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Accurate target identification of UAV (Unmanned Aerial Vehicle)-captured images is a prerequisite for maritime rescue and maritime surveillance. However, UAV-captured images pose several challenges, such as complex maritime backgrounds, tiny targets, and crowded scenes. To reduce the impact of these challenges on target recognition, we propose an efficient maritime rescue network (EMR-YOLO) for recognizing images captured by UAVs. In the proposed network, the DRC2f (Dilated Reparam-based Channel-to-Pixel) module is first designed by the Dilated Reparam Block to effectively increase the receptive field, reduce the number of parameters, and improve feature extraction capability. Then, the ADOWN downsampling module is used to mitigate fine-grained information loss, thereby improving the efficiency and performance of the model. Finally, CASPPF (Coordinate Attention-based Spatial Pyramid Pooling Fast) is designed by fusing CA (Coordinate Attention) and SPPF (Spatial Pyramid Pooling Fast), which effectively enhances the feature representation and spatial information integration ability, making the model more accurate and robust when dealing with complex scenes. Experimental results on the AFO dataset show that, compared with the YOLOv8s network, the EMR-YOLO network improves the mAP (mean average precision) and mAP50 by 4.7% and 9.2%, respectively, while reducing the number of parameters and computation by 22.5% and 18.7%, respectively. Overall, the use of UAVs to capture images and deep learning for maritime target recognition for maritime rescue and surveillance improves rescue efficiency and safety. [ABSTRACT FROM AUTHOR]

Poisson multi‐Bernoulli Mixture (PMBM) filter has been known as an available or practical point and multiple extended target tracking (METT) method. The authors present an improved PMBM filter with adaptive detection probability and adaptive newborn distributions, accompanying with an associated distributed fusion strategy for the tracking extended multiple targets. First, the augmented state of unknown and changing target detection probability is assumed as Gamma (GAM) distribution. Second, extended states are described by Inverse Wishart (IW) distribution based on this augmented state, accompanying with dynamic states presented by Gaussian distribution. And then, an adaptive newborn distribution is adopted to describe the newborn targets appearing arbitrarily. Consequently, the closed‐form solutions of the proposed filter can be derived by approximating the intensity of newborn and potential targets to the Gamma Gaussian Inverse Wishart (GGIW) form. Moreover, the fused means that Generalised Covariance Intersection (GCI) is performed in such a large‐scale aquaculture sensor network. Experiments are presented to verify the availability of the GCI‐GGIW‐PMBM method, and comparisons with other METT filters also demonstrate that tracking behaviours are improved largely. [ABSTRACT FROM AUTHOR]

As a new type of displacement sensor, grating eddy current displacement sensor (GECDS) combines traditional eddy current sensors and grating structure in one. The GECDS performs a wide range displacement measurement without precision reduction. This paper proposes an analytical modeling approach for the GECDS. The solution model is established in the Cartesian coordinate system, and the solving domain is limited to finite extents by using the truncated region eigenfunction expansion method. Based on the second order vector potential, expressions for the electromagnetic field as well as coil impedance related to the displacement can be expressed in closed-form. Theoretical results are then confirmed by experiments, which prove the suitability and effectiveness of the analytical modeling approach.

Localization is crucial to many applications in wireless sensor networks (WSNs) because measurement data or information exchanges happened in WSNs without location information are meaningless. Most localization schemes for mobile WSNs are based on Sequential Monte Carlo (SMC) algorithm. These SMC-based methods often suffer from too many iterations, sample impoverishment and less sample diversity, which leads to low sampling and filtering efficiency, and consequently low localization accuracy and high localization costs. In this paper, we propose an improved range-free localization scheme for mobile WSNs based on improved Population Monte Carlo localization (PMCL) method, accompanying with Hidden Terminal Couple scheme. A population of probability density functions is proposed to approximate the distribution of unknown locations based on a set of observations through an iterative importance sampling procedure. Behaviors are enhanced by adopting three improved methods to increase accuracy, enhance delay and save cost. Firstly, resampling, with importance weights, is introduced in PMCL method to avoid sample degeneracy. Secondly, twofold constraints, constraining the number of random samples in initialized step and constraining valid observations in resampling step, are proposed to decrease the number of iterations. Thirdly, mixture perspective is introduced to maintain the diversity of samples in resampling weighted process. Then, localization error, delay and consumption, especial delay, are predicted based on the statistic point of view, which takes mobile model of RWP into account. Moreover, performance comparisons of PMCL with other SMC-based schemes are also proposed. Simulation results show that delay of PMCL has some superiorities to that of other schemes, and accuracy and energy consumption is improved in some cases of less anchor rate and lower mobile velocity. [ABSTRACT FROM AUTHOR]