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.

Target approaching is an essential ability in marine applications, such as underwater rescue. The recent advances in autonomous underwater vehicles (AUVs) make them ideal options to deliver the target approaching in anchor-free environments, where no device with a known position (anchor) is pre-deployed. To approach a moving target, the trajectory planning of AUVs is an indispensable technical guarantee. However, due to the lack of anchors and the error accumulation during the navigation, the position errors of AUVs grow without bound and degrade trajectory planning performance. Thus, an AUV localization procedure is required and should be used in combination with the trajectory planning. In this paper, we research the multiple-AUV trajectory planning based on AUV cooperative localization and propose a framework named multiple-AUV cooperative localization based trajectory planning (MCLTP). Under MCLTP, scalable cooperative localization for multiple AUVs (SCLMA) and trajectory planning for multiple AUVs (TPMA) algorithms are derived. The design of SCLMA focuses on improving the localization accuracy by alleviating the accuracy degradation caused by the accumulated errors in the inertial measurements. TPMA formulates the trajectory planning into a convex optimization problem with balanced target detection probability and tracking accuracy. Furthermore, the considerations of the practical constraints, such as the kinematic and communication limitations of AUVs, collision and obstacle avoidance, make TPMA reliable in the complex marine environments. Simulation results reveal the advantages of the proposed methods by comparisons with the state-of-the-art alternative algorithms.

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.

This article presents a hybrid time-of-arrival (TOA) and angle-of-arrival (AOA) cooperative localization algorithm, termed HTACL, for the multiple-autonomous-underwater-vehicle (multi-AUV) navigation in the absence of anchors (devices with known positions). Compared with traditional TOA-based methods, the joint use of TOA and AOA measurements improves the quality of position prediction and inter-AUV relative position information in localization. Specifically, by exploring the relationship between AOA measurements and AUV attitudes, the impact of the accumulated errors in inertial measurements on the position prediction is alleviated. Moreover, the directivity and resolution of relative position representation is improved through involving AOA information. As a result, the localization accuracy can be improved by HTACL. The design of HTACL also considers the influence of the long propagation delay during AUV cooperation to enhance its adaptability to harsh underwater environments. Moreover, HTACL is naturally distributed and with good scalability. The abovementioned features make it applicable and suitable for multi-AUV localization issues. Through various simulations, the advantages of HTACL are verified and it could stably provide better localization accuracy compared with the state-of-the-art alternative methods under different conditions.

The asynchronization among autonomous underwater vehicles (AUVs) is inevitable due to the inherent offsets and drifts in clocks, which critically degrades the accuracy of AUV cooperative localization (CL), especially in underwater anchor-free environments. This article designs a novel relative measurement representation called time difference of communication (TDOC) to eliminate the impact of asynchronization, which only requires one-way inter-AUV communications at different time steps even in the presence of large asynchronization. By exploiting TDOC, a joint spatial-temporal CL model is established with specially constructed measurement vectors, and a CL method, termed TDOC-CL, is proposed. It transforms the nonlinear observations into a weighted least-square (WLS) formation and is solved via iterative optimization, where various coupled uncertainties (i.e., measurement biases, measurement noises, and position errors) are involved and analyzed. Moreover, to compensate the accuracy degradation caused by the uncertainties, the TDOC-CL with bias reduction and error compensation, named iTDOC-CL-BREC, is further designed. In addition, the hybrid Cramér-Rao lower bound (HCRLB) is derived as the performance benchmark to evaluate the localization behaviors. Simulations and field experiments show that iTDOC-CL-BREC can provide higher localization accuracy compared with state-of-the-art methods, especially for long-duration tasks.