A finite memory structure filtering for indoor positioning in wireless sensor networks with measurement delay

2019

Applied Sciences

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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.

Section: Introductionmentioning

confidence: 99%

Finite Memory Structure Filtering and Smoothing for Target Tracking in Wireless Network Environments

2019

Applied Sciences

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“…Thus, the Kalman filter tends to accumulate the filtering error as time goes and can show even divergence phenomenon for temporary modeling uncertainties and round-off errors [10]- [13]. This inherent property of the Kalman filter has been shown in applications of wireless sensor networks [14] [15]. In addition, actually, long past measurements are not useful for detection of faults with unknown times of occurrence.…”

Section: Introductionmentioning

confidence: 99%

A Design of Finite Memory Residual Generation Filter for Sensor Fault Detection

2016

Preprint

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In the current paper, a residual generation filter with finite memory structure is proposed for sensor fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite observations and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noise-free systems. The proposed residual generation filter is specified to the digital filter structure for the amenability to hardware implementation. Finally, to illustrate the capability of the proposed residual generation filter, numerical examples are performed for the discretized DC motor system having the multiple sensor faults.

“…Thus, the Kalman filter tends to accumulate filtering errors as time goes by and can even show divergence phenomenon for temporary modeling uncertainties and round-off errors [10]- [13]. This inherent property of the Kalman filter has been shown in sensor application areas [14], [15]. In addition, long past measurements are not useful for fault detection with unknown occurrence times.…”

Section: Introductionmentioning

confidence: 99%

A Design of Finite Memory Residual Generation Filter for Sensor Fault Detection

Measurement Science Review

2017

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In the current paper, a residual generation filter with finite memory structure is proposed for sensor fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite measurements and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noisefree systems. The proposed residual generation filter is specified to the digital filter structure for the amenability to hardware implementation. Finally, to illustrate the capability of the proposed residual generation filter, extensive simulations are performed for the discretized DC motor system with two types of sensor faults, incipient soft bias-type fault and abrupt bias-type fault. In particular, according to diverse noise levels and windows lengths, meaningful simulation results are given for the abrupt bias-type fault.