Simultaneous localization and mapping (SLAM) has emerged as a promising solution to address the localization challenges faced by underwater robots. This work proposes a factor graph optimization-based Visual-Inertial-Doppler Velocity Log (DVL) tightly-coupled SLAM method designed for underwater robot localization. The approach introduces a novel DVL residual construction method to maximize the utility of DVL measurements. IMU data is integrated to detect anomalies in DVL measurements, and a DVL synchronization marking strategy is developed to enhance the method’s applicability and strengthen data associations. To improve system robustness in scenarios where visual tracking fails, a new sliding window strategy is incorporated. Experimental results on underwater datasets and simulations demonstrate that the proposed method achieves significant improvements in localization accuracy and robustness compared to existing underwater localization algorithms. The implementation code of the proposed method and a simulated underwater dataset can be accessed at https://github. com/uestc-icsp/VID-SLAM.

Infrared small target detection (ISTD) boasts extensive applications across civil and military domains, owing to its exceptional all-day performance. Neural network innovations have led to deep ISTD models that achieve heightened accuracy through extensive datasets. However, these general networks often fail to perceive the sensitivity of small targets and adopt heavy constructions to preserve potential target features, neglecting domain-specific insights and suffering from poor explainability. Our work seeks to rectify this by revisiting the saliency principles inherent to ISTD and developing a learnable local saliency kernel network (L2SKNet). This approach implements a learnable local saliency kernel module (LLSKM) that embodies the concept of “Center subtracts Neighbors,” guiding the network to capture the saliency features (points or edges). We enhance LLSKM by incorporating strategic dilation and structuring it hierarchically, which boosts its capability to capture multiscale infrared features while avoiding parameter explosion. In pursuit of efficiency, we also refine LLSKM into a more compact form by factorizing it into two orthogonal 1-D kernels, yielding a lightweight version. Heatmap visualizations and rigorous quantitative analyses corroborate the effectiveness of our local saliency-guided networks. Comprehensive testing reveals that L2SKNet variants outperform established baselines, demonstrating significant improvements in both visual and numerical assessments. The code is available athttps://github.com/fengyiwu98/L2SKNet.

High-resolution remote sensing image change detection focuses on ground surface changes. It has wide applications, including territorial spatial planning, urban region detection, and military operations. However, class imbalance and pseudo-changes are caused by the unchanged areas far outnumbering the changed areas and lighting changes. To address these problems, we propose spatial-temporal attention with a difference enhancement-based network (STADE-CDNet). In STADE-CDNet, a change detection difference enhancement module (CDDM) is proposed to extract important features from the difference map to detect changed regions. This module enhances the network with differential feature attributes through the training layer, improving the network’s learning ability and reducing the imbalance problem. A temporal memory module (TMM) is designed to extract temporal and spatial information. Inspired by the self-attention mechanism of the transformer, we propose a transformer and TMM (TTMM). Four encoding layers are designed to detect the semantic information from high to low levels of the multitemporal image pairs. The fusion and parallelism of multivariate data are achieved through collaborative modeling of deep learning and change detection, compensating for the need for excessive human intervention in traditional algorithms. We evaluate our approach in two different datasets (LEVIR-CD and DSIFN-CD). Promising quantitative and qualitative results show that STADE-CDNet can improve accuracy. In particular, the proposed CDDM significantly reduces false positive detection, with F1 scores at least 1.97% and 2.1% higher than other methods in the case of the LEVIR-CD and DSIFN-CD datasets, respectively. Our code is available at https://github.com/LiLisaZhi/STADE-CDNet.

Remote sensing image change detection (CD) is used to identify ground surface changes by analyzing multi-temporal images. Scholars have summarized and extended numerous useful methods for high-resolution remote sensing images. Most of the existing approaches use clustering and correction to detect change regions. However, some methods cannot detect change areas accurately due to bountiful clutters and complex information in high-resolution remote sensing images. To overcome these limitations, we propose a new detection approach using image-inverse correction and density peak clustering: Firstly, the multi-temporal images are corrected by the relative geometry and intensity. Then, the positive and inverse phase difference images are calculated.Moreover, the V channel is extracted in the HSV color space. Subsequently, applying image-inverse correction enhances the intensity of change regions and suppresses unchanged regions. Furthermore, density peak clustering is implemented to remove clutters. Finally, the processed positive-phase differential images are fused with the inverse-phase differential images to obtain the final change detection results. This approach establishes an accurate and efficient module to detect the change areas, which can accurately detect the change regions in the multi-temporal remote sensing image. The experiments show that the proposed research has stronger detection accuracy than the other six advanced change detection algorithms.

Infrared cirrus detection is extensively used in military and civil fields, but it poses several challenges to existing methods. These challenges include the complex and diverse shapes of the cirrus clouds, as well as their varying sizes. Additionally, false alarms can be easily triggered by the shadows of cirrus clouds and strong edges. Furthermore, dim and weak cirrus clouds blend into the background and lack distinct features, leading to missed detections. This paper presents an innovative infrared cirrus detection model based on multi-directional graph learning and local fractal feature prior weight mapping to overcome these challenges. Taking into account the structural characteristics of cirrus and background continuity, the proposed method utilizes graph learning enhanced matrix decomposition to separate the cirrus clouds and background. Additionally, to highlight cirrus clouds while suppressing strong edges in the background caused by mountains or rivers, weighted local fractal features are proposed as prior knowledge. To improve the detection of dim and small cirrus clouds as well as accelerate the convergence, a reweighting optimization scheme is proposed. The model is solved using the Alternating Direction Method of Multipliers (ADMM) framework. Extensive experiments demonstrate that the proposed scheme outperforms a variety of classic techniques in terms of detection performance.

Infrared small target detection (ISTD) is vital for civil and military applications. However, existing methods often face challenges in coping with complex scenes, discriminating targets from similar objects, or leveraging temporal information effectively. To tackle these limitations, we offer an innovative approach that exploits the spatio-temporal structure of infrared images. A four-dimensional (4D) infrared tensor is initially constructed from a sequence of infrared images, and decomposed into lower-dimensional tensors using the tensor train (TT) and its extension – tensor ring (TR) techniques. The ISTD problem is then formulated as a sparse plus low-rank decomposition problem, where the sparse part is the target and the low-rank part is the background. We factorize the composed tensors into matrices via TT and TR unfolding approaches, which mitigates the imbalance between different modes containing spatial and temporal information. By constraining the balanced unfolded components with the weighted sum of nuclear norm, we solve the problem using the alternating direction multiplier method (ADMM). Furthermore, we validate models on several datasets and benchmark them with state-of-the-art techniques in detection accuracy and background suppression. Comparison results demonstrate the superiority of our approach over the existing methods. Moreover, the results of an ablation study with three-dimensional (3D) tensor structures show the effectiveness and feasibility of the dimension expansion to 4D.

As a significant component of ocean exploration, underwater localization has attracted extensive attention in both military and civil fields. Due to its low cost and convenience, underwater wireless sensor networks (UWSNs) is favored by related fields. As an important part of the Internet of Things (IoT), it can strengthen the trinity of land, sea, and air. The location of the underwater sensor node is the foundation of the UWSN application and is one of the research hotspots today. Many relevant research scholars have optimized the localization algorithm or introduced new methods to better locate the target nodes, thus promoting the development of related fields. In this article, the challenges of underwater acoustic communication and underwater positioning, the comparison between UWSNs and terrestrial wireless sensor networks (WSNs), the network structure, routing technology, and localization evaluation criteria are all introduced in detail. Moreover, we survey many cutting-edge underwater localization algorithms based on a new taxonomy (i.e., distance measurement, network scale, and anchor utilization). Moreover, these localization algorithms are compared and analyzed from various aspects. Meanwhile, localization discussion and research prospects are illustrated in this article.

The underwater sensor network is currently a hot research field in academia and industry with many underwater applications, such as ocean monitoring, seismic monitoring, environment monitoring, and seabed exploration. Underwater target tracking is a critical component of ocean development. This paper studies the underwater target tracking problem of the wireless sensor network. The core technology of the target tracking algorithm is the filtering algorithm, which identifies the accuracy of the target tracking system. Nonlinear filtering is a hot issue in target tracking because feasible projects are mostly non-linear systems. The linearization method used in traditional Kalman filtering has serious shortcomings. Therefore, this paper presents the improved cubature Kalman filtering (ICKF) algorithm for underwater target tracking. There is uncertainty in the target movement, an adaptive forgetting factor is given into the cubature Kalman filtering algorithm to directly modify the error covariance to reduce the impact of uncertainties. Then, interactive multi-model technology is introduced to establish the IMMICKF algorithm with multiple states. Compared with other filtering algorithms, the new algorithm can effectively deal with non-linear target tracking problems and obtain better estimation accuracy. The numerical simulation is given to demonstrate the effectiveness of the IMMICKF algorithm.

There are two main classes of decision fusion methods, namely hard decision fusion (HD) and soft decision fusion (SD), in which the number of bits transmitted by each local sensor to the fusion centre (FC) is always same, namely one bit in HD and n ( n ≥ 2) bits in SD. However, considering that there is always a limit of bandwidth in a distributed detection system, the number of bits sent by each local sensor to the FC does not need to be the same and should be allocated reasonably and suitably. Therefore, this study proposes an optimal bit allocation scheme based on the memetic algorithm, in which the number of bits transmitted by each local sensor could be different. This scheme aims to maximise the detection probability under the limit of bandwidth for a detection system with imperfect channels. The overall detection probability objective function about the number of allocated bits is derived. To optimise this objective function, an improved memetic algorithm with two local adjustment strategies, namely non‐elite learning local adjustment optimisation strategy and elite greedy local adjustment optimisation strategy, is proposed to allocate the optimal number of bits. Simulation results show the efficiency and effectiveness of the proposed scheme.

The effective node depth adjustment of distance-measuring sensors for target tracking in underwater wireless sensor networks (UWSNs) is investigated in this paper. Due to the limited energy and bandwidth in UWSNs, there is only a part of sensors participating in the tracking task. In this paper, the mobility of sensor nodes in depth is utilized to improve the tracking accuracy. Firstly, considering the complexity of the underwater environment, the measurement error is formulated as addictive and multiplicative noise. Secondly, the relationship between the depth of sensor nodes and the Fisher information matrix (FIM) is derived and taken as the metric for tracking accuracy. Thirdly, the optimal depth adjustment is determined for active sensors with low complexity by simplifying the objective function. Finally, by combining the optimal depth adjustment and traditional sensor selection algorithm, the best task sensors are selected for the purpose of the energy-efficiency. The simulation results illustrate the performance of the proposed method for improving the tracking accuracy and computational efficiency on the premise of employing the same number of sensors.

Multi-sensor fusion has been extensively studied in information fusion field, and the distributed target detection is one of the most important applications in the multiple sensor detection theories. In this paper, a data fusion algorithm for target detection is proposed based on tree topology combined with the orderly full binary tree and we discuss the optimal threshold fusion rule problem. Different from the conventional tree topology, the sensors in our topology are well ordered and the sensors with highest signal amplitude are selected as the fusion center. Moreover, we derive a fusion decision rule which takes the channel noise into consideration based on this topology. By introducing the probability of error, we prove that with equal prior probability there is a concave function of the likelihood ratio threshold used in the sensor decision rule. In the case of minimizing the probability of error, the optimal threshold of each level can be obtained. Finally, the distributed detection performance of tree topology is analyzed and comparisons with other topologies are drawn. These results show that the detection performance improves as the level decreases and our fusion rule can achieve a higher probability of detection.

Mobile ad-hoc networks (MANETs) are based on cooperative and trust characteristic of mobile nodes. Typically, nodes are both autonomous and self-organized without requiring a central administration or a fixed network infrastructure. Due to their distributed nature, MANETs are very vulnerable to various attacks. To enhance the security of MANETs, it is important to rate the trustworthiness of other nodes without central authorities to build up a trust environment. In this paper, we propose a fuzzy trust recommendation based on collaborative filtering, which stimulates collaboration among distributed computing and communicating nodes, facilitates the detection of untrustworthy nodes, and assists decision-making in various protocols for MANETs. Due to the uncertain interaction outcomes, we use fuzzy logic to model trust recommendation in a MANET environment. Our trust model combines direct trust and trust recommendation information based on collaborative filtering to allow nodes to represent and reason with uncertainty and imprecise information regarding other nodespsila trustworthiness. Simulation results show that the proposed model is flexible and valid.