Accurate long-range acoustic ranging is a critical enabling technology for Internet of Underwater Things (IoUT), particularly in deep-sea environments where electromagnetic and optical signals fail to propagate efficiently. In this regard, we propose an Acoustic Ranging algorithm based on BELLHOP ranging model and Dynamic ranging-ratio model (ARBD). Specifically, in response to the limitations of traditional ranging algorithms such as the shape of the Sound Speed Profiles (SSP), lack of consideration for reflection effects, and complex nonlinear root-finding methods, we transform the BELLHOP acoustic toolbox into a BELLHOP ranging model. A novel ARBD algorithm is developed by combining the fast convergence solution strategy of the Dynamic Ranging-ratio Model (DRM) with ray tracing. Furthermore, the Cramér-Rao lower bound (CRLB) is derived to establish the theoretical performance limits. The results demonstrate that the proposed algorithm reduces ranging errors compared to traditional algorithms, while maintaining fast computational efficiency and adaptability, particularly in long-range distance measurements in deep-sea environments.

Internet of Underwater Things (IoUT) has emerged as a critical infrastructure for marine exploration and resource utilization, yet faces inherent challenges in data collection due to the limitations of multi-hop transmission. Multiple Autonomous Underwater Vehicles (AUVs) systems present a promising solution in data collection; however, conventional multi-AUV data collection schemes struggle with formation collaboration awareness, high-dimensional observation spaces, and environmental non-stationarity in underwater scenarios. To address these limitations, this paper proposes a Distributed Switching Multi-Agent Attention Actor-Critic (DS-MAAC) framework for energy-efficient underwater data collection. Specifically, we consider a scenario where the location of the sensor nodes are unknown. In this context, multi-AUV systems initially adopt a leader-follower formation for stable communication and obstacle avoidance, transitioning to a distributed system at appropriate timings to balance the effectiveness and efficiency of exploration. The fuzzy perception map is proposed to assist in distributed switching and progressively reduce environmental uncertainty. To improve the ability of AUVs to extract critical information from high-dimensional observation spaces, we introduce an attention mechanism to further enhance the collaborative collection performance. The simulation results demonstrate that the proposed DS-MAAC algorithm outperforms other baseline methods across multiple performance metrics, including reward, collection path, collection rate, collection efficiency, energy consumption, delay rate, and value of information, with performance improvements ranging from 11.07% to 56.30%. Sensitivity analyses are conducted to verify the robustness of the DS-MAAC algorithm and provide practical guidelines for parameter selection.

Underwater positioning is a crucial technology for Autonomous Underwater Vehicles (AUVs) to effectively carry out a range of underwater tasks. With an underwater environment that is complex and constantly changing, it can be challenging for a single positioning technology to keep up with long-range, multi-target, and high-precision applications. Solutions in the field of underwater positioning are continually evolving and improving. A multi-sensor fusion positioning scheme, which integrates multiple sensors including the Inertial Navigation System (INS), Long Baseline (LBL), Doppler Velocity Log (DVL), Conductivity Temperature Depth (CTD) and Depth Gauge (DG), can provide more accurate and reliable positioning. In this paper, a Bayesian inversion based Multi-sensor Fusion Localization (BIMFL) algorithm is proposed to enhance the performance of underwater fusion positioning. The proposed algorithm combines the strengths of multiple sensors and provides more precise location information. BIMFL uses the residual values of the measured data from various sensors as observations. Bayesian inversion effectively integrates prior information with observational information, both of which have their covariance matrices considered, hence determining the appropriate parameters of the fusing positioning system. Additionally, an update iterative process based on the Extended Kalman Filter (EKF) is adopted in BIMFL to refine positioning results. Simulation and experimental results have shown that BIMFL surpasses traditional single-sensor and EKF-based filtering methods.

Autonomous underwater vehicle (AUV) has been widely used in underwater missions. Cooperative localization (CL) is a key technology especially for multi-AUVs collaborative operations. With great demands for accurate and real-time localization, the error dispersion in nonlinear fusion and information transmission difficulties caused by underwater environment limitations become challenges in multi-AUVs CL. In this article, a federated learning (FL) framework for multi-AUVs CL is designed, based on which a novel CL algorithm combining the FL and extended Kalman filter (EKF) is proposed. The proposed FL-EKF algorithm can fuse the advantages of EKF and FL adequately to realize high-precision real-time underwater CL in long-duration operations. Simulations and experiments are conducted to verify the performance of the proposed algorithm.

For the increasingly strong demand for low-orbit navigation enhancement, a novel navigation and communication integrated (NAVCOM) waveform for the LEO constellation is proposed, which can conduct communication and navigation simultaneously. Zadoff-Chu (ZC) sequence with controllable power is superimposed on the communication signal in the frequency domain as the positioning signal. Furthermore, positioning can be conducted with both time-domain correlation detection (TDCD) and frequency-domain phase estimation (FDPE) for higher ranging accuracy benefits from the Fourier invariance of ZC sequence. Interference between positioning and communication components is analyzed, and the Cramer-Rao low bound (CRLB) of ranging error is given. The performance evaluations show that the novel waveform can achieve high-precision positioning without signifilcantly affecting communication performance.

Accurate cooperative localization (CL) is a crucial requirement for multi-AUVs operation. However, the Velocity Uncertainty (VU) measured by DVL (Doppler Velocity Logger) in certain environments makes it challenging for master-slaver CL. A single beacon-aided CL positioning method based on Maximum Correntropy Criterion (MCC) is proposed in this paper. Firstly, a velocity correction method is presented to deal with the problem of VU. Then a circular intersection solving method relying on time transmission delay is adopted to achieve slaver AUV's location. In which a judging rule is designed to distinguish mirror solutions in circular intersection equations. Simulation results verify that the proposed algorithm can achieve a high localization accuracy with velocity uncertainty.

Based on data fusion technology, multi-sensor fusion positioning merges several positioning sources together to achieve an optimal positioning result by making full use of all the homogeneous or heterogeneous information from different fusion sensors. However, there has not been much research carried out about the effectiveness evaluation of multi-sensor fusion positioning system. In this paper, a self-feedback effectiveness evaluation algorithm is proposed which can not only evaluate multi-sensor fusion positioning systems, but also improve positioning performance by adopting feedback information. Besides traditional evaluation parameters, confidence level and plug and play capability are proposed as new evaluation parameters to estimate effectiveness of multi-sensor fusion positioning system. Simulations verify the efficiency of proposed evaluation parameters and self-feedback effectiveness evaluation algorithm.

Fingerprint positioning is considered to be one of the most promising indoor positioning technique. The most significant technological bottleneck of fingerprint positioning technology is the high labor cost of the radio map establishing process. Many radio map fast establishing algorithms were proposed to solve this problem. There is no evaluation norm for these algorithms but positioning accuracy, which is supposed to be unreliable. This paper presents a radio map establishing algorithm evaluation criterion. To avoid the influence of other factors, the proposed evaluation criterion focuses on the quality of the radio map only. The Euclidean distance between radio maps was calculated to indicate the radio map quality. Simulation results show that the evaluation criterion can effectively measure the quality of radio maps. Some typical radio map establishing algorithms are also compared in this paper based on the proposed evaluation criterion.

Multi-source fusion localization is an effective approach when a single source is unavailable or the positioning accuracy is unsatisfied, and it can take advantages of different location sources to achieve a better result. Data fusion is a process of merging different solutions and techniques with disparate types of information. In order to provide users with better location based services, this paper proposes a factor graph based multi-source data fusion algorithm for wireless localization. Different fusion sources are divided into multi-levels by adopting confidence level estimate algorithm. By using sum-product algorithm, the soft-information is calculated to complete the fusion process. Through some simulations, we can see that the proposed algorithm can improve the positioning accuracy greatly. At the same time, it has low complexity and a plug and play capability.