Augmented Filtering Based on Information Weighted Consensus Fusion for Simultaneous Localization and Tracking via Wireless Sensor Networks

Xiangyuan Jiang,Baozhou Lu,Chunbo Luo,Xinheng Wang,Peng Ren

doi:10.1155/2015/391757

Abstract

This paper develops a novel augmented filtering framework based on information weighted consensus fusion, to achieve the simultaneous localization and tracking (SLAT) via wireless sensor networks (WSNs). By integrating augmented transition and observation models, we formulate a dynamical system that encodes both the target moving manners and coarse sensor locations in an augmented state. We then conduct augmented filtering based on augmented extended Kalman filters to estimate the augmented state. We further refine our target estimate according to information weighted consensus filtering which fuses the target information obtained from neighboring sensors. The fused information is fed back as the target estimate to the augmented filter. Our framework is computationally efficient because it only requires neighboring sensor communications. Experiments on SLAT problem validate the effectiveness of the proposed algorithm in terms of tracking accuracy and localization precision in limited ranging conditions.

Highlights

Simultaneous localization and tracking (SLAT) has gained great research interest recently
In non-line-of-sight environments, cubature Kalman filters have been developed for SLAT [2], with augmented state vector constructed by concatenating a target state and a sensor location
Traditional SLAT frameworks rely on centralized fusion of sensor-based target state estimation, and they suffer from heavy communication overheads and are inefficient

Summary

Introduction

Simultaneous localization and tracking (SLAT) has gained great research interest recently. The difficulty of sensor localization lies in the fact that a target may not be observed by some or all sensors in a practical network To overcome this limited observability, many strategies have been proposed, such as the distributed sensor localization framework with weighted consensus [7], the hybrid peer-to-peer tracking architecture with Kalman consensus filter [8], and the information weighted consensus filter [9]. Comparing with the belief propagation schemes, our framework has the advantage of low computation cost because it reduces the unnecessary communication overheads It addresses the error decoupling problem, which is usually unavoidable in the algorithms that distributively estimate sensor positions and target trajectories, by proposing a two-stage filtering architecture.

Preliminaries

Augmented Filtering Based on Information Weighted Consensus Fusion

Simulation

Findings

Discussion and Conclusion