An imaging algorithm for multiple receiver synthetic aperture sonar

Motion compensation, referred to as micronavigation, is an unavoidable issue to obtain a high quality image for multiple receiver synthetic aperture sonar (SAS). A feasible micronavigation method based on the inertial navigation system (INS) data is presented in this paper. The INS data is obtained from some high-precision inertial navigation equipment, thus the accuracy of motion errors can be guaranteed. Using the back projection algorithm, some imaging results of experiment data are shown in the end of paper. Meanwhile, some imaging results focused by the conventional chirp scaling algorithm are also given, and the compared results show the effectiveness of the presented method.

The rich content of synthetic aperture sonar (SAS) data is typically used to generate imagery with resolution as high as theoretically possible. But when the ultimate purpose of the imagery is for detecting objects with sizes large compared to the resolution cell, exploiting the raw data in alternative ways can be more useful. We first show how multiple lower-resolution SAS images (i.e., “looks”) can be obtained in an efficient, principled manner by band-limiting the image wavenumber spectrum of a full-resolution SAS image. By combining these multiple looks (from different aspect and frequency bands), a despeckled image that enjoys greater (target) signal to (seabed) reverberation ratio can be produced. On a large set of real SAS data collected at sea, it is demonstrated how better target detection performance of underwater mines can be achieved by using the lower-resolution despeckled imagery rather than single-look, maximum-resolution imagery.

Synthetic Aperture Sonar (SAS) achieves high-resolution imaging of underwater targets by moving a small-aperture array and coherently processing the signals, thereby generating a virtual large-aperture imaging effect. The conventional Range-Doppler (RD) algorithm offers advantages such as high computational efficiency, simple processing flow, and ease of engineering implementation. However, due to the limitations of its underlying assumptions and its heavy reliance on interpolation for range walk correction, the RD algorithm cannot achieve both imaging efficiency and accuracy. To address this issue, this paper proposes an improved RD algorithm based on a sparse Bayesian learning two-dimensional deconvolution (SBL-dCv2) framework. The mathematical model of the algorithm is derived, and comparative simulation experiments with the traditional RD algorithm are conducted for multi-point targets. The simulation results show that the SBL-dCv2 improved RD algorithm delivers both high resolution and computational efficiency, offers stronger resolution performance for multi-point targets, and is better suited for practical engineering applications of SAS.

This paper proposes a scheme for the realization of multi-beam synthetic aperture sonar (SAS) based on the multi-beam bathymetry and aperture synthetic principle, designs a new multiple receiver array structure, deduces correlative math model, and simulates the dot-by-dot imaging algorithm according to the novel array structure. Computer simulation results have tested the correctness of theory analysis and the efficiency of algorithm, the results show that, compared with multi-beam bathymetry sonar, the new scheme can remarkably improve the along-track resolution, and compared with conventional SAS, the new scheme can get more information of the target, thereby enhance the imaging quality.

The use of Multiple Input Multiple Output (MIMO) orthogonally coded waveforms sharing the same bandwidth, has the potential to increase area coverage rate in synthetic aperture sonar (SAS). The disadvantage is cross‐talk between the waveforms. In this letter, the benefits and limitations are listed for a best case use of MIMO in SAS. A scheme is developed to emulate a MIMO system with realistic cross‐talk that can be tested on non‐MIMO SAS data. The impact of cross‐talk is evaluated on simulated data and real data collected by a HISAS interfereometric SAS carried by a HUGIN autonomous underwater vehicle. The finding is that the negative impact of cross‐talk on SAS is significant.

Synthetic Aperture Sonar (SAS) coherently processes the acoustic data acquired along a linear trajectory. The imaging process is in essence an inverse problem where the reflectivity of the seafloor has to be estimated. Several imaging algorithms have been proposed over the years including back-projection algorithms. One commonly assumed hypothesis, however, is that the antenna is motionless during transmission and reception. This hypothesis is known as the start-stop assumption. This paper questions the validity of this assumption, and proposes a full derivation of the SAS processing taking into account the vehicle motion by using the Lorentz transformation. The cell migration for the SAS system is computed and the validity limit of the stop-start assumption depending on the SAS system parameters is derived. Numerical examples of start-stop assumption violations are given and the Doppler cell migration correction on real SAS data are presented and discussed.

Synthetic aperture sonar (SAS) provides high-resolution acoustic imaging by processing coherently the backscattered acoustic signal recorded over consecutive pings. Traditionally, object detection and classification tasks rely on high-resolution seafloor mapping achieved with widebeam, broadband SAS systems. However, aspect- or frequency-specific information is crucial for improving the performance of automatic target recognition algorithms. For example, low frequencies can be partly transmitted through objects or penetrate the seafloor providing information about internal structure and buried objects, while multiple views provide information about the object's shape and dimensions. Sub-band and limited-view processing, though, degrades the SAS resolution. In this paper, SAS imaging is formulated as an ℓ1-norm regularized least-squares optimization problem which improves the resolution by promoting a parsimonious representation of the data. The optimization problem is solved in a distributed and computationally efficient way with an algorithm based on the alternating direction method of multipliers. The resulting SAS image is the consensus outcome of collaborative filtering of the data from each ping. The potential of the proposed method for high-resolution, narrowband, and limited-aspect SAS imaging is demonstrated with simulated and experimental data.

Transfer function estimation of turboalternator from practical operating data by spectral analysis

Synthetic aperture sonar (SAS) imagery can generate high resolution images of the seafloor. Thus, segmentation algorithms can be used to partition the images into different seafloor environments. In this paper, we compare two possibilistic segmentation approaches. Possibilistic approaches allow for the ability to detect novel or outlier environments as well as well known classes. The Possibilistic Fuzzy Local Information C-Means (PFLICM) algorithm has been previously applied to segment SAS imagery. Additionally, the Possibilistic K-Nearest Neighbors (PKNN) algorithm has been used in other domains such as landmine detection and hyperspectral imagery. In this paper, we compare the segmentation performance of a semi-supervised approach using PFLICM and a supervised method using Possibilistic K-NN. We include final segmentation results on multiple SAS images and a quantitative assessment of each algorithm.

Synthetic aperture sonar (SAS) is a technique that acquires an underwater image by synthesizing the signal received by the sonar as it moves. By forming a synthetic aperture, the sonar overcomes physical limitations and shows superior resolution when compared with use of a side-scan sonar, which is another technique for obtaining underwater images. Conventional SAS algorithms require a high concentration of sampling in the time and space domains according to Nyquist theory. Because conventional SAS algorithms go through matched filtering, side lobes are generated, resulting in deterioration of imaging performance. To overcome the shortcomings of conventional SAS algorithms, such as the low imaging performance and the requirement for high-level sampling, this paper proposes SAS algorithms applying compressive sensing (CS). SAS imaging algorithms applying CS were formulated for a single sensor and uniform line array and were verified through simulation and experimental data. The simulation showed better resolution than the ω-k algorithms, one of the representative conventional SAS algorithms, with minimal performance degradation by side lobes. The experimental data confirmed that the proposed method is superior and robust with respect to sensor loss.

Synthetic aperture radar (SAR) systems, as wideband radar systems, are inherently susceptible to interference signals within their operational frequency band, which significantly affects SAR signal processing and image interpretation. Recent studies have demonstrated that semiparametric methods (e.g., the RPCA method) exhibit excellent performance in suppressing these interference signals. However, these methods predominantly focus on processing SAR’s raw echo data, which does not satisfy the sparsity requirements and entails extremely high computational complexity, complicating integration with imaging algorithms. This paper introduces an effective method for suppressing interference signals by leveraging the sparsity of the SAR image domain. It utilizes the sparsity of the interference signal in the two-dimensional frequency domain, following focusing processing, rather than relying on low-rank properties. This approach significantly reduces the computational complexity. Ultimately, the effectiveness and efficiency of the proposed algorithm are validated through experiments conducted with simulated and real SAR data.

In this paper, we describe a space-time analysis for synthetic aperture sonar (SAS) employing Green's functions, and show that it's basically an inverse problem. Iterative focusing is proposed based on this viewpoint. In order to mitigate difficulties of slow convergence and computational costs inherent in iterative methods, an acceleration technique is used. Applications to SAS imaging is demonstrated through reconstructions from simulated data, with emphasis on high resolution and image quality.

Current sonar autofocus techniques for blur removal originate in the radar community but have not provided a complete solution for Synthetic Aperture Sonar (SAS) imagery. The wide-beam, wide-band nature of SAS imagery makes implementation of Synthetic Aperture Radar (SAR) autofocus techniques difficult. This paper describes a generalisation of the standard Phase Gradient Autofocus (PGA) algorithm used in spotlight SAR that allows operation with stripmap SAS geometries. PGA uses prominent points within the target scene to estimate image blurring and phase errors. We show how PGA can be generalised to work with wide-band, wide-beam stripmap geometries. The SPGA method works by employing wave number domain 2D phase estimation techniques. The 2D phase errors are related to aperture position errors using the wave number transform. Robust sway estimates are obtained by using redundancy over a number of target points. We also present an improved Phase Curvature Autofocus (PCA) algorithm using the wave number transform. Preliminary results from the two algorithms (both on field-collected and simulated data sets) are presented and related to those obtained using previous methods. A discussion of SPGA's benefits over traditional algorithms and the limitations of the SPGA algorithm is presented. The SPGA algorithm was found to perform better than 2-D PCA on both simulated and field-collected data sets. Further testing on a variety of target scenes and imagery is required to investigate avenues of autofocus improvement.

This paper aims at iterative learning control (ILC) design and analysis for a class of linear distributed parameter systems (DPSs) that may be hyperbolic, parabolic, or elliptic, and include many important physical processes such as diffusion, vibration, heat conduction and wave propagation as special cases. Owing to the linear characteristic of systems, the system equations are first cast into a matrix form in the Laplace transform domain. Then, through determination of a fundamental matrix, the system transfer function is precisely evaluated in a closed form. The derived transfer function clearly demonstrates the input-output relationship of system, and thus facilitates the consequent ILC design and convergence analysis in the frequency domain. The proposed control design scheme is able to deal with parametric and non-parametric uncertainties and makes full use of the process repetition, while avoids any simplification or discretization for the 3D dynamics of distributed parameter systems in the time, space, and iteration domains. In the end, an illustrative example is presented to demonstrate the efficacy of the proposed ILC scheme.

A comparison is made of the signal-to-noise characteristics of synthetic-aperture sonar and conventional sonar. For the synthetic-aperture sonar case a design procedure is described which makes possible the choice of essentially any unambiguous range and any resolution. Often multiple beam receivers are required. It is shown for the synthetic-aperture sonar case that the horizontal aperture is the major factor affecting area coverage rate. The comparisons are made with as many parameter values as spossible identical for both the synthetic-aperture cases and the nonsynthetic-aperture cases. If identical physical aperture dimensions are used and other parameters held to identical values, the synthetic-aperture sonar gives higher signal-to-ratio than does the nonsynthetic-aperture sonar primarily because the latter must operate at higher acoustic frequencies for which signal attenuation becomes extremely large. If the sonar systems are operated at the same frequency, then the nonsynthetic aperture sonar can give a higher signal-to-noise ratio only when its physical aperture is larger than that used in the synthetic-aperture case. Subject Classification: 30.82; 60.20.