UFIR Filtering for GPS-Based Tracking over WSNs with Delayed and Missing Data

Karen Uribe-Murcia,Jose A Andrade-Lucio,Yuriy S Shmaliy

doi:10.1155/2018/7456010

Abstract

In smart cities, vehicles tracking is organized to increase safety by localizing cars using the Global Positioning System (GPS). The GPS-based system provides accurate tracking but is also required to be reliable and robust. As a main estimator, we propose using the unbiased finite impulse response (UFIR) filter, which meets these needs as being more robust than the Kalman filter (KF). The UFIR filter is developed for vehicle tracking in discrete-time state-space over wireless sensor networks (WSNs) with time-stamped data discretely delayed on k-step-lags and missing data. The state-space model is represented in a way such that the UFIR filter, KF, and H∞ filter can be used universally. Applications are given for measurement data, which are cooperatively transferred from a vehicle to a central station through several nodes with k-step-lags. Better tracking performance of the UFIR filter is shown experimentally.

Highlights

Accurate target tracking is one of the key problems in urban areas [1], which especially arises in smart cities design [2]
If a target is equipped with the Global Positioning System (GPS) tracker, measurement data can be transferred to a central station through one or several nodes of a wireless sensor network (WSN) [3]
We develop the unbiased finite impulse response (UFIR) filter for GPSbased vehicle tracking over WSNs with time-stamped data discretely delayed and missing data

Summary

Introduction

Accurate target tracking is one of the key problems in urban areas [1], which especially arises in smart cities design [2]. The robust H∞ filter bounds the mean square error (MSE) for admissible parameter perturbations and delays [19, 22], which allows for minimizing errors with less information required than for the noise statistics [20, 23] Another way to achieve better robustness is to process most recent finite data [24] using finite impulse response (FIR) filters [25]. Journal of Electrical and Computer Engineering [28, 41,42,43] is most robust among other FIR solutions owing to an ability to ignore the noise statistics and initial values This filter is bounded-input bounded-output (BIBO) stable and blind on given horizons of N points, but is still not developed for observations with delayed and missing data.

Tracking Model and Problem Formulation

UFIR Filter for Tracking with Delayed and Missing Data

GPS-Based Tracking of a Moving Vehicle

Conclusions