Target Tracking In Sensor Networks Research Articles

Sensor Placement Optimization of Visual Sensor Networks for Target Tracking Based on Multi-Objective Constraints

With the advancement of sensor technology, distributed processing technology, and wireless communication, Visual Sensor Networks (VSNs) are widely used. However, VSNs also have flaws such as poor data synchronization, limited node resources, and complicated node management. Thus, this paper proposes a sensor placement optimization method to save network resources and facilitate management. First, some necessary models are established, including the sensor model, the space model, the coverage model, and the reconstruction error model, and a dimensionality reduction search method is proposed. Next, following the creation of a multi-objective optimization function to balance reconstruction error and coverage, a clever optimization algorithm that combines the benefits of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) is applied. Finally, comparison studies validate the methodology presented in this paper, and the combined algorithm can enhance optimization effect while relatively reducing running time. In addition, a sensor coverage method for large-range target space with obstacles is discussed.

Coordinated target tracking in sensor networks by maximizing mutual information

Abstract This paper addresses the problem of coordinated target tracking in sensor networks. For a typical target tracking scene with nonlinear bearing-only measurements, we first investigate the mutual information between multiple sensors and the target state. To improve the performance of target tracking, we analyzed the relative positions between sensor agents and the target to be tracked and derived the optimal positions for sensors in the network by mutual information maximization. Simulation results are presented and discussed to demonstrate that the performance of estimated target states could be improved by the proposed method.

Information space of sensor networks: Lagrangian, energy-momentum tensor, and applications

It is a challenge to investigate the interrelationship between the geometric structure and performance of sensor networks due to the increasingly complex and diverse architecture of them. This paper presents two new formulations for the information space of sensor networks, including Lagrangian and energy–momentum tensor, which are expected to integrate sensor networks target tracking and performance evaluation from a unified perspective. The proposed method presents two geometric objects to represent the dynamic state and manifold structure of the information space of sensor networks. Based on that, the authors conduct the property analysis and target tracking of sensor networks. To the best of our knowledge, it is the first time to investigate and analyze the information energy–momentum tensor of sensor networks and evaluate the performance of sensor networks in the context of target tracking. Simulations and examples confirm the competitive performance of the proposed method.

Intelligent Sensing and Computing in Wireless Sensor Networks for Multiple Target Tracking

With sixth generation (6G) communication technologies, target sensing can be finished in milliseconds. The mobile tracking-oriented Internet of Things (MTT-IoT) as a kind of emerging application network can detect sensor nodes and track targets within their sensing ranges cooperatively. Nevertheless, huge data processing and low latency demands put tremendous pressure on the conventional architecture where sensing data is executed in the remote cloud and the short transmission distance of 6G channels presents new challenges into the design of network topology. To cope with the above difficulties, this paper proposes a new resource allocation scheme to perform delicate node scheduling and accurate tracking in multitarget tracking mobile networks. The dynamic tracking problem is formulated as an infinite horizon Markov Decision Process (MDP), where the state space that considers energy consumption, system responding delay, and target important degree is extended. A model-free reinforcement learning is applied to obtain satisfied tracking actions by frequent iterations, in which smart agents interact with the complicated environment directly. The performance of each episode is evaluated by the action-value function in search of the optimal reward. Simulation results demonstrate that the proposed scheme shows excellent tracking performance in terms of energy cost and tracking delay.

Distributed Target Tracking in Sensor Networks by Consistency Algorithm and Semantic Moving Computing of Internet of Things

The target tracking algorithm of mobile wireless sensor networks involves target motion trend prediction and subsequent node guidance. This study aims to solve the problems of global consistency of node information and significant errors in forecasting fast-moving targets’ trajectories through traditional distributed tracking methods in sensor networks. Initially, the average consistency algorithm is used to average the local measurements of each node to achieve global consistency. Then, semantic moving computing of the Internet of Things calculates and analyzes the node movement to support the subsequent movement guidance of nodes and target movement prediction. Finally, the simulation experiment is carried out to evaluate the commonly used target trajectory prediction model. The simulation results show that the node movement algorithm by average consistency can effectively improve the positioning accuracy of the network for moving targets. Besides, the positioning error decreases with the increase of the sensing radius R, the number of moving nodes nm, and the total number of nodes ns deployed in a particular range in a two-dimensional (2D) space. The positioning error after node movement in 2D space is about 20%–30%R lower than that in a static state. After node movement in a three-dimensional (3D) space, the positioning error is about 40%–50%R lower than in a dormant state. When the target moves at a speed greater than 7m/s, the consistency-based moving computing algorithm’s target loss rate and tracking errors are about 0~10% and 1.5%~2% lower than the target tracking algorithm via Kalman Filter. Therefore, the algorithm reported here can precisely track the high-speed moving target. The existing research on point target tracking has problems of insufficient accuracy and robustness. The algorithm proposed here has stronger robustness, reduced data error in multi-node, and more flexible node movements, providing a reference for the subsequent research on distributed point target tracking.

Foreground-aware Siamese tracker with dynamic template in wireless sensor networks

Accuracy and reliability are the main problems to be considered when using wireless sensor networks for target tracking. Deep learning has a good prospect in visual object tracking, in which Siamese based trackers achieve a balance between performance and computational efficiency.Template adaptability and feature expression of the target are crucial for Siamese trackers which aim to locate the target by learning a decision-making based similarity evaluation. However, the fixed template can hardly handle the severe target variation and the background information in the complex scene will also interfere the similarity evaluation learning process.In these work, we propose a foreground-aware Siamese tracker with dynamic template to tackle these issues. Firstly, we construct a local effective template feature attention network to guide the template updating according to context tracking information. Then the similarity attention update mask is generated to enhance the discriminative ability of tracker to adapt to complex tracking task. Further, we propose a foreground-aware channel selection module which let the tracker pay more attention to the foreground semantic feature channel in the target search area, thus our tracker can suppress the tracking drift of Siamese trackers in the background clutter scene. Extensive experiments demonstrate that our tracker can significantly improve the performance of our baseline and performs favorably against many state-of-the-art trackers.

A consensus-based square-root cubature Kalman filter for manoeuvring target tracking in sensor networks

In this paper, a novel distributed tracking method is proposed for the problem of manoeuvring target tracking in sensor networks. Firstly, an adaptive adjustment tracking model is established by extended state observer (ESO) theory. Then, the consensus-based square-root cubature Kalman filter (SCKF) algorithm is proposed in order to improve the global accuracy and stability. In addition, the integrated model could reduce the influence of measurement noise. Finally, simulation is performed to verify the effectiveness of the scheme, whereby comparison results show that the estimation accuracy of the method proposed is higher than that of the traditional ESO and SCKF.

A new energy efficient management approach for wireless sensor networks in target tracking

This paper mainly studied the problem of energy conserving in wireless sensor networks for target tracking in defensing combats. Firstly, the structures of wireless sensor nodes and networks were illustrated; Secondly, the analysis of existing energy consuming in the sensing layer and its calculation method were provided to build the energy conserving objective function; What’s more, the other two indicators in target tracking, including target detection probability and tracking accuracy, were combined to be regarded as the constraints of the energy conserving objective function. Fourthly, the three energy conserving approaches, containing optimizing the management scheme, prolonging the time interval between two adjacent observations, and transmitting the observations selectively, were introduced; In addition, the improved lion algorithm combined with the Logistic chaos sequence was proposed to obtain sensor management schemes. Finally, simulations had been made to prove the effectiveness of the proposed methods and algorithm.

A Sleep Scheduling Target Tracking Research for Sensor Networks

Moving target tracking of sensor networks is an important application. The sensor nodes cooperate in the networks to discover the target, and the target’s perceptual information is feeded back to the user. In order to effectively reduce the network energy consumption and improve the tracking quality, a target tracking protocol is proposed based on two-stage sleep scheduling. The whole tracking process is divided into two stages in the protocol. According to the different requirements of node density, the different sleep scheduling mechanism is adopted. In this paper, the proposed protocol is further optimized to ensure the tracking quality while minimizing system energy consumption. Finally, the validity of the proposed protocol was verified with 36 sensor nodes.

Sensor radiation interception risk control in target tracking

This paper is mainly on the problem of radiation interception risk control in sensor network for target tracking. Firstly, the sensor radiation interception risk is defined as the product of the interception probability and the cost caused by the interception. Secondly, the radiation interception probability model and cost model are established, based on which the calculation method of interception risk can be obtained. Thirdly, a sensor scheduling model of radiation risk control is established, taking the minimum interception risk as the objective function. Then the Hungarian algorithm is proposed to obtain sensor scheduling scheme. Finally, simulation experiments are mad to prove the effectiveness of the methods proposed in this paper, which shows that compared with the sensor radiation interception probability control method, the interception risk control method can keep the sensor scheduling scheme in low risk as well as protect sensors of importance in the sensor network.

An improved CI EKF data fusion algorithm for multi-sensor time-delay system

In order to solve the problem of time-delay in non-linear multi-sensor information fusion, a simulation model of moving target tracking in sensor networks with the time-delay state and observation system is established. Using the augmented matrix to transform the time-delay system into the non-time-delay system, an improved Covariance Intersection (CI) Extended Kalman filter (EKF) data fusion algorithm for multi-sensor systems with time-delay is presented. This method avoids calculating any two local filter error cross-covariance matrices and greatly reduces the computational complexity and time. Analysing the precision of this method, comparing the precision of improved CI EKF data fusion algorithm and the locally optimal fusion EKF algorithm. The results show that the precision of improved CI EKF data fusion algorithm is higher than that of the local EKF and close to the precision of the optimal EKF fusion.

A Novel Approach to Reliable Sensor Selection and Target Tracking in Sensor Networks

This paper addresses the problem of sensor selection in a sensor network for tracking a moving target. By considering network uncertainties and unpredictable movements of the target, reliable sensor selection approaches such as sigma points probability and target trajectory are proposed. An updated unscented Kalman filter is proposed to achieve effective tracking of the target through the sensor selection. A multialgorithm genetically adaptive multiobjective is utilized to have a selection strategy without knowing the number of sensors to be selected. Extensive experiments are conducted to evaluate the effectiveness of the proposed approach both in simulation and practical experimentation. The proposed algorithm is also tested in the industrial setting where providing safety is of great importance for a human worker who walks in a potentially dangerous workplace. The results confirm the effectiveness and utility of the proposed scheme.

A type of energy-efficient target tracking approach based on grids in sensor networks

To enhance the reliability as well as the value of sensing data in Wireless Sensor Networks (WSNs), a type of Energy-efficient Target Tracking Approach (ETTA) is proposed in this paper. The sensor network is divided into several virtual grids for distributed tracking and three kinds of states (tracking state, prepared-tracking state and preparing-tracking state) of these grids are also proposed to reduce energy consumption and enhance the accuracy of node localization. Moreover, a tracking recovery strategy is also described in this paper that effectively enhance the robustness of the tracking system. Experiment results show that ETTA has a good performance on target tracking in sensor networks compared to BPS and EMTT.

Semi-Flocking-Controlled Mobile Sensor Networks for Dynamic Area Coverage and Multiple Target Tracking

Mobile sensor networks (MSNs) can provide sensing coverage to large areas of interest. Area coverage and target tracking capabilities of MSNs are heavily depending on their motion control and coordination mechanisms. Many existing MSN motion control algorithms ignore or poorly utilize the available information from their operating environment, thus lead to unsatisfactory monitoring performances. This paper proposes a fully distributed semi-flocking algorithm which enables mobile nodes to self-organize themselves based on mobility and sensing information via information exchanges among nearby nodes. A distributed mechanism is designed to maximize area coverage and target tracking performances of MSNs. Mobile nodes perform evaluations based on received information and switch between searching and tracking modes. Behaviors of MSNs controlled by the proposed algorithm are studied under different levels of information exchanges. Our study shows that the proposed semi-flocking algorithm is capable of delivering desirable area coverage and target tracking performances in MSNs.

Distributed joint sensor registration and target tracking via sensor network

This paper deals with distributed registration of a sensor network for target tracking in the presence of false and/or missed measurements. Each sensor acquires measurements of the target position in local coordinates, having no knowledge about the relative positions (referred to as drift parameters) of its neighboring nodes. A distributed Bernoulli filter is run over the network to compute in each node a local posterior target density. Then a suitable cost function, expressing the discrepancy between the local posteriors in terms of averaged Kullback–Leibler divergence, is minimized with respect to the drift parameters for sensor registration purposes. In this way, a computationally feasible optimization approach for joint sensor registration and target tracking is devised. Finally, the effectiveness of the proposed approach is demonstrated through simulation experiments on both tree networks and networks with cycles, as well as with both linear and nonlinear sensors.

Distributed target tracking in sensor networks using multi‐step consensus

The authors propose a new algorithm for distributed target tracking by sensor networks with sensors having limited sensing range (radars, sonars, cameras), using an adaptive multi-step consensus scheme. The proposed distributed adaptation strategy represents the core element of the algorithm, providing: (i) asymptotic consensus gains giving the desired importance to the nodes observing the target, and (ii) fast convergence rate of the consensus scheme, enabling efficient implementation. Another contribution of this study is a theoretical study of the algorithm properties, including asymptotic stability and reduction of noise influence. The given simulation results show that the proposed algorithm outperforms the existing algorithms, preserving, at the same time, much lower communication and computation requirements.

Distributed Optimal Control of Sensor Networks for Dynamic Target Tracking

This paper presents a distributed optimal control approach for managing omnidirectional sensor networks deployed to cooperatively track moving targets in a region of interest. Several authors have shown that under proper assumptions, the performance of mobile sensors is a function of the sensor distribution. In particular, the probability of cooperative track detection, also known as track coverage, can be shown to be an integral function of a probability density function representing the macroscopic sensor network state. Thus, a mobile sensor network deployed to detect moving targets can be viewed as a multiscale dynamical system in which a time-varying probability density function can be identified as a restriction operator, and optimized subject to macroscopic dynamics represented by the advection equation. Simulation results show that the distributed control approach is capable of planning the motion of hundreds of cooperative sensors, such that their effectiveness is significantly increased compared to that of existing uniform, grid, random, and stochastic gradient methods.

Fuzzy logic based high speed data transmission algorithm of sensor networks for target tracking

Fuzzy logic based high speed data transmission algorithm of sensor networks for target tracking

EM-Based High Speed Wireless Sensor Networks for Underwater Surveillance and Target Tracking

Underwater Wireless Sensor Networks (UWSNs) are considered as tangible, low cost solution for underwater surveillance and exploration. Existing acoustic wave-based UWSN systems fail to meet the growing demand for fast data rates required in military operations, oil/gas exploration, and oceanographic data collection. Electromagnetic (EM) wave-based communication systems, on the other hand, have great potential for providing high speed data rates in such scenarios. This paper will(1)discuss the challenges faced in the utilization of EM waves for the design of tactical underwater surveillance systems and(2)evaluate several EM wave-based three-dimensional (3D) UWSN architectures differing in topologies and/or operation principles on the performance of localization and target tracking. To the best of our knowledge, this is the first of its kind in the field of underwater communications where underwater surveillance techniques for EM wave-based high speed UWSNs have been investigated. Thus, this will be a major step towards achieving future high speed UWSNs.

Dynamic Cluster Members Scheduling for Target Tracking in Sensor Networks

The sensor scheduling for energy-efficient target tracking with high performance in wireless sensor networks (WSNs) is a dilemma problem. By analyzing the intrinsic relationship between tracking performance and energy consumption, we cast the scheduling problem of WSN as the optimal policy problem of partially observable Markov decision process (POMDP), and propose a dynamic cluster members scheduling (DCMS) algorithm to solve the tradeoff between tracking performance and energy consumption. First, we exploit an election method, based on the optimal mixed weights of the signal strength and the residual energy of node, to choose the cluster head node. Then, we seem each cluster members an agent, and model the scheduling problem of cluster members by POMDP. At last, a point-based online value iteration algorithm is presented to solve the DCMS to generate the collaboration strategy of sensor cluster members dynamically. The simulation results show that the proposed approach can improve the accuracy of target tracking, decrease the energy consumption of sensor nodes, and prolong the lifetime of sensor networks.