<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>We consider a multi-target tracking problem that aims to simultaneously determine the number and state of mobile targets in the field. Conventional paradigms tend to report only the existence and state of targets according to centralized detection and data fusion. On the contrary, we investigate a multi-target, multi-sensor scenario in which (a) both the number and the state of the targets are unknown a priori; and (b) the detection with respect to targets is employed in a distributed manner. Toward this end, we exploit random set theory, a statistical tool based on Bayesian framework, for establishing generalized likelihood and Markov density functions to yield an iterative filtering procedure. We conduct a study regarding how the design of distributed detection has impact on the result of system level information fusion. The sources of analyzed data include (a) simulation-based sensor readings through bi-directional sensing/communication; and (b) practical images taken by multiple cameras through uni-directional sensing/communication. The formulation of Bayesian filtering suggests that a design of a tracking system be adaptive to change of detection performance.</p></abstract>

We consider a multi-target tracking problem that aims to simultaneously determine the number and state of mobile targets in the field. Conventional paradigms tend to report only the existence and state of targets according to centralized detection and data fusion. On the contrary, we investigate a multi-target, multi-sensor scenario in which (a) both the number and the state of the targets are unknown a priori; and (b) the detection with respect to targets is employed in a distributed manner. Toward this end, we exploit random set theory, a statistical tool based on Bayesian framework, for establishing generalized likelihood and Markov density functions to yield an iterative filtering procedure. We conduct a study regarding how the design of distributed detection has impact on the result of system level information fusion. The sources of analyzed data include (a) simulation-based sensor readings through bi-directional sensing/communication; and (b) practical images taken by multiple cameras through uni-directional sensing/communication. The formulation of Bayesian filtering suggests that a design of a tracking system be adaptive to change of detection performance.

Multi-Target, Multi-Sensor Tracking Based on Quality-Of-Information and Formal Bayesian Frameworks

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

We consider a multi-target tracking problem that aims to simultaneously determine the number and state of mobile targets in the field. Conventional paradigms...

Multi-Target, Multi-Sensor Tracking Based on Quality-Of-Information and Formal Bayesian Frameworks

Published Online: Nov 02, 2017

Page range: 842 - 857

DOI: https://doi.org/10.21307/ijssis-2017-324

Keywords
Sensor networks, multi-target tracking, detection, random set theory

© 2008 Juo-Yu Lee et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Multi-Target, Multi-Sensor Tracking Based on Quality-Of-Information and Formal Bayesian Frameworks

Published Online: Nov 02, 2017

Page range: 842 - 857

DOI: https://doi.org/10.21307/ijssis-2017-324

KeywordsSensor networks, multi-target tracking, detection, random set theory

© 2008 Juo-Yu Lee et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
Sensor networks, multi-target tracking, detection, random set theory