Performance of sample covariance based capon bearing only tracker

Abstract

Bearing estimates input to a tracking algorithm require a concomitant measurement error to convey confidence. When Capon algorithm based bearing estimates are derived from low signal-to-noise ratio (SNR) data, the method of interval errors (MIE) provides a representation of measurement error improved over high SNR metrics like the Cramér-Rao bound or Taylor series. A corresponding improvement in overall tracker performance is had. These results have been demonstrated [4] assuming MIE has perfect knowledge of the true data covariance. Herein this assumption is weakened to explore the potential performance of a practical implementation that must address the challenges of non-stationarity and finite sample effects. Comparisons with known non-linear smoothing techniques designed to reject outlier measurements is also explored.

Condition numbers of indefinite rank 2 ghost Wishart matrices

“…Though much is known when Σ is positive definite [5], to the extent of our knowledge, the indefinite case is rarely considered. We work out the distribution of the ratio of the eigenvalues and the condition number of A as it has applications in hypersensitive ground based radars [1,2].…”

“…The natural generalization would be to extend the results to L × N indefinite matrices. We satisfied ourselves with N = 2 given its relevance for applications [1,2].…”