Performance of sample covariance based capon bearing only tracker

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.