Abstract
Vehicles tracking is organized to increase safety in smart cities 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 a more robust alternative to 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 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]
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) [2]
We develop the unbiased finite impulse response (UFIR) filter for GPS-based vehicle tracking over WSNs with time-stamped discretely delayed and missing data
Summary
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) [2]. The robust H∞ filter bounds the mean square error (MSE) for admissible parameter perturbations and delays [9], which allows minimizing errors with less information required than for the noise statistics [10] Another way to achieve better robustness is to process most recent finite data [12] using finite impulse response (FIR) filters [13]. The KF and H∞ filter were most developed for data with latency
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