This paper presents new pseudolinear estimation algorithms for angle-of-arrival (AOA) target motion analysis when the sensor locations are not precisely known. Sensor location errors can cause significant performance degradation for the existing pseudolinear estimator (PLE) and its variants. In particular, we analytically show that, in the presence of sensor location errors, the well-known weighted instrumental variable estimator (WIVE) is no longer asymptotically unbiased due to the non-vanishing correlation between the instrumental variable matrix and the pseudolinear noise vector. To ameliorate this bias problem, a novel bias compensation method is developed for the WIVE, which is proven to be approximately efficient for a large number of measurements under the small noise assumption. In addition, a selective-angle-measurement (SAM) strategy is integrated into the bias-compensated WIVE (BCWIVE) to strengthen the correlation between the instrumental variable matrix and the measurement matrix, alleviating the performance degradation in large AOA and sensor location noises, and unfavourable geometries. The performance advantages of the proposed BCWIVE and SAM-BCWIVE over the PLE, its WIVE variants and the maximum likelihood estimator are demonstrated by way of extensive simulation examples.

There are numerous studies showing that there is a constant increase in the ocean ambient noise level and the ever-growing demand for developing algorithms for detecting weak signals in ambient noise. In this study, we utilize dynamical and statistical complexity to detect the presence of weak ship noise embedded in ambient noise. The ambient noise and ship noise were recorded in the South China Sea. The multiscale entropy (MSE) method and the complexity-entropy causality plane (C-H plane) were used to quantify the dynamical and statistical complexity of the measured time series, respectively. We generated signals with varying signal-to-noise ratio (SNR) by varying the amplification of a ship signal. The simulation results indicate that the complexity is sensitive to change in the information in the ambient noise and the change in SNR, a finding that enables the detection of weak ship signals in strong background ambient noise. The simulation results also illustrate that complexity is better than the traditional spectrogram method, particularly effective for detecting low SNR signals in ambient noise. In addition, complexity-based MSE and C-H plane methods are simple, robust and do not assume any underlying dynamics in time series. Hence, complexity should be used in practical situations.

Underwater target passive tracking has ubiquitous applications in oceanic fields for both commercial and military purposes. The underwater wireless sensor networks (UWSNs) draw great interest and become promising tools for underwater target passive tracking. However, batteries provide power for nodes in the UWSNs, and it is unrealistic to replace them when their energies are exhausted. Therefore, it is essential to study on energy-efficient underwater target passive tracking algorithm. To make a tradeoff between tracking accuracy and energy consumption, this paper proposes a multi-sensor cooperative bearing-only passive tracking algorithm based on dynamic clustering via UWSNs. Under the framework of distributed fusion, we utilize the linear minimum variance fusion criterion to minimize the trace of fusion error covariance. Furthermore, we introduce the dynamic clustering process into passive target tracking, select cluster head nodes from the perspective of energy consumption, and choose cluster member nodes through an adaptive node selection method, which formulates the nodes selection problem into a knapsack problem in mathematics. Finally, simulation results and analysis demonstrate that this algorithm can effectively reduce energy consumption on the premise of sufficient tracking accuracy.

Orthogonal signal-division multiplexing (OSDM) is a promising modulation scheme that provides a generalized framework to unify orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency-domain equalization. By partitioning each data block into vectors, it allows for a flexible configuration to trade off resource management flexibility with peak-to-average power ratio. In this paper, an OSDM system is proposed for underwater acoustic communications. The channel Doppler effect after front-end resampling is modeled as a common time-varying phase on all propagation paths. It leads to a special signal distortion structure in the OSDM system, namely, intervector interference, which is analogous to the intercarrier interference in the conventional OFDM system. To counteract the related performance degradation, the OSDM receiver performs iterative detection, integrating joint channel impulse response and phase estimation, equalization, and decoding in a loop. Meanwhile, to avoid inversion of large matrices in channel equalization, frequency-domain per-vector equalization is designed, which can significantly reduce the computational complexity. Furthermore, the performance of the proposed OSDM system is evaluated through both numerical simulations and a field experiment, and its reliability over underwater acoustic channels is confirmed.

Orthogonal signal-division multiplexing (OSDM) is a generalized modulation scheme to bridge the gap between orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency-domain equalization. It allows significantly more flexibility in system design; however, over doubly-selective channels, it suffers from a special signal distortion structure, namely inter-vector interference, which is analogous to inter-carrier interference in conventional OFDM. To analyze its effect, in this paper, the complex exponential basis expansion model (CE-BEM) is used to approximate the doubly-selective channel. We show that the composite channel matrix of OSDM systems is cyclically block-banded in this case, and the blocks in its main band can be further diagonalized. By exploiting this unique matrix structure, low-complexity block and serial OSDM equalization algorithms are then proposed. These two equalization algorithms are based on block LDLH factorization and block iterative matrix inversion, respectively, both of which are implemented in a transformed domain to avoid direct inversion of large matrices. In addition, a CE-BEM channel estimation method is designed for OSDM systems, which uses frequency-shifted Chu sequences as pilots to ease the computation. Numerical simulations are finally provided to justify the validity of our channel equalization and estimation algorithms.

This paper addresses the channel estimation problem for Orthogonal time frequency space (OTFS) systems in the presence of fractional Doppler. Channel estimation with fractional Doppler can be considered as an off-grid sparse signal recovery problem, where the virtual sampling grid is introduced in the delay-Doppler domain. First-order linear approximation as a conventional approach to estimate fractional Doppler and sparse signal in OTFS systems. However, linear approximation approach may incur considerable modeling errors, since the finite sampling grid may be insufficiently refined in the OTFS systems. Furthermore, this error will deteriorate recovery performance. To solve this problem, we propose an efficient sparse Bayesian learning (SBL) method to jointly estimate the fractional Doppler and sparse channel vectors. Specifically, we reformulate the input-output relationship for fractional Doppler, and further present an off-grid channel estimation model. Then, an in-exact block majorization-minimization (MM) algorithm is adopted to iteratively update the associated parameters under the SBL framework. Fractional Doppler can be iteratively refined to eliminate the off-grid gap using the gradient descent method. The proposed scheme presents an exact channel estimation model that avoids any approximation operation, so that it effectively alleviates the model error. Simulation results show that the proposed channel estimation method significantly outperforms state-of-the-art methods