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Underwater monitoring and surveillance systems are essential for underwater target detection, localization, and classification. The aim of this work is to investigate the possibility of target detection by using data transmission between communication nodes in an underwater acoustic (UWA) network, i.e, reusing acoustic communication signals for target detection. A new target detection method based on estimation of the time-varying channel impulse response between the communication transmitter(s) and receiver(s) is proposed and investigated. This is based on a lake experiment and numerical experiments using a simulator developed for modeling the time-varying UWA channel in the presence of a moving target. The proposed detection method provides a clear indication of a target crossing the communication link. A good similarity between results obtained in the numerical and lake experiments is observed.

This article proposes design techniques for partially calibrated sparse linear subarrays and algorithms to perform direction-of-arrival (DOA) estimation. First, we introduce array architectures that incorporate two distinct array categories, namely type-I and type-II arrays. The former breaks down a known sparse linear geometry into as many pieces as we need, and the latter employs each subarray such as it fits a preplanned sparse linear geometry. Moreover, we devise two DOA estimation algorithms that are suitable for partially calibrated array scenarios within the coarray domain. The algorithms are capable of estimating a greater number of sources than the number of available physical sensors, while maintaining the hardware and computational complexity within practical limits for real-time implementation. To this end, we exploit the intersection of projections onto affine spaces by devising the generalized coarray multiple signal classification (GCA-MUSIC) in conjunction with the estimation of a refined projection matrix related to the noise subspace, as proposed in the GCA root-MUSIC algorithm. An analysis is performed for the devised subarray configurations in terms of Degrees of Freedom, as well as the computation of the Cramér–Rao lower bound for the utilized data model, in order to demonstrate the good performance of the proposed methods. Simulations assess the performance of the proposed design methods and algorithms against existing approaches.

This brief proposes a recursive constrained sine second-order error promoting adaptive (RCSSOEPA) algorithm. Compared with classical recursive method, the RCSSOEPA algorithm can achieve better steady state performance in impulsive-noise. In general, the sine second-order error (SSOE) is constructed to devise a new recursive constrained adaptive-filtering within the least-squares framework for solving linear constrained optimization problems. The mean-square (MS) stability of the RCSSOEPA and its theoretical instantaneous MS deviation under Gaussian and non Gaussian noise are analyzed, numerically investigated and discussed in detail. Simulated results are reported to give a confirmation of the theoretical analysis, and show that the RCSSOEPA outperforms recent developed constrained adaptive filtering algorithms in the estimation misalignment and when used for system identification under impulsive-noise.

In forward blind source separation (FBSS) scenarios, subband adaptive filtering (SAF) algorithms provide fast convergence due to the SAF decorrelation ability. However, existing SAF algorithms suffer from a signal delay stemmed from the utilization of the synthesis filters. To solve this problem, we propose a delayless multisampled multiband-structured subband FBSS (DMSFBSS) algorithm by inserting a delayless multisampled multiband SAF structure. For a fixed SAF step-size, there is also a tradeoff between the convergence speed and steady-state error. We break this relationship by further developing an optimal DMSFBSS (ODMSFBSS) algorithm that minimizes the mean squared deviation (MSD) of DMSFBSS with respect to the subband gain vectors, to provide simultaneously fast convergence and low steady-state behavior. Intrinsically, the ODMSFBSS algorithm is an extension of the Kalman filtering theory over the subband-based FBSS structure. We also present an analysis of the algorithm stability. A vectorized implementation of the ODMSFBSS algorithm is proposed to reduce the computational complexity from $\mathcal {O}(L^{2})$ to $\mathcal {O}(L)$ , where $L$ is the length of the adaptive filter. Since adaptive FBSS scenarios require a voice activity detection (VAD) technique to control the alternating update of two adaptive filters, we develop two VAD approaches not relying on the priori knowledge of the speech source signal, thereby guaranteeing the practicability of the proposed ODMSFBSS algorithm. Simulation results in high and low SNR environments confirm that the proposed ODMSFBSS algorithm outperforms existing state-of-the-art algorithms.

In this brief, we address the classical tradeoff between the convergence rate and steady-state error in the proportionate subband adaptive filter (PSAF) algorithm by designing an optimal variable step-size (OVSS) strategy that minimizes the mean square deviation at each iteration. To keep low computational complexity, we also present the vector implementation of OVSS. Furthermore, we extend the OVSS mechanism to the robust PSAF algorithm, to achieve fast convergence and low steady-state error in the impulsive noise. Simulations in an acoustic echo cancellation scenario have illustrated that the proposed algorithms are superior to existing state-of-the-art algorithms.