By Topic

Enhanced Tracking Performance with Signal Amplitude Information of Sensor Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Xuezhi Wang ; Dept. of Electr. & Electron., Melbourne Univ., Vic. ; Musicki, D.

Automatic multi-target tracking in a binary sensor network needs to solve the simultaneous multiple sources localization problem. The virtual measurement (VM) approach provides a way to solve this problem via the integrated sensing processing (ISP). In the VM approach, a set of activated sensor detections are mapped into a set of virtual measurements as if they were observed by a large sensor. The set of VMs are then used for multi-target tracking. A drawback of this method is that some VMs may have larger variances when sensor nodes are sparsely distributed, which can yield considerably large estimation error. In this paper, we present a method to reduce the uncertainty of VMs using relative signal amplitude information at the cost of communicating more bits from activated sensors to base station. Instead of assigning a VM, we estimate the target source position using relative signal amplitude information which is also known as received signal strength (RSS) and is assumed to be available to the base station. This position is then treated as an alternative VM used by a LMIPDA tracker. Simulation results shown that an improved tracking accuracy can be obtained compared to the case where the standard VMs are used

Published in:

Information Fusion, 2006 9th International Conference on

Date of Conference:

10-13 July 2006