For the sonar arrays mounted on an unmanned underwater vehicle (UUV), the direction-of-arrival (DOA) estimation of the far-field (FF) weak sources is influenced by the near-field (NF) interferences generated from the radiated self-noise of the UUV and the FF interferences simultaneously. To address the problem, a sparsity-based DOA estimation method resistant to the NF and FF interferences is proposed in this paper. This method isolates the FF signals from the NF signals by sparse reconstruction. Additionally, subspace projection is applied to address the masking problem of the weak target signal by the strong interferences in the spatial domain, effectively enhancing the capacity of estimating the DOA of the weak target signal in the presence of strong interferences. Numerical simulations and experimental results demonstrate the effectiveness of the proposed method. Compared to other advanced DOA estimation methods, the proposed method exhibits better DOA estimation performance in the presence of strong NF and FF interferences.

Adaptive line enhancers (ALEs) have been widely used in passive sonars for enhancing narrowband discrete components (known simply as lines or tonals) radiated by surface or underwater targets. Unfortunately, the performance of the conventional ALE based on the least‐mean‐square (LMS) adaptive algorithm is limited by the high steady‐state misadjustment, which limits the output signal‐to‐noise ratio (SNR) of the conventional ALE. To break through the limit of the conventional ALE, this study proposes a sparsity‐based ALE (SALE). The SALE takes into account the frequency‐domain sparsity of narrowband discrete components and realises the adaption in the frequency domain. A ‐norm sparse penalty is incorporated into authors’ frequency‐domain adaption to reduce the misadjustment dramatically. The simulations show that the SNR gain of the SALE is 6.3 dB higher than that of the conventional ALE. The results of processing the real data collected in the sea trial also demonstrate that authors’ proposed SALE outperforms the conventional ALE. To reduce the computational complexity of the SALE, a fast SALE (FSALE) is also proposed. In authors’ setup, the computational complexity of the FSALE is one order of magnitude lower than that of the SALE, with comparable SNR performance.

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.

A spatial rotation technique in application to unmanned underwater vehicle (UUV) sonar arrays is proposed. The essence of the technique is to rotate the signals of interest from the array coordinates to geodetic coordinates while providing robustness against interferences and preserving the white noise property at the output. Convex formulation of the rotation matrix design problem is derived using the semi‐definite relaxation technique and the optimal solution is obtained by rank‐one decomposition theorems. Using the authors’ technique, the subspace‐based direction of arrival estimation methods can get large snapshot support for weak sources in the UUV motion‐case.

This paper addresses the challenge of pulse source localization in a deep sea bottom bounce area using multipath time differences via a vertical dual-hydrophone configuration. By analyzing the variation trends of multipath time-delay differences with respect to source range and depth in deep sea bottom bounce area, a set of analytical expressions relating multipath time-delay differences to source range and depth is derived based on geometric relationships. Furthermore, by integrating ray tracing technology with simulated annealing and Bayesian inversion methods, source localization within the range-depth two-dimensional plane is achieved. Simulation results indicate that the source positions calculated using the geometric analytical expressions can provide starting values for the other two methods. The Bayesian inversion approach demonstrates higher accuracy in estimating source range compared to simulated annealing. However, simulated annealing yields better depth estimation results with higher concentration. Sea trial results confirm the effectiveness of both methods in localizing cooperative sources under high signal-to-noise ratio conditions.

In non-cooperative DSSS signal reception, the accurate estimation of the pseudo-noise (PN) sequence period is essential for successful despreading and information recovery. In this paper, we propose an average second-order-moment autocorrelation method based on the accumulated code difference function to enhance the estimation accuracy under low-signal-to-noise-ratio (SNR) conditions. The proposed approach involves three key aspects: First, a quadrature receiver is employed to mitigate the impact of the random initial phase on demodulation, enabling the full utilization of the signal energy. Second, a code difference function is constructed using transition information between adjacent spreading codes, and by leveraging the strong correlation between these functions, accumulated processing effectively suppresses the noise influence. Third, the autocorrelation result of the accumulated code difference function displays periodic peaks separated by intervals equal to the PN sequence period, allowing for period estimation through peak interval extraction. In addition, the introduced average-second-order-moment technique addresses the peak loss caused by information code randomness while further smoothing noise. Simulation and experimental results verify the effectiveness and practicality of the proposed method, which outperformed the average-second-order-moment method by about 1.5 dB and the accumulated-power-spectrum-reprocessing method by about 2 dB.