Multiple Target Counting and Localization Using Variational Bayesian EM Algorithm in Wireless Sensor Networks

2017 ◽  
Vol 65 (7) ◽  
pp. 2985-2998 ◽  
Author(s):  
Baoming Sun ◽  
Yan Guo ◽  
Ning Li ◽  
Dagang Fang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Em Algorithm ◽  
Wireless Sensor ◽  
Multiple Target ◽  
Variational Bayesian

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.

scholarly journals An efficient dictionary refinement algorithm for multiple target counting and localization in wireless sensor networks

2017 ◽  
Vol 13 (8) ◽  
pp. 155014771772380
Author(s):  
Baoming Sun ◽  
Yan Guo ◽  
Gengfa Fang ◽  
Eryk Dutkiewicz
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Compressive Sensing ◽  
State Of The Art ◽  
Region Of Interest ◽  
Wireless Sensor ◽  
Multiple Target ◽  
Reconstruction Algorithms ◽  
Simulation Results ◽  
Refinement Algorithm

Many applications provided by wireless sensor networks rely heavily on the location information of the monitored targets. Since the number of targets in the region of interest is limited, localization benefits from compressive sensing, sampling number can be greatly reduced. Despite many compressive sensing–based localization methods proposed, existing solutions are based on the assumption that all targets fall on a sampled and fixed grid, performing poorly when there are targets deviating from the grid. To address such a problem, in this article, we propose a dictionary refinement algorithm where the grid is iteratively adjusted to alleviate the deviation. In each iteration, the representation coefficient and the grid parameters are updated in turn. After several iterations, the measurements can be sparsely represented by the representation coefficient which indicates the number and locations of multiple targets. Extensive simulation results show that the proposed dictionary refinement algorithm achieves more accurate counting and localization compared to the state-of-the-art compressive sensing reconstruction algorithms.

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