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Large uniform rectangular arrays (URAs) are widely used in 3-D underwater imaging systems to obtain high-quality images; however, the gain and phase errors of these arrays can distort the results. To restore the image quality, many calibration methods have been proposed, most of which adopt a far-field model. This model requires the calibrator source to be far away, which reduces the signal-to-noise ratio and adversely affects the calibration accuracy. An effective means of alleviating this contradiction is to adopt the near-field signal model. However, the source location estimation in the near-field model typically uses spectral peak searches, which is a significant computational burden, particularly for large URAs. A fast and efficient near-field model self-calibration method for large URAs is proposed to obtain improved calibration results. First, a convenient preprocessing and two extended three-step iterative algorithms are used instead of spectral peak searches to estimate the source location efficiently. The array is then calibrated using a spatial matched filter and maximum a posteriori approach. Numerical results proved that the proposed method almost reaches the Cramér–Rao bound. Compared with existing near-field model calibration methods using spectral peak searches, the computational complexity of the proposed method is reduced by more than three orders of magnitude when the array size reaches 60 $\times $ 60 or more. By virtue of the near-field model, the phase calibration accuracy for large URAs at a close range is more than 10 times that of far-field model calibration methods. [ABSTRACT FROM AUTHOR]

An adaptive beamformer is effective at suppressing interference and noise. However, when the desired signal component is included in the covariance matrix, the beamformer performance becomes seriously degraded. Moreover, while the linear array has been actively researched, few studies have focused on the planar array. In this paper, an adaptive beamformer with more accurate reconstruction of the covariance matrix for a planar array is therefore proposed. The reconstruction is based on the Bayesian compressive sensing (BCS) theory. First, the directions of arrival (DOA) estimation of interferences are conducted. This problem is transformed into that of finding the minimum number of DOAs with a nonzero input because the array output is known. Accordingly, it can be converted into a probabilistic framework using the BCS technique. Then, the interference plus noise covariance matrix is reconstructed by using the DOAs of the interferences and the Capon spatial spectrum estimator. The reconstruction matrix is more accurate than other methods that directly use a data sampling matrix. Further constraints are then added to control the side-lobe level of the beam pattern of the proposed beamformer. Our numerical results confirm the effectiveness of the proposed method in terms of interference suppression, robustness to mismatch errors, and effective side-lobe-level control. [ABSTRACT FROM AUTHOR]

The development of a real-time 3-D underwater imaging system with a planar array is restricted by the huge hardware cost and computational burden associated with the large number of elements required. In this paper, a new design of low-complexity 3-D underwater imaging system is proposed, involving two key parts: a special sparse array with low hardware cost and an efficient beamforming with low computational burden. Especially, the simulated annealing algorithm is optimized based on distributed and parallel subarray (DPS) beamforming and results in fewer active elements than existing methods. To improve the computational efficiency especially for sparse arrays, an optimized DPS beamforming is presented with the pruning fast Fourier transform technology. To test the validity of the design, a real system based on the proposed methods was devised and employed in the lake and sea trials. The experiment results demonstrate that the low-complexity 3-D underwater imaging system can provide a satisfactory imaging quality. [ABSTRACT FROM AUTHOR]

The development of real-time 3-D underwater imaging is restricted by the huge hardware cost and the computational burden associated with a large number of transducers. In this paper, a low-complexity real-time 3-D sonar imaging system with a cross array is proposed. The low complexity, in both hardware cost and computational load, is achieved by an innovative method of signal processing in the far field and the advantages of the cross array in minimal transducer numbers. The method consists of two parts: a multifrequency (MF) algorithm for the transmitting process and a parallel subarray (PS) algorithm for the receiving beamforming. The MF algorithm solves the real-time problem of the cross array by reducing the scanning time that is proportional to the number of transmissions. The PS algorithm improves the computational efficiency using a two-stage parallel and pipeline framework. The PS algorithm is an approximate method, and its precision is analyzed in this paper. The computational efficiency of the proposed method is compared with the direct method (DM) beamforming in both cross and planar arrays. A prototype, based on the innovative method, was designed and tested in pool and lake trials. The results demonstrate that the low-complexity system can satisfy the real-time requirement of 3-D underwater imaging applications with an acceptable imaging quality. [ABSTRACT FROM PUBLISHER]

Real-time 3-D acoustical imaging technique is a key advance to broaden the scope and enhance the feasibility of underwater missions. In this paper, a real-time 3-D underwater acoustical imaging system to handle the actual tasks under a narrowband excitation is presented. The system consists of three parts: a transmitter, a receiving hydrophone array, and a signal processor. In this system, a distributed and parallel subarray (DPS) beamforming algorithm is proposed to process the acquired signals from a scene placed in the far field. The DPS beamforming algorithm is an approximate method for the sonar signal processing with an advantageous computational efficiency. The direct method (DM) and fast Fourier transform (FFT) beamforming are compared with DPS beamforming for the memory and computational requirements. Based on this algorithm, a prototype was developed, which has been extensively employed in the lake and sea trials. The trials demonstrate that the system can achieve the 3-D imaging of the scene and meet the real-time requirement of the underwater operations. [ABSTRACT FROM PUBLISHER]

In this paper, a new calibration method for gain and phase errors in large uniform rectangle arrays is proposed for underwater 3-D sonar imaging systems. It requires only one calibrator source at an unknown position in the far field. An efficient and speedy three-step-iteration algorithm is performed first to provide a robust direction-of-arrival (DOA) estimator in the presence of gain and phase errors. It then follows with the gain and phase errors estimation using a spatial matched filter. Finally, a maximum a posteriori (MAP) exercise is executed to further adjust the estimated phase parameters and the source direction. The statistical performance of the proposed algorithm will be demonstrated and compared to representative methods. It will be shown that the proposed method is computationally effective and asymptotically efficient in Monte Carlo simulations. [ABSTRACT FROM PUBLISHER]