PROJECT TITLE :

Performance Measures of Covariance and Information Matrices in Resource Management for Target State Estimation

ABSTRACT:

In target tracking, sensor resource management (SRM) assigns to every target a best combination of sensors, that needs performance analysis of track filter updates. Two popular implementations of track filters are the Kalman filter (or covariance filter) and the knowledge filter. SRM with Kalman filters makes an attempt to reduce the estimation error covariance matrix-based mostly scalar performance measures, whereas SRM with information filters aims to maximize the knowledge matrix-primarily based counterpart. During this paper, we investigate problems related to scalar performance measures and, in specific, compare the use of trace, determinant, and eigenvalues of the covariance matrix or data matrix as scalar performance measures. The study demonstrates that matrix measures are applicable for resource management applications. Furthermore, the study shows when the matrix measures lead to equivalent goals. While this analysis is agnostic to the type of measurement, the paper demonstrates how to accommodate bearing and vary measurements. Overall, the analysis provides insight about how sensor measurements best reduce uncertainty thus that we tend to will properly exploit performance measures to satisfy requirements of practical tracking and SRM applications.


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