A Consistent Filter for Robust Decentralized Data Fusion

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Authors
  1. Benaskeur, A.R.
  2. Roy, J.
Corporate Authors
Defence R&D Canada - Valcartier, Valcartier QUE (CAN)
Abstract
The Situation Analysis Support Systems (SASS) Group in the Decision Support Systems (DSS) Section at Defence Research & Development Canada (DRDC) - Valcartier is currently investigating advanced concepts for adaptation and integration of the data fusion and sensor management processes. These concepts could apply to any current Canadian military platform's sensor suite, as well as its possible future upgrades, to improve its performance against the predicted future threat. The reported work addresses the problem of automatically aggregating information from multiple data sources. "Multiple Source Data Fusion" (MSDF) is used to indicate the general approach for combining the sensor data into global tracks. The selection of the appropriate MSDF techniques depends on the underlying architecture. For the centralized scheme, the sources are known to be independent and the Kalman filter provides an optimal solution. Unfortunately, when the decentralized architecture is used the sources become correlated and the Kalman filter cannot be applied. The covariance intersection method has been proposed as a solution to the problem of the decentralized data fusion, but results in a decrease in performance. A new fusion algorithm, that avoids both the inconsistency of the Kalman filter and the lack of performance of the covariance intersection, is proposed.

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Keywords
MSDF (Multi-Sensor Data Fusion);Data association;Sensor fusion;Fusion algorithms;MSDF (Multi-Source Data Fusion)
Report Number
DRDC-VALCARTIER-TR-2001-223 — Technical Report
Date of publication
29 Oct 2002
Number of Pages
71
DSTKIM No
CA021529
CANDIS No
518345
Format(s):
Hardcopy;Document Image stored on Optical Disk

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