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 δ-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.
Highlights
With the development of communication and information fusion technologies, multi-target tracking (MTT) [1] based on sensor network becomes a new research hotspot
The lower bound of label assignment (LA) metric based mean square error (MSE) between the labeled multi-target state set and its estimate is treated as the optimized objective function of sensor selection
The simulation results show that when the sensors in a decentralized large-scale network have different observation performance, 1) the MTT accuracy of our method is much better than that of the Cauchy-Schwarz (CS) divergence based methods [31,32]; 2) compared with the genetic algorithm [38], the coordinate descent method significantly shortens the calculation time of sensor selection; 3) the Gaussian mixture (GM) implementation of the bound is obviously faster than its sequential Monte Carlo (SMC) implementation
Summary
With the development of communication and information fusion technologies, multi-target tracking (MTT) [1] based on sensor network becomes a new research hotspot. The sensor selection is described as a constrained optimization problem with a Bayesian recursion of labeled multi-target RFS. The lower bound of LA metric based mean square error (MSE) between the labeled multi-target state set and its estimate is treated as the optimized objective function of sensor selection. The simulation results show that when the sensors in a decentralized large-scale network have different observation performance, 1) the MTT accuracy of our method is much better than that of the Cauchy-Schwarz (CS) divergence based methods [31,32]; 2) compared with the genetic algorithm [38], the coordinate descent method significantly shortens the calculation time of sensor selection; 3) the GM implementation of the bound is obviously faster than its SMC implementation
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