Distributed Particle Filter for Target Tracking: With Reduced Sensor Communications
For efficient and accurate estimation of the location of objects, a network of sensors can be used to detect and track targets in a distributed manner. In nonlinear and/or non-Gaussian dynamic models, distributed particle filtering methods are commonly applied to develop target tracking algorithms....
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doaj-329854552eda4eeebe3cdd8ffcbc584f2020-11-24T22:59:56ZengMDPI AGSensors1424-82202016-09-01169145410.3390/s16091454s16091454Distributed Particle Filter for Target Tracking: With Reduced Sensor CommunicationsTadesse Ghirmai0School of STEM, Division of Engineering and Mathematics, University of Washington Bothell, Bothell, WA 98011, USAFor efficient and accurate estimation of the location of objects, a network of sensors can be used to detect and track targets in a distributed manner. In nonlinear and/or non-Gaussian dynamic models, distributed particle filtering methods are commonly applied to develop target tracking algorithms. An important consideration in developing a distributed particle filtering algorithm in wireless sensor networks is reducing the size of data exchanged among the sensors because of power and bandwidth constraints. In this paper, we propose a distributed particle filtering algorithm with the objective of reducing the overhead data that is communicated among the sensors. In our algorithm, the sensors exchange information to collaboratively compute the global likelihood function that encompasses the contribution of the measurements towards building the global posterior density of the unknown location parameters. Each sensor, using its own measurement, computes its local likelihood function and approximates it using a Gaussian function. The sensors then propagate only the mean and the covariance of their approximated likelihood functions to other sensors, reducing the communication overhead. The global likelihood function is computed collaboratively from the parameters of the local likelihood functions using an average consensus filter or a forward-backward propagation information exchange strategy.http://www.mdpi.com/1424-8220/16/9/1454particle filteringtarget-trackingsensor networksconsensus filterforward-backward |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tadesse Ghirmai |
spellingShingle |
Tadesse Ghirmai Distributed Particle Filter for Target Tracking: With Reduced Sensor Communications Sensors particle filtering target-tracking sensor networks consensus filter forward-backward |
author_facet |
Tadesse Ghirmai |
author_sort |
Tadesse Ghirmai |
title |
Distributed Particle Filter for Target Tracking: With Reduced Sensor Communications |
title_short |
Distributed Particle Filter for Target Tracking: With Reduced Sensor Communications |
title_full |
Distributed Particle Filter for Target Tracking: With Reduced Sensor Communications |
title_fullStr |
Distributed Particle Filter for Target Tracking: With Reduced Sensor Communications |
title_full_unstemmed |
Distributed Particle Filter for Target Tracking: With Reduced Sensor Communications |
title_sort |
distributed particle filter for target tracking: with reduced sensor communications |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2016-09-01 |
description |
For efficient and accurate estimation of the location of objects, a network of sensors can be used to detect and track targets in a distributed manner. In nonlinear and/or non-Gaussian dynamic models, distributed particle filtering methods are commonly applied to develop target tracking algorithms. An important consideration in developing a distributed particle filtering algorithm in wireless sensor networks is reducing the size of data exchanged among the sensors because of power and bandwidth constraints. In this paper, we propose a distributed particle filtering algorithm with the objective of reducing the overhead data that is communicated among the sensors. In our algorithm, the sensors exchange information to collaboratively compute the global likelihood function that encompasses the contribution of the measurements towards building the global posterior density of the unknown location parameters. Each sensor, using its own measurement, computes its local likelihood function and approximates it using a Gaussian function. The sensors then propagate only the mean and the covariance of their approximated likelihood functions to other sensors, reducing the communication overhead. The global likelihood function is computed collaboratively from the parameters of the local likelihood functions using an average consensus filter or a forward-backward propagation information exchange strategy. |
topic |
particle filtering target-tracking sensor networks consensus filter forward-backward |
url |
http://www.mdpi.com/1424-8220/16/9/1454 |
work_keys_str_mv |
AT tadesseghirmai distributedparticlefilterfortargettrackingwithreducedsensorcommunications |
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