A lucky covariance estimator based on cumulative coherence

Abstract

The performance of adaptive acoustic localization methodologies depends on the quality of the covariance matrix being inverted. This paper demonstrates a technique to improve covariance estimation using the principles of lucky signal processing and the cumulative coherence. Lucky processing, popularized in astro-photography, is a technique that increases signal quality by selectively keeping only a small fraction from a pool of potential snapshots. Cumulative coherence, a measure of how well a set of vectors is described by its subsets, provides the measure of “data quality” that enables the lucky processing. This approach was applied to covariance estimation on an acoustic array by taking a fixed duration sample of data and creating a dense set of snapshots with higher than usual overlap. From these densely sampled snapshots, the “luckiest” ones were found using cumulative coherence, and the covariance was averaged as normal. Using data from the SWellEX-96 experiment, this new estimator was compared with standard practice. It was found that the lucky covariance estimate was successful at adaptive matched field processing and produced a less ambiguous processor output than the conventional estimator. The lucky covariance estimate had a higher estimated signal-to-noise ratio, especially when the source was at longer ranges from the array.