Improving Reliability of Particle Filter-Based Localization in Wireless Sensor Networks via Hybrid Particle/FIR Filtering

Pak, Jung Min; Ahn, Choon Ki; Shmaliy, Yuriy S.; Lim, Myo Taeg

doi:10.1109/tii.2015.2462771

Cited by 215 publications

(115 citation statements)

References 35 publications

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“…It uses radio frequency identification devises (RFID) to collect material information, and transmits information to the data service center through the network. It also integrates the material information on the map displayed by GIS system to achieve the visibility control of material [7][8].…”

Section: State Of the Artmentioning

confidence: 99%

An Intelligent Logistics Tracking System Based on Wireless Sensor Network

Jijun¹

2018

Int. J. Onl. Eng.

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Abstract-In view of the shortcomings in information collection and tracking management in existing logistics, the key technologies of wireless sensor network and the basic concepts and methods of Information Technology Infrastructure Library(ITIL) is studied. The basic structure and main functions of the logistics tracking system based on wireless sensor network is put forward. It can effectively solve the detection, location, recognition and automatic processing of fault events and other issues of goods in the process of logistics tracking management. In addition, combined with the GIS software, the logistics tracking and monitoring system based on the wireless sensor network is realized by using Java language coding. The results show that this system can realize the tracking, visualization and automatic management of the information in the process of goods transportation. Therefore, the proposed method significantly improves the accuracy, rapidity and intuition of the logistics information management.

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Section: State Of the Artmentioning

confidence: 99%

An Intelligent Logistics Tracking System Based on Wireless Sensor Network

Jijun¹

2018

Int. J. Onl. Eng.

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“…[15] conducted a simulation-based study with WiFi-based tracking system using 'particle filter' and from the simulated experimental results, they established that incorporating single-hidden layer feed-forward networks (SLFNs) in measurement likelihood model has positive contributions in improving positioning estimation. [16] in their simulationbased study of the positioning with 'particle filter' and UWB radio beacons established the fact that when 'particle filter' fails that failure can be detected by computing the Mahalanobis distance between the sensor observation and the filtered sensor observation, and thereby certainty of the state estimation by the filter can be computed assuming that the sensor noise that leads to this wrong estimation is normally distributed. Thus, the square of 'Mahalanobis' distance between real observation and estimated observation shall follow a 'Chi-Square' distribution.…”

Section: Introductionmentioning

confidence: 99%

Investigation of wireless tracking performance in the tunnel-like environment with particle filter

Halder¹,

Chakravarty²

2018

MMEP

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When the basic nonlinear filtering problem for dynamic systems is considered, in such case, the particle filter is one of the suitable methods that has perhaps become one of the most commonly used methods in recent years. Positioning or localization falls under such a nonlinear filtering problem. Positioning is a matter of interest for both domestic and industrial application, due to its potential use in wide range of context-aware services that it can enable by leveraging the Internet of Things approach. However, in areas where GPS signals are not available such as underground tunnel roadways, where the localization is done mostly using radio beacons. This study mathematically simulates tracking operation in such a tunnel-like situation and studied the position estimation by the particle filter. From the results, we were able to visualize how varying different configuration parameters affect the estimation accuracies and also get an idea of worst-case estimates by seeing its standard deviation of estimated positions for different instances of repeated experiments. And our results also confirmed that deploying additional beacons have a contribution to the improvement in error tolerance. However, the improvements are significantly notable only around the point where beacon has been added. When the basic nonlinear filtering problem for dynamic systems is considered, in such case, the particle filter is one of the suitable methods that has perhaps become one of the most commonly used methods in recent years. Positioning or localization falls under such a nonlinear filtering problem. Positioning is a matter of interest for both domestic and industrial application, due to its potential use in wide range of context-aware services that it can enable by leveraging the Internet of Things approach. However, in areas where GPS signals are not available such as underground tunnel roadways, where the localization is done mostly using radio beacons. This study mathematically simulates tracking operation in such a tunnel-like situation and studied the position estimation by the particle filter. From the results, we were able to visualize how varying different configuration parameters affect the estimation accuracies and also get an idea of worst-case estimates by seeing its standard deviation of estimated positions for different instances of repeated experiments. And our results also confirmed that deploying additional beacons have a contribution to the improvement in error tolerance. However, the improvements are significantly notable only around the point where beacon has been added.

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“…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

Kim

2017

Measurement Science Review

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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.

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Improving Reliability of Particle Filter-Based Localization in Wireless Sensor Networks via Hybrid Particle/FIR Filtering

Cited by 215 publications

References 35 publications

An Intelligent Logistics Tracking System Based on Wireless Sensor Network

An Intelligent Logistics Tracking System Based on Wireless Sensor Network

Investigation of wireless tracking performance in the tunnel-like environment with particle filter

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

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