Underwater target tracking is a fundamental technology for marine development, providing real-time position estimates of the interested targets. However, due to the harsh underwater environment and the noncooperativity of targets, improving tracking accuracy remains a challenge, especially for highly-maneuvering targets. To address this problem, based on multi-autonomous underwater vehicle (multi-AUV) networks, this paper extends the idea of interacting multiple models (IMM) and designs a bearing-only cooperative tracking algorithm in the consideration of the harsh underwater acoustic channels. Specifically, in position prediction, the combination of historical information and the concept of IMM reduces the severe time-lagged effect in traditional prediction methods and the model reliance in standard IMM filters. Then, during position update, a rigidity-assisted relative position representation is designed based solely on bearing measurements, which alleviates the impact of information loss due to communication interruptions, significantly enhancing the continuity of target tracking. Moreover, the algorithm design also considers various uncertainties that may concurrently occur underwater (e.g., error accumulation and model mismatches), and robust optimization strategies with the principle of maximum entropy are designed to enhance the environmental adaptability. Through various simulations and field experiments, the advantages of the proposed method have been validated.

RGB-Thermal (RGB-T) multimodal tracking has gained widespread attention due to its robustness in handling complex scenarios. Some existing methods focused on fully fine-tuning RGB-based trackers, which was parameter-inefficient and prone to overfitting due to the scarcity of multimodal data. Therefore, recent studies have explored multimodal prompting strategies, which mainly treat RGB as the dominant modality and TIR as an auxiliary prompting modality. This asymmetric framework lacks adaptability in changing dominant modalities, resulting in reduced robustness. To address these limitations, we propose LRPD: a novel multimodal Low-Rank (LoRA) Prompting and Distillation tracking framework. Specifically, the framework consists of two distinct stages. In the first stage, we pre-train a teacher (ViT-L encoder) and a student model (ViT-B encoder) using our LoRA-Prompting (LoRA-P) module. LoRA-P adopts a symmetric architecture that enables bidirectional cross-modal interaction in a parameter-efficient manner. In the subsequent stage, we design a prompt-driven knowledge distillation framework to transfer knowledge from the large teacher model to the lightweight student model. Task-specific designs, including an enhanced patch masking strategy, a feature alignment projector, deep visual prompts, and specialized distillation losses, are tailored to optimize student's tracking performance. Extensive experiments on three popular RGB-T tracking benchmarks demonstrate our method achieves new state-of-the-art performances.

Cooperative localization aims to continuously provide position estimates for multiple-autonomous underwater vehicle (multi-AUV) systems during task execution such as marine monitoring; it is preferable over noncooperative schemes due to its high accuracy and strong robustness. However, various uncertain factors underwater, including model mismatches, accumulated errors, measurement noises and biases, time-varying communication channels, etc., still challenge the accuracy and robustness of cooperative localization. When such uncertainties arise, the performances of traditional methods degrade significantly. Therefore, this paper proposes a robust multi-AUV cooperative localization method that is able to combat these uncertainties by leveraging the principle of maximum entropy. To be explicit, a message-passing scheme is established using factor graphs, over which a distributed position estimation strategy for AUVs is designed based on belief propagation. To reduce the damages of uncertainties, maximum-entropy distributions are designed respectively for the prediction and correction processes of localization and are realized by particles. Specifically, through enlarging the particle coverage, uncertainty-induced misleading in position estimation is alleviated, and hence higher robustness is achieved. Simulations and field experiments show the advantages of the proposed algorithm over the state-of-the-art methods in terms of localization accuracy, robustness, and scalability.Note to Practitioners—In practical applications, due to the unavailability of global positioning systems underwater, AUV localization still lacks mature and stable solutions. In harsh underwater environments, most theoretical models often fail to accurately describe the practical conditions, leading to widespread mismatches, which severely degrade localization performance. Moreover, uncertainties such as accumulated errors, measurement noises, and position deviations would further reduce the position estimation accuracy. Existing methods usually consider these uncertainties independently, while, in practical uses, uncertainties often emerge in combination, making it challenging to maintain localization accuracy. What is worse is that most sophisticatedly designed algorithms pursue high accuracy, and their adaptability in engineering applications is difficult to guarantee. Hence, this work provides a robust solution for multi-AUV cooperative localization that can handle uncertainties simultaneously. By reducing the sensitivity to different uncertainties, the proposed algorithm can provide sustained high-accuracy position estimates for AUVs in complex underwater environments. The proposed method is experimentally verified and suitable for multi-AUV applications such as oceanic rescue, resource development, and marine monitoring.

Simultaneous target approaching is a fundamental ability for multiple autonomous underwater vehicle (multi-AUV) systems to continuously move toward the target for surrounding, capturing, information gathering, etc., and the efficiency and accuracy of the approaching heavily rely on the qualities of planned trajectories. This work presents a cooperative trajectory planning method for the simultaneous approaching of multi-AUV systems, and further provides strategies for connectivity maintenance and position errors to enhance the adaptability in practical uses. To be specific, a cooperative planning framework is designed with the inspiration of sensor selection, where trajectory planning is converted to a successive candidate selection problem and modeled as convex optimization. Under the framework, the considered simultaneous approaching, connectivity maintenance, and position errors are formulated as constraints and integrated into one convex optimization problem, which can be easily solved via well-established solvers. Various simulations and field experiments have validated the advantages of the proposed method by comparisons with alternative methods.

In the operation of multiple autonomous underwater vehicle (AUV) networks, AUV self-localization, inter-AUV communication, and AUV trajectory planning (collectively referred to as LCP) are fundamental technologies. Among them, accurate position estimation is often a prerequisite for effective planning, while communication is essential in both parts. Previous studies have typically addressed these three areas independently, yet their conflicting demands on resources, objectives, etc., frequently pose significant challenges to performance improvement. This paper investigates the LCP from an integrated perspective and a co-designed approach jointly addresses all of them is proposed based on graphical models and belief propagation. Specifically, guided by the various underwater communication conditions, a cooperative localization algorithm for AUVs is designed with the assistance from trajectory planning. Different from the previous, planning results are directly used for localization to resist harsh communications, rather than indirectly affecting localization accuracy by, for example, optimizing geometric relationships. As a result, the feedback enhancement from trajectory planning to AUV localization is realized and the mechanism of their mutual promotion for different communication conditions is achieved. Moreover, considering the changing trajectories, the proposed cooperative localization algorithm is further refined for underwater obstacle-avoidance scenarios. Various simulations and field experiments validate that the proposed algorithm can improve the overall performance of LCP by comparisons with state-of-the-art alternative methods.

This letter investigates the cooperative localization of autonomous underwater vehicle (AUV) systems in underwater anchor-free environments. Different from previous works, the impacts of mutual asynchronization among AUVs and acoustic wave bends are collectively considered to enhance localization accuracy. Specifically, a novel relative measurement representation called modified range difference (MRD) is firstly designed to mitigate the measurement errors jointly caused by the above two factors, which can be obtained with every two different underwater acoustic communications and further corrected with sound speed profiles. Corresponding to MRD, an extended state is also designed to explore the use of historical position information. As a result, an improved cubature Kalman filter is proposed, which can tackle the nonlinearity of MRD and the commonly unknown measurement variance. Consequently, the proposed method can provide higher localization accuracy by mitigating the combined effects of asynchronization and acoustic wave bends without synchronization operations and anchor assistances, which delivers superior adaptability for anchor-free scenarios. Simulations verify that the proposed method outperforms state-of-the-art ones in terms of localization accuracy.

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.

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.

Industrial Internet of Things (IIoT)-based control is growing rapidly, such as smart factories and industrial automation. In practice, imperfect wireless networks and time delay caused by delayed completion of computing tasks in IIoT may deteriorate the control performance. To enhance the performance of the control system, a multidomain resource allocation problem is formulated by co-designing control, communication, and computation resources, which is a mixed-integer nonlinear programming (MINLP) problem. In this article, a bilevel optimization framework is proposed to solve the MINLP, in which the sharing decision is derived in the upper level, and then, the optimal allocation of multidomain resources is derived in the lower level. A control-aware distributed bilevel (CADB) algorithm is developed, where these two levels interact with each other. In each round, the upper level optimization problem is updated based on the last resource allocation and solved by a primal-decomposition algorithm with provable finite-time feasibility. Then, according to the newly derived sharing decision, the lower level optimization problem is solved by the proposed mixed proximal-gradient-tracking algorithm. It is shown that CADB algorithm enables control systems to achieve enhanced control performance and energy consumption. Finally, simulations are conducted to verify the effectiveness of the proposed algorithm.

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.

Traditional compressive sensing (CS) methods assume the data sparsity to be constant over time, which holds well in many long‐term scenarios. However, the authors’ recent study on electrocardiogram (ECG) monitoring reveals that data sparsity varies dramatically for real‐time monitoring systems where the data latency must be bounded, due to limited data collected within the delay bound. The variation of data sparsity makes the reconstruction error (RE) unstable. Furthermore, the variation of wireless channel quality also impacts the reconstruction quality. To accommodate both variations, this study proposes a novel adaptive feedback architecture for real‐time wireless ECG monitoring based on the CS technique, which can bound the REs in the presence of the variations of data sparsity and wireless channel. An experiment testbed has been built to evaluate the performance of the proposed system. The results show that the data latency can be limited to <300 ms and the RE can be controlled to 9%.

In this demo, an efficient and reliable wireless link is designed for mobile video surveillance systems. In the link, the idea of cognitive radio is utilized and an adaptive channel switching mechanism is employed to avoid unpredictable interferences. Packets pipelining and accumulative acknowledgement (ACK) is proposed based on the stop-and-wait ARQ protocol to improve the communication efficiency of the link. Moreover, an integration design of the ACK packet is used to piggyback different kinds of control messages. With the efficient and reliable wireless link, a cognitive radio prototype is developed for video transmission between the telerobot and teleoperator. The video information from the telerobot can be transmitted back to the teleoperator quickly and reliably even under channel interferences. The telerobot can also be controlled timely and accurately. A video demo shows the whole story and performance.