Paper Info
Title
Sensor Selection for Decentralized Large-Scale Multi-Target Tracking Network.
Abstract
A new optimization algorithm of sensor selection is proposed in this paper for decentralized large-scale multi-target tracking (MTT) network within a labeled random finite set (RFS) framework. The method is performed based on a marginalized delta-generalized labeled multi-Bernoulli RFS. The rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. A new metric, named as the label assignment (LA) metric, is proposed to measure the distance for two labeled sets. The lower bound of LA metric based mean square error between the labeled multi-target state set and its estimate is taken as the optimized objective function of sensor selection. The proposed bound is obtained by the information inequality to RFS measurement. Then, we present the sequential Monte Carlo and Gaussian mixture implementations for the bound. Another advantage of the bound is that it provides a basis for setting the weights of KLA. The coordinate descent method is proposed to compromise the computational cost of sensor selection and the accuracy of MTT. Simulations verify the effectiveness of our method under different signal-to-noise ratio scenarios.
Year
DOI
Venue
2018
10.3390/s18124115
SENSORS
Keywords
Field
DocType
sensor selection,multi-target tracking,labeled random finite set,decentralized sensor network,error bound
Multi target tracking,Real-time computing,Electronic engineering,Engineering,Sensor selection
Journal
Volume
Issue
ISSN
18
12.0
1424-8220
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Feng Lian1133.83
Hou LiMing222.06
bo wei35814.91
Chongzhao Han444671.68