systems offer new perspectives for target detection and area surveillance. This paper introduces a unified formulation for sonar MIMO systems and focuses on the target detection and recognition capability of these systems. The multiplication of the number of transmitters and receivers not only provides a greater variety in terms of target view angles but also provides meaningful statistics on the target itself. Assuming that views are independent and the MIMO system is large enough, we demonstrate that target recognition is possible with only one MIMO snapshot. By studying the detection performance of MIMO sonars we also demonstrate that such systems solve the speckle noise and decorrelate individual scatterers inside one cell resolution, leading to super-resolution imaging. We also show that, if carefully designed, MIMO systems can surpass the resolution of a synthetic aperture sonar (SAS) system using the same bandwidth. All the discussed properties are derived from the independent view assumption. Fulfilling this assumption drives the design and efficiency of such systems.

Tracking with MIMO sonar systems: applications to harbour surveillance

Multiple-input multiple-output (MIMO) sonar systems offer new perspectives for area surveillance especially in complex environments where strong multipath and dense clutter can become very challenging. This study proposes a MIMO sonar system based scheme to tackle the difficult problem of harbour surveillance. An emphasis is put on recognition and tracking on low-profile mid-water targets. First, a MIMO simulator which can compute synthetic raw data for any transmitter/ receiver pair in a multipath, cluttered and dynamic environment is presented. The study then proposes two radically different methods for the underwater target tracking problem in complex environment: a digital tracker and an analogue tracker. On the digital side, an implementation of the recently developed hypothesised filter for independent stochastic populations is presented. This filter enables robust multi-object tracking as well as track classification capabilities without the use of heuristics. An analogue filter based on acoustical time reversal techniques is also introduced. This filter directly uses the returned acoustic field from the scene to focus the sound on the expected target position, hence improving the signal-to-noise ratio on the target in complex environments and taking full advantage of the MIMO architecture.

Low complexity MIMO sonar imaging using a virtual sparse linear array

A multiple-input multiple-output (MIMO) sonar can synthesize a large-aperture virtual uniform linear array (ULA) from a small number of physical elements. However, the large aperture is obtained at the cost of a great number of matched filters with much heavy computation load. To reduce the computation load, a MIMO sonar imaging method using a virtual sparse linear array (SLA) is proposed, which contains the offline and online processing. In the offline processing, the virtual ULA of the MIMO sonar is thinned to a virtual SLA by the simulated annealing algorithm, and matched filters corresponding to inactive virtual elements are removed. In the online processing, outputs of matched filters corresponding to active elements are collected for further multibeam processing and hence, the number of matched filters in the echo processing procedure is effectively reduced. Numerical simulations show that the proposed method can reduce the computation load effectively while obtaining a similar imaging performance as the traditional method.

Fast DOA estimation algorithm for MIMO sonar based on ant colony optimization

The maximum likelihood (ML) estimator demonstrates remarkable performance in direction of arrival (DOA) estimation for the multiple input multiple output (MIMO) sonar. However, this advantage comes with prohibitive computational complexity. In order to solve this problem, an ant colony optimization (ACO) is incorporated into the MIMO ML DOA estimator. Based on the ACO, a novel MIMO ML DOA estimator named the MIMO ACO ML (ML DOA estimator based on ACO for MIMO sonar) with even lower computational complexity is proposed. By extending the pheromone remaining process to the pheromone Gaussian kernel probability distribution function in the continuous space, the proposed algorithm achieves the global optimum value of the MIMO ML DOA estimator. Simulations and experimental results show that the computational cost of MIMO ACO ML is only 1/6 of the MIMO ML algorithm, while maintaining similar performance with the MIMO ML method.

Compensating for intensity loss in a large-aperture MIMO sonar imaging system

To reduce the computation burden of a large-aperture multiple-input multiple-output(MIMO) sonar imaging system,the phase-shift beamformer(PSBF) is used at the cost of bringing the intensity loss(IL).The cause of the IL is analyzed in detail and a variable termed as IL factor is defined to quantify the loss amount.To compensate for the IL,two methods termed as intensity compensation for the PSBF(IC-PSBF) and the hybrid beamforming(HBF),respectively,are proposed.The IC-PSBF uses previously estimated IL factors to compensate for output intensities of all PSBFs;and the HBF applies the IC-PSBF to the center beam region and the shifted-sideband beamformer(SSBF) to the side beam region,respectively.Numerical simulations demonstrate the effectiveness of the two proposed methods.