Abstract
In this paper, a state estimation problem is considered for a target tracking scheme in wireless network environments. Firstly, a unified algorithm of finite memory structure (FMS) filtering and smoothing is proposed for a discrete-time state-space model. As shown in the terminology unified, both FMS filter and smoother are derived by solving one optimization problem directly with incorporation of the unbiasedness constraint. Hence, the unified algorithm provides simultaneously the current state estimate as well as the lagged state estimate using only finite measurements and inputs on the most recent window. The proposed unified algorithm of FMS filtering and smoothing shows that there are some unique properties such as unbiasedness, deadbeat, time-invariance and intrinsic robustness, which cannot be obtained by the recursive infinite memory structure (IMS) filtering such as Kalman filter. The on-line computational complexity of the proposed unified algorithm is discussed. Secondly, as an application of the proposed unified algorithm, a target tracking scheme in wireless network environments is considered via computer simulations for moving target’s accelerations of various shapes. The proposed unified algorithm-based target tracking scheme provides estimates for position as well as acceleration of moving target in real time, while eliminating unwanted noise effects and maintaining desired moving positions. Due to intrinsic robustness and deadbeat properties, the proposed unified algorithm-based scheme can outperform the existing IMS filtering-based scheme when acceleration suddenly changes.
Highlights
The Kalman filter and smoother [1,2,3,4,5] have been the most commonly fundamental tools for filtering and smoothing in statistical time series analysis
As an alternative to the Kalman filter, the finite memory structure (FMS) filter has been designed for state estimation and shown inherently to have BIBO stability and be more robust against temporary uncertainties [6,7,8,9]
This paper has dealt with the state estimation problem for the target tracking scheme in wireless communication environments
Summary
The Kalman filter and smoother [1,2,3,4,5] have been the most commonly fundamental tools for filtering and smoothing in statistical time series analysis. As an alternative to the Kalman filter, the finite memory structure (FMS) filter has been designed for state estimation and shown inherently to have BIBO stability and be more robust against temporary uncertainties [6,7,8,9] This FMS filter has been applied successfully in various engineering fields [10,11,12]. In real situations, moving targets maneuver and change velocity and move temporarily with nonzero acceleration This can be a temporary uncertainty and effects typically occur over a short time interval, the state estimation filter should be robust to diminish the effects of the temporary uncertainty. A target tracking in wireless network environments is considered as an application of the proposed unified algorithm of FMS filtering and smoothing.
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