16,629 research outputs found
Adaptive Non-myopic Quantizer Design for Target Tracking in Wireless Sensor Networks
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 Cramer-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.Comment: Submitted to 2013 Asilomar Conference on Signals, Systems, and
Computer
A survey of localization in wireless sensor network
Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network
Localisation of mobile nodes in wireless networks with correlated in time measurement noise.
Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated
Two-layer particle filter for multiple target detection and tracking
This paper deals with the detection and tracking of an unknown number of targets using a Bayesian hierarchical model with target labels. To approximate the posterior probability density function, we develop a two-layer particle filter. One deals with track initiation, and the other with track maintenance. In addition, the parallel partition method is proposed to sample the states of the surviving targets
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