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Efficient multiple target tracking (MTT) is the key to achieving green, precision, and large-scale aquaculture, marine exploration, and marine farming. The traditional MTT methods based on Bayes estimation have some pending problems such as an unknown detection probability, random target newborn, complex data associations, and so on, which lead to an inefficient tracking performance. In this work, an efficient two-stage MTT method based on a YOLOv8 detector and SMC-PHD tracker, named EMTT-YOLO, is proposed to enhance the detection probability and then improve the tracking performance. Firstly, the first detection stage, the YOLOv8 model, which adopts several improved modules to improve the detection behaviors, is introduced to detect multiple targets and derive the extracted features such as the bounding box coordination, confidence, and detection probability. Secondly, the particles are built based on the previous detection results, and then the SMC-PHD filter, the second tracking stage, is proposed to track multiple targets. Thirdly, the lightweight data association Hungarian method is introduced to set up the data relevance to derive the trajectories of multiple targets. Moreover, comprehensive experiments are presented to verify the effectiveness of this two-stage tracking method of the EMTT-YOLO. Comparisons with other multiple target detection methods and tracking methods also demonstrate that the detection and tracking behaviors are improved greatly. [ABSTRACT FROM AUTHOR]

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 one of the essential problems in wireless sensor applications (WSNs). Most range‐free localization schemes for mobile WSNs are based on the Sequential Monte Carlo (SMC) algorithm. Multiple iterations, sample impoverishment and less sample diversity, leading to low localizing efficiency, are the most usual problems demanding to be solved in these SMC‐based methods. An improved localization scheme for mobile aquaculture WSNs based on the Improving Dynamic Population Monte Carlo Localization (I‐DPMCL) method is proposed. A population of probability density functions is proposed to approximate the unknown location distribution based on a set of observations through an iterative mixture importance sampling procedure, accompanied by node dynamic behaviours being analysed quantitatively or definitely. Threefold constrain rules are put forward in the I‐DPMCL scheme to decrease the iteration number and trade off iteration number and enough valid samples to obtain the optimum iteration number. Then, these localization behaviours, especial delay, are predicted based on the statistical point of view. Moreover, performance comparisons of I‐DPMCL with other SMC‐based schemes are also proposed. Simulation results show that delay of I‐DPMCL has some superiority to those of other schemes, and accuracy and energy consumption are improved in some cases of lower mobile velocity. [ABSTRACT FROM AUTHOR]

The Poisson multi-Bernoulli Mixture (PMBM) filter, as well as its variants, is a popular and practical multitarget tracking algorithm. There are some pending problems for the standard PMBM filter, such as unknown detection probability, random target newborn distribution, and high energy consumption for limited computational and processing capacity in sensor networks. For the sake of accommodating these existing problems, an improved multitarget tracking method based on a PMBM filter with adaptive detection probability and adaptive newborn distribution is proposed, accompanied by an associated distributed fusion strategy to reduce the computational complexities. Firstly, gamma (GAM) distribution is introduced to present the augmented state of unknown and changing target detection probability. Secondly, the intensity of newborn targets is adaptively derived from the inverse gamma (IG) distribution based on this augmented state. Then, the measurement likelihood is presented as a gamma distribution for the augmented state. On these bases, the detailed recursion and closed-form solutions to the proposed filter are derived by means of approximating the intensity of target birth and potential targets to an inverse gamma Gaussian mixture (IGGM) form and the density of existing Bernoulli components to a single IGGM form. Moreover, the associated distributed fusion strategy generalized covariance intersection (GCI), whose target states are measured by multiple sensors according to their respective fusion weights, is applied to a large-scale aquaculture tracking network. Comprehensive experiments are presented to verify the effectiveness of this IGGM–PMBM method, and comparisons with other multitarget tracking filters also demonstrate that tracking behaviors are largely improved; in particular, tracking energy consumption is reduced sharply, and tracking accuracy is relatively enhanced. [ABSTRACT FROM AUTHOR]

Node localization in two-dimensional (2D) and three-dimensional (3D) space for wireless sensor networks (WSNs) remains a hot research topic. To improve the localization accuracy and applicability, we first propose a quantum annealing bat algorithm (QABA) for node localization in WSNs. QABA incorporates quantum evolution and annealing strategy into the framework of the bat algorithm to improve local and global search capabilities, achieve search balance with the aid of tournament and natural selection, and finally converge to the best optimized value. Additionally, we use trilateral localization and geometric feature principles to design 2D (QABA-2D) and 3D (QABA-3D) node localization algorithms optimized with QABA, respectively. Simulation results show that, compared with other heuristic algorithms, the convergence speed and solution accuracy of QABA are greatly improved, with the highest average error of QABA-2D reduced by 90.35% and the lowest by 17.22%, and the highest average error of QABA-3D reduced by 75.26% and the lowest by 7.79%. [ABSTRACT FROM AUTHOR]

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]

Analytical method for eddy current evaluation is an attractive field of learning the concept of electromagnetic phenomena. This paper proposes an analytical approach to a transverse eddy current displacement sensor with a rectangular testing coil. Solving domain in the Cartesian coordinate system is limited to finite extents through imposing artificial boundaries at x and y axis, and solutions to the electromagnetic field are expressed as double series. Complete closed-form expressions based on second order vector potential for the electromagnetic field in each region can be obtained from the continuity conditions at the interfaces by mode matching. Closed-form expressions of the eddy current density as well as coil impedance related to the transverse displacement can thus be derived, and the calculated results show good agreement with the experimental results. [ABSTRACT FROM AUTHOR]

The hidden‐terminal problem and routing establishment or maintenance bring about a tremendous amount of energy consumption in multi‐hop wireless sensor networks (WSNs). In this study, the authors propose a carrier sense multiple access with collision avoidance (CSMA/CA) scheme MultiCSMA for multi‐hop WSNs adopting two strategies which refer to a novel hidden‐terminal list algorithm to avoid the impact of hidden terminals and a parallel access scheme to dispense with taking the routing overhead into account. The accurate statistical performance metrics of throughput and delay of unsaturated, unacknowledged IEEE 802.15.4 beacon enabled networks for one‐hop and two‐hop scenarios are predicted based on Markov models adopting these two improved strategies, in which nodes are assumed to locate randomly over a circle plane according to Poisson distribution. Comprehensive simulations demonstrate that the analysis results match well with the simulation results, and delay performance of the authors schemes is superior to those of other schemes, whereas throughput is superior to others for relative higher node density. [ABSTRACT FROM AUTHOR]

Studies of the IEEE 802.15.4 Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) scheme have been received considerable attention recently, with most of these studies focusing on homogeneous or saturated traffic. Two novel transmission schemes-OSTS/BSTS (One Service a Time Scheme/Bulk Service a Time Scheme)-are proposed in this paper to improve the behaviors of time-critical buffered networks with heterogeneous unsaturated traffic. First, we propose a model which contains two modified semi-Markov chains and a macro-Markov chain combined with the theory of M/G/1/K queues to evaluate the characteristics of these two improved CSMA/CA schemes, in which traffic arrivals and accessing packets are bestowed with non-preemptive priority over each other, instead of prioritization. Then, throughput, packet delay and energy consumption of unsaturated, unacknowledged IEEE 802.15.4 beacon-enabled networks are predicted based on the overall point of view which takes the dependent interactions of different types of nodes into account. Moreover, performance comparisons of these two schemes with other non-priority schemes are also proposed. Analysis and simulation results show that delay and fairness of our schemes are superior to those of other schemes, while throughput and energy efficiency are superior to others in more heterogeneous situations. Comprehensive simulations demonstrate that the analysis results of these models match well with the simulation results. [ABSTRACT FROM AUTHOR]

This paper presents recent development on magnetostrictive linear position sensors (MLPS). A new compensation coil structure improves the EMI suppression and accuracy considerably. Furthermore, experimental results indicate that the new structure can improve the accuracy to ± 0.13 mm nearly double the ± 0.2 mm obtained with traditional structures. As another design continuation after the differential waveguide structure, this new structure is a practical and reliable implementation technique for the commercialization of MLPS.