Measurement-converted Kalman filter tracking with Gaussian intensity attenuation signal in wireless sensor networks

Sha Wen,Xiaoqing Hu,Liqiang Xing,Hui Zhang

doi:10.1177/1550147717700896

Abstract

In this article, the target tracking problem in a wireless sensor network with nonlinear Gaussian signal intensity attenuation model is considered. A Bayesian filter tracking algorithm is presented to estimate the locations of moving source that has unknown central signal intensity. This approach adopts a measurement conversion method to remove the measurement nonlinearity by the maximum likelihood estimator, and a linear estimate of the target position and its associated noise statistics obtained by the Newton–Raphson iterative optimization steps are applied into the standard Kalman filter. The Monte Carlo simulations have been conducted in comparison with the commonly used extended Kalman filter with an augmented state that consists of both the original target state and the augmentative central signal intensity. It is observed that the proposed measurement-converted Kalman filter can yield higher accurate estimate and nicer convergence performance over existing methods.

Highlights

Recent advances in micro-electro-mechanical systems, networking systems, and embedded microprocessor technologies have drawn tremendous interests and applications of wireless sensor networks (WSNs)
We remark that the augmented-state extended Kalman filter (AEKF) is initialized by guessing the initial estimate ^a0 and error covariance .20 of unknown parameter a, so that its convergence performance is extremely sensitive to the initialization accuracy
Under the high measurement noise conditions, the measurement errors become bigger, the RMSE of the maximum likelihood (ML) becomes the worst, the RMSE of the AEKF increases as the initial covariance of a decreases, but the RMSE of the proposed measurement-converted Kalman filter (MCKF) is still lower than the AEKF and traditional extended Kalman filter (TEKF). These results indicate that the MCKF can yield higher accurate estimate and nicer convergence performance than the AEKF, TEKF, and the ML

Summary

Introduction

Recent advances in micro-electro-mechanical systems, networking systems, and embedded microprocessor technologies have drawn tremendous interests and applications of wireless sensor networks (WSNs). In the work by Mysorewala et al.,[25] an EKF-based localization method using an elliptical forest fire spread model is developed, but the linearization procedure of nonlinear measurement function in the EKF algorithm may incur larger errors in the true posterior mean and covariance of the target state.

Results

Conclusion