Optimal Deployment for Mobile Target Tracking in Sensor Networks

2012 ◽  
Vol 468-471 ◽  
pp. 1657-1660
Author(s):  
Ying Chi Mao
Keyword(s):  
Target Tracking ◽  
Tracking Performance ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Surveillance Systems ◽  
Challenging Problem ◽  
Irregular Pattern ◽  
Optimal Deployment ◽  
Mobile Target ◽  
Simulation Results

Mobile target tracking is a key application of wireless sensor network-based surveillance systems. Sensor deployment is an important factor in tracking performance and remains a challenging problem. In this paper, we address the problem of optimal sensor deployment for mobile target tracking. We analyze the tracking performance of three patterns. Simulation results demonstrate that the irregular pattern outperforms the other two patterns.

scholarly journals A Sensor Deployment Approach Using Improved Virtual Force Algorithm Based on Area Intensity for Multisensor Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Jiahao Xie ◽  
Daozhi Wei ◽  
Shucai Huang ◽  
Xiangwei Bu
Keyword(s):  
Sensor Deployment ◽  
Local Optimum ◽  
Detection Model ◽  
Coverage Optimization ◽  
Virtual Force ◽  
Global Coverage ◽  
Optimal Deployment ◽  
Coverage Quality ◽  
Simulation Results ◽  
Sensor Detection

Sensor deployment is one of the major concerns in multisensor networks. This paper proposes a sensor deployment approach using improved virtual force algorithm based on area intensity for multisensor networks to realize the optimal deployment of multisensor and obtain better coverage effect. Due to the real-time sensor detection model, the algorithm uses the intensity of sensor area to select the optimal deployment distance. In order to verify the effectiveness of this algorithm to improve coverage quality, VFA and PSOA are selected for comparative analysis. The simulation results show that the algorithm can achieve global coverage optimization better and improve the performance of virtual force algorithm. It avoids the unstable coverage caused by the large amount of computation, slow convergence speed, and easily falling into local optimum, which provides a new idea for multisensor deployment.

scholarly journals Variational Bayesian Based Adaptive Shifted Rayleigh Filter for Bearings-Only Tracking in Clutters

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1512 ◽  
Author(s):  
Jing Hou ◽  
Yan Yang ◽  
Tian Gao
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Tracking Performance ◽  
Computation Complexity ◽  
Variational Bayesian ◽  
Target State ◽  
Scenarios Simulation ◽  
Simulation Results

This paper considers bearings-only target tracking in clutters with uncertain clutter probability. The traditional shifted Rayleigh filter (SRF), which assumes known clutter probability, may have degraded performance in challenging scenarios. To improve the tracking performance, a variational Bayesian-based adaptive shifted Rayleigh filter (VB-SRF) is proposed in this paper. The target state and the clutter probability are jointly estimated to account for the uncertainty in clutter probability. Performance of the proposed filter is evaluated by comparing with SRF and the probability data association (PDA)-based filters in two scenarios. Simulation results show that the proposed VB-SRF algorithm outperforms the traditional SRF and PDA-based filters especially in complex adverse scenarios in terms of track continuity, track accuracy and robustness with a little higher computation complexity.

Grid-based Mobile Target Tracking Mechanism in Wireless Sensor Networks

2010 ◽  
Vol 5 (6) ◽  
Author(s):  
Kuo-Feng Huang ◽  
Jui-Fa Chen ◽  
Ying-Hong Wang ◽  
Ting-Wei Chang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Target Tracking ◽  
Wireless Sensor ◽  
Mobile Target ◽  
Grid Based

scholarly journals A Multi-Target Tracking and Detection Algorithm for Wireless Sensor Networks

2021 ◽  
Vol 15 ◽  
pp. 661-665
Author(s):  
Gang Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Target Tracking ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
Main Function ◽  
Multiple Target ◽  
Target State ◽  
Multi Target Tracking ◽  
Simulation Results

There are a large number of sensor nodes in wireless sensor network, whose main function is to process data scientifically, so that it can better sense and cooperate. In the network coverage, it can comprehensively collect the main information of the monitoring object, and send the monitoring data through short-range wireless communication to the gateway. Although there are many applications in WSNs, a multi-Target tracking and detection algorithm and the optimization problem of the wireless sensor networks are discussed in this paper. It can be obviously seen from the simulation results that this node cooperative program using particle CBMeMBer filtering algorithm can perfectly handle multi-target tracking, even if the sensor model is seriously nonlinear. Simulation results show that the tracking - forecasting data association scheme applying GM-CBMeMBer, which is proposed in this paper, runs well in identifying multiple target state, and can improve the estimation accuracy of multiple target state.

Object Tracking Algorithms in Wireless Sensor Networks

2015 ◽  
pp. 287-320
Author(s):  
Maryam Sadat Mirsadeghi ◽  
Ali Mahani
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Object Tracking ◽  
Target Tracking ◽  
Energy Supply ◽  
High Accuracy ◽  
Wireless Sensor ◽  
Mobile Object ◽  
Mobile Target

Mobile target tracking is one of the most important applications of wireless sensor networks (WSNs). But, the use of sensor networks for object tracking faces a number of issues in which the limited energy supply is the most important. So in target tracking problem, using methods to decrease the energy consumption as well as high accuracy and quality of tracking is the main goal. Hence, reducing the number of participant nodes in tracking phase, increasing the sleep duration of noninvolved nodes and decreasing the number of transmitted packets to the sink are the most referred methods. In this chapter the authors introduce the most suitable methods for energy efficient mobile object tracking.

Self Recurrent Neural Network Based Target Tracking in Wireless Sensor Network using State Observer

2019 ◽  
Vol 9 (2) ◽  
pp. 165-178
Author(s):  
Satish R. Jondhale ◽  
Rajkumar S. Deshpande
Keyword(s):  
Neural Network ◽  
System Dynamics ◽  
Target Tracking ◽  
Recurrent Neural Network ◽  
Simulation Analysis ◽  
State Observer ◽  
Point Of View ◽  
Wireless Sensor ◽  
Accurate Estimation ◽  
Mobile Target

Background & Objective: Mobile target tracking based on data from wireless sensor networks (WSN) is a hot topic that has been investigated both from a theoretical and practical point of view in the literature. Tracking the position and velocity of a target moving in WSN (especially in the context of uncertain noisy measurement channel) is a very challenging task. To deal with the uncertainty in system dynamics as well as uncertainty in target states, an Observer Based Self Recurrent Neural Network (OBSRNN) is proposed in this paper. Methods: The proposed algorithm employs a state observer based tracking control strategy and thereby allows for accurate estimation of mobile target moving along a predefined route in WSN. The Self Recurrent Neural Network (SRNN) framework is used to approximate the uncertainty in the system dynamics, while a full-order state observer is used to estimate the unknown state vector. Conclusion: The simulation analysis is performed to evaluate the efficacy of the proposed work.

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