Reduced-complexity scheme using alpha–beta filtering for location tracking

Chiou, Yih‐Shyh; Wang, Chin-Liang; Yeh, Sheng‐Cheng

doi:10.1049/iet-com.2010.0968

Cited by 28 publications

(40 citation statements)

References 18 publications

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“…Taylor-series expansion, whereat x n ( ) (t) means n-fold derivation by time t (see (18)). Using E{D t n j } = 1 −1 t ′n p Dt j t ′ dt ′ , (18) may be rewritten as (see (19)).…”

Section: Acknowledgmentsmentioning

confidence: 99%

M‐sequence‐based ultra‐wideband sensor network for vitality monitoring of elders at home

Sachs

Herrmann

2015

IET Radar, Sonar & Navigation

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The western societies are increasingly faced with the challenges of enabling a self-determined and safe life for a growing number of elderly people. Remote monitoring of vitality is an important tool for health risk mitigation but many of the current methods either deliver only infrequent information or require conscious cooperation of the senior. The authors propose an ultrawideband (UWB) radio sensor network based on M-sequence technology specifically designed for the vitality monitoring of persons living alone. It uses the frequency band in the range from 6 to 8.5 GHz (electronic communications committee (ECC)-band), and provides precise localisation, captures breathing motion during rests, can be used for fall detection and allows the extraction of gait features. The sensor network can be installed in common living spaces. It operates continuously in the background without any user interaction. Vitality monitoring by radar sensors is based on motion detection and tracking. This poses some challenges to radar devices because of the harsh multi-path conditions in apartments. This study discusses these challenges, illustrates how to meet them and gives examples for vitality features which can be extracted from UWB-radar data.

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“…Taylor-series expansion, whereat x n ( ) (t) means n-fold derivation by time t (see (18)). Using E{D t n j } = 1 −1 t ′n p Dt j t ′ dt ′ , (18) may be rewritten as (see (19)).…”

Section: Acknowledgmentsmentioning

confidence: 99%

M‐sequence‐based ultra‐wideband sensor network for vitality monitoring of elders at home

Sachs

Herrmann

2015

IET Radar, Sonar & Navigation

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“…An Alpha-Beta (α-β) filtering approach can be based on the steady state of the KF-based approach, which does not require repeatedly calculating the Kalman gain, and the computational complexity of the scheme is much lower than the traditional KF algorithm [6]. In this paper, using x for position and v for speed, the α-β tracking approach is expressed as follows.…”

Section: Location Tracking Using Alpha-beta Filteringmentioning

confidence: 99%

“…The location estimator based on the α-β tracking scheme to track the location information of an MT was described in [6]. Furthermore, α and β are tuning constants between number 0 and number 1 to smooth the location and speed estimates.…”

Section: Location Tracking Using Alpha-beta Filteringmentioning

confidence: 99%

“…Furthermore, to develop an algorithm with low computational complexity in the proposed experimental testbed, the decision mode of the KF-based approach is replaced with an α-β-based approach to avoid repeatedly calculating the Kalman gain in the ZPS testbed [6]. In terms of the inherent fixed-coefficient feature of α-β filtering, the location information between the prediction phase and correction phase is efficiently cycled, thus simplifying implementation of a KF-based approach.…”

Section: Zps Testbedmentioning

confidence: 99%

“…To improve location-estimation efficiency with algorithm during location tracking, it would be useful to develop an algorithm with high location accuracy and low computational complexity. Therefore, some fixed coefficient or degenerate form algorithms were proposed to avoid repeatedly calculating the Kalman gain, and the computational complexity of these schemes is much lower than the traditional KF algorithm [5]- [6]. However, for location-tracking approaches, the dramatic speed changes in a short time may result in the corner effect and diminish location accuracy.…”

Section: Introductionmentioning

confidence: 99%

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A Location-Tracking Testbed Using Vision-Assisted Scheme for Wireless Sensor Networks

Tsai

Chiou

Chang

2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents the performance of an efficient location tracking algorithm based on Alpha-Beta (α-β) filtering with visionassisted in a wireless sensor network (WSN) environment. With a vision-assisted calibration technique based on normalized crosscorrelation scheme, the proposed approach is an accuracy enhancement procedure that effectively removes system errors causing uncertainty in measuring a dynamic environment. That is, using the vision-assisted approach to estimate the locations of the reference nodes as landmarks, an α-β tracking scheme with the landmark information can calibrate the location estimation and improve the corner effect. The experimental results demonstrate that the proposed location-tracking algorithm combining visionassisted scheme with α-β filtering approach can achieve an accurate location very close to the traditional Kalman filtering (KF) algorithm in a ZigBee positioning platform. As compared with the KF-based approach, the proposed tracking approach can avoid repeatedly calculating the Kalman gain and achieve reasonably good performance with much lower computational complexity.

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