Capon beamforming is often applied in passive sonar to improve the detectability of weak underwater targets. However, we often have no accurate prior information of the direction-of-arrival (DOA) of the target in the practical applications of passive sonar. In this case, Capon beamformer will suffer from performance degradation due to the steering vector error dominated by large DOA mismatch. To solve this, a new robust Capon beamforming approach is proposed. The essence of the proposed method is to decompose the actual steering vector into two components by oblique projection onto a subspace and then estimate the actual steering vector in two steps. First, we estimate the oblique projection steering vector within the subspace by maximizing the output power while controlling the power from the sidelobe region. Subsequently, we search for the actual steering vector within the neighborhood of the estimated oblique projection steering vector by maximizing the output signal-to-interference-plus-noise ratio (SINR). Semidefinite relaxation and Charnes-Cooper transformation are utilized to derive convex formulations of the estimation problems, and the optimal solutions are obtained by the rank-one decomposition theorem. Numerical simulations demonstrate that the proposed method can provide superior performance, as compared with several previously proposed robust Capon beamformers in the presence of large DOA mismatch and other array imperfections.

In this paper, the issue that the underwater unmanned vehicle (UUV) with a sonar array achieves the passive detection of vessel targets by detecting the tones radiated from the targets is considered. The multi-beam low-frequency analysis and recording method is widely applied in a manned sonar system. The sonar operator provides an auxiliary decision to extract the target tones from the multiple beams output. However, the complexity of the multi-dimensional information fusion makes it difficult to apply the multi-beam processing in the unmanned sonar system. Aiming at this problem, we introduce the self-adjusting characteristics of adaptive line enhancer to a time domain broadband beamformer and then propose a self-steering broadband beamformer. The proposed beamformer can adaptively steer the main beam to the direction-of-arrival (DOA) of the tonal target. There is no need to pre-form the multiple beams. The complexity of the UUV-based tone detection is reduced. Theoretical derivation and simulation experiments verify that the main beam of the proposed beamformer can track the DOA of tonal target which is rapidly changing. Meanwhile, the tonal interferences as well as the wide-band noise are well suppressed.

This study considers the problem of adaptive detection for a multichannel signal in the presence of spatially and temporally correlated compound‐Gaussian disturbance. By modelling the disturbance as a multichannel autoregressive process, the authors resort to a parametric generalised likelihood ratio test (PGLRT) with the capability of dimension reduction. In the derivation, due to treating the random texture components of disturbance as deterministic unknown parameters, the test requires no a priori knowledge about the disturbance statistics. Moreover, the involving highly non‐linear maximum‐likelihood (ML) estimation procedure underlying the PGLRT is subtly addressed by ad hoc ML estimators. Accordingly, the derived detector applies to challenging cases with limited or even no training signals for parameter estimation. Such cases (especially when training‐free case) are attractive for signal detection in heterogeneous or fast changing environments. In simulations, the performance evaluations are conducted by means of Monte Carlo trials. Numerical results investigate the issues of the estimator efficiency, detection and false alarm probabilities in terms of several concerned cases. Compared to the existing and benchmark detectors, the proposed PGLRT achieves improved detection performance, in general.

Considering the requirement of the near-field calibration under strong underwater multipath condition, a high-precision geometric calibration method based on maximum likelihood estimation is proposed. It can be used as both auxiliary-calibration and self-calibration. According to the near-field geometry error model, the objective function of nonlinear optimization problem is constructed by using the unconditional maximum likelihood estimator. The influence of multipath on geometric calibration is studied. The strong reflections are considered as the coherent sources, and the compensation strategy for auxiliary-calibration is realized. The optimization method (differential evolution, DE) is used to solve the geometry errors and sources’ position. The method in this paper is compared with the eigenvector method. The simulation results show that the method in this paper is more accurate than the eigenvector method especially under high signal-to-noise ratio (SNR) and multipath environment. Experiment results further verify the effectiveness.