Most applications of underwater acoustic sensor networks (UASNs) rely upon reliable location information of sensor nodes. However, the harsh underwater environment makes localization more challenging as compared to terrestrial sensor networks. This paper is concerned with a localization issue for UASNs, subject to clock asynchronization and node mobility. A hybrid architecture including autonomous underwater vehicles (AUVs), active and passive sensor nodes is designed, where AUVs act as anchor nodes to provide localization information for sensor nodes. In order to eliminate the effect of asynchronous clocks and compensate the mobility of sensor nodes, an asynchronous localization algorithm with mobility prediction is provided for active and passive sensor nodes. Then, two localization optimization problems are formulated as minimizing the sum of all measurement errors. To solve the localization optimization problems, iterative least squares estimators are designed. In addition, the convergence analysis and Cramér-Rao lower bound for the localization errors are also provided. Finally, simulation results show that the proposed localization approach can reduce the localization time by comparing with the exhaustive search-based localization method. Meanwhile, the asynchronous algorithm in this paper can effectively eliminate the impact of the clock asynchronization and node mobility.

The localization of autonomous underwater vehicles (AUVs) in anchor-free environments has always been a difficult problem due to the lack of global positioning systems and absolute references. In general, AUVs localize themselves by dead reckoning (DR), whereas the localization error grows without bound. To alleviate the growth of the localization errors, we propose intermittent belief propagation based dead reckoning (IBPDR) as a cooperative localization (CL) framework. In IBPDR, AUVs use DR to localize themselves and periodically correct DR’s deviation with CL methods. The intermittent feature of IBPDR reduces communication costs among AUVs by decreasing the frequency of CL. In the IBPDR framework, we design a particle-based underwater-adaptive belief propagation (UABP) algorithm for CL. The UABP algorithm is naturally distributed and viable in nonlinear and non-Gaussian situations. Thus, it is suitable for CL issues. Furthermore, the UABP algorithm is robust to the accumulated inertial measurement errors and reduces communication costs among AUVs. Moreover, we propose a particle-based current-aided filter to further improve the localization accuracy by comparing AUVs’ ambient current observations with the available current maps. Simulation results validate the proposed algorithms by comparisons with alternative approaches in localization accuracy, communication costs, and robustness to abnormal cases, such as packet loss, ranging bias, and outliers.

Most applications of underwater acoustic sensor networks (UASNs) rely on accurate location information of targets. However, the asynchronous clock, stratification effect, and strong-noise characteristics of underwater environment make target localization more challenging as compared with terrestrial sensor networks. This paper focuses on an asynchronous localization issue for underwater targets, subjected to the isogradient sound speed and noise measurements. A network architecture including surface buoys, sensors, and the target is first designed, where the clocks on sensors and the target are not required to be synchronized. To eliminate the effect of asynchronous clocks, we establish the relationship between the propagation delay and the position. Particularly, the ray tracing approach is adopted to model the stratification effect. Then, a localization optimization problem is formulated to minimize the sum of all measurement errors. To solve the localization optimization problem, a consensus-based unscented Kalman filtering (UKF) localization algorithm is proposed, where the convergence conditions and Cramér–Rao lower bounds are also given. Finally, simulation results reveal that the proposed localization approach can reduce the localization time by comparing with the exhaustive search method. Meanwhile, the consensus-based UKF localization algorithm can improve localization accuracy as compared with other works.

Underwater communications are fundamental techniques for the Internet of Underwater Things (IoUT) to establish information links among underwater devices. Recently, orthogonal chirp division multiplexing (OCDM) has drawn great attention in underwater communications due to its advantages in dealing with burst interference in both time and frequency domains. However, the harsh underwater channel conditions (e.g., multipath propagation, temporal variations, and significant Doppler effects) have not been systematically considered in current underwater OCDM systems. In this paper, two transmission block structures (Structure A and Structure B) based on OCDM are designed, and a unified OCDM receiver framework is proposed under a single scale multipath lag (SSML) channel model. In the receiver framework, the Doppler scaling factor and carrier frequency offset (CFO) are sequentially compensated, and receiver algorithms that apply the separate or superimposed transmission features (for the pilot and data) of Structure A and Structure B are proposed, respectively. To be specific, a multi-peak Doppler scaling factor estimation algorithm and a closed-form CFO estimator are designed for the received signal of Structure A. Moreover, a null symbol based CFO estimation algorithm is presented for the received signals of Structure B. The effectiveness and advantages of the proposed methods are analyzed and validated through simulations and channel data by comparisons with existing methods under different conditions.

The noncooperative target tracking is an important issue for the Internet of Underwater Things (IoUT). Autonomous underwater vehicles (AUVs) are preferred options to achieve the target tracking especially in anchor-free environments, where no equipments with known positions, named anchors, are deployed. The self-organized mobile network of multiple AUVs can localize and continuously monitor the target. Thus, in this article, we investigate the problem of the noncooperative target tracking using multiple AUVs in anchor-free environments. In the target tracking, AUVs play as references and their positions need to be estimated first. We propose a multi-AUV cooperative localization and target tracking (MCLTT) framework based on belief propagation (BP). Under MCLTT, BP-based underwater cooperative localization (BPUCL) and noncooperative mobile target tracking (NcMTT) algorithms are designed. Gaussian approximations are used to reduce communication costs among AUVs. The designed BPUCL alleviates the impact of the accumulated errors in the inertial measurements of AUVs and slows down the growth of the localization error. In NcMTT, model-free position prediction processes are proposed and a novel form of the particle-based BP message is designed using time-difference-of-arrival (TDOA) measurements. The simulation results validate the proposed algorithms by comparing with state-of-the-art methods.

Jointdetection and communication systems demonstrate their unique efficiencies in both spectrum and cost. In this letter, we propose a sonar-communication (SonarCom) system for underwater scenarios based on two multicarrier (MC) waveforms, which are orthogonal frequency division multiplexing (OFDM) and orthogonal chirp division multiplexing (OCDM) waveforms. Different types of waveforms provide flexibilities to deal with various underwater environments. Furthermore, a generalized likelihood ratio test (GLRT) is proposed for detection to deal with multipath channels. Time aliasing techniques are applied to leverage the signal structure. Moreover, a minimum mean square error (MMSE) equalizer is developed for communication to counter frequency-selective fading. Simulation results show that the GLRT detector for the SonarCom system outperforms the existing matched filter (MF). The OCDM scheme maintains better communication performance than the OFDM one.