Abstract-In this paper, we investigate the problem of nonmyopic (multi-step ahead) quantizer design for target tracking using a wireless sensor network. Adopting the alternative conditional posterior Cramér-Rao lower bound (A-CPCRLB) as the optimization metric, we theoretically show that this problem can be temporally decomposed over a certain time window. Based on sequential Monte-Carlo methods for tracking, i.e., particle filters, we design the local quantizer adaptively by solving a particlebased non-linear optimization problem which is well suited for the use of interior-point algorithm and easily embedded in the filtering process. Simulation results are provided to illustrate the effectiveness of our proposed approach.
Liu et al. REF adopted the alternative conditional posterior Cramer-Rao lower bound as the optimization metric and designed the local quantizer adaptively by solving a particle-based non-linear optimization problem.
Adaptive non-myopic quantizer design for target tracking in wireless sensor networks
2013 Asilomar Conference on Signals, Systems and Computers
2013 Asilomar Conference on Signals, Systems and Computers
Mathematics Computer Science