In surveillance and reconnaissance applications, dynamic objects are dynamically followed by track filters with
sequential measurements. There are two popular implementations of tracking filters: one is the covariance or Kalman
filter and the other is the information filter. Evaluation of tracking filters is important in performance optimization not
only for tracking filter design but also for resource management. Typically, the information matrix is the inverse of the
covariance matrix. The covariance filter-based approaches attempt to minimize the covariance matrix-based scalar
indexes whereas the information filter-based methods aim at maximizing the information matrix-based scalar indexes.
Such scalar performance measures include the trace, determinant, norms (1-norm, 2-norm, infinite-norm, and Forbenius
norm), and eigenstructure of the covariance matrix or the information matrix and their variants. One natural question to
ask is if the scalar track filter performance measures applied to the covariance matrix are equivalent to those applied to
the information matrix? In this paper we show most of the scalar performance indexes are equivalent yet some are not.
As a result, the indexes if used improperly would provide an "optimized" solution but in the wrong sense relative to
track accuracy. The simulation indicated that all the seven indexes were successful when applied to the covariance
matrix. However, the failed indexes for the information filter include the trace and the four norms (as defined in
MATLAB) of the information matrix. Nevertheless, the determinant and the properly selected eigenvalue of the
information matrix were successful to select the optimal sensor update configuration. The evaluation analysis of track
measures can serve as a guideline to determine the suitability of performance measures for tracking filter design and