The Characterization Of The Infrasonic Noise Field And Its Effects On Least Squares Estimation

Abstract

Localization of the source of an acoustic wave propagating through the atmosphere is not a new problem. Location methods date back to World War I, when sound location was used to determine enemy artillery positions. Since the drafting of the Comprehensive Nuclear-Test-Ban Treaty in 1996 there has been increased interest in the accurate location of distant sources using infrasound. A standard method of acoustic source location is triangulation of the source from multi-array back azimuth estimates. For waves traveling long distances through the atmosphere, the most appropriate method of estimating the back azimuth is the least squares estimate (LSE). Under the assumption of an acoustic signal corrupted with additive Gaussian, white, uncorrelated noise the LSE is theoretically the minimum variance, unbiased estimate of the slowness vector. The infrasonic noise field present at most arrays is known to violate the assumption of white, uncorrelated noise. The following work characterizes the noise field at two infrasound arrays operated by the University of Alaska Fairbanks, The power distribution and coherence of the noise fields was determined from atmospheric pressure measurements collected from 2003-2006. The estimated power distribution and coherence of the noise field were not the white, uncorrelated noise field assumed in the analytic derivation of the LSE of the slowness vector. The performance of the LSE of azimuth and trace velocity with the empirically derived noise field was numerically compared to its performance under the standard noise assumptions. The effect of violating the correlation assumption was also investigated. The inclusion of clutter in the noise field introduced a dependence to the performance of the LSE on the relative signal amplitude. If the signal-to-clutter ratio was above 10 dB, the parameter estimates made with the correlated noise field were comparable to the estimates made with uncorrelated noise. From the results of these numerical studies, it was determined that the assumption of Gaussian, white, uncorrelated noise had little effect on the performance of the LSE at signal-to-noise ratios greater than 10 dB, but tended to over estimate the performance of the LSE at lower signal-to-noise ratios.