Abstract
The localization of various acoustic sources in a battlefield (such as weapon rounds, mortars, rockets, mines, improvised explosive devices, vehicle-borne improvised explosive devices and airborne vehicles) nowadays has a significant history. In acoustic source localization systems of multiple sensors, networked and placed at known positions are used to detect signals emitted from the source and perform localization of the source. Time of arrival estimation of the gun fire shock waves at large distances is significant when the purpose is the gun localization. At large distances shock wave signal is significantly deformed and therefore it is relatively difficult to accurately estimate the time of arrival. In this paper methods for TOA estimation are proposed, which are based on cumulants of the acoustic signal which originate from distant gunfire events. Localization of acoustic sources is performed in two steps by time difference estimation of acoustic signal arrival and using Discrete Probability Density method for positioning. Numerous field experiments have been conducted in order to verify performance of the proposed approach.
Highlights
ACOUSTIC signal which originates from a gunfire event is a stochastic non-linear process which generally more or less deviates from the Gaussian process of a known mean
Considering that the analysis showed that almost all the cumulative lags were negative, it can be concluded that the position of the hydrophone with respect to the position of the underwater explosion was such that it was only possible to receive shock waves reflected from the water / air boundary surface, that is, they had a negative pressure producing in the hydrophone position dilution
Based on the shown results, it can be concluded that increasing the number of samples in the time series causes worse accuracy of time of arrival (TOA) estimation and that directly reflects the accuracy of acoustic source position determination
Summary
ACOUSTIC signal which originates from a gunfire event is a stochastic non-linear process which generally more or less deviates from the Gaussian process of a known mean. The best results were performed by wavelet decomposition, see papers [3, 4] In this paper, another approach in the algorithm for TOA estimation was made by using the fourth order cumulant in order to identify time interval when BoN-wave occurs, because BoN-wave produces nonlinearity of the acoustic signal during its occurrence. The methods based on higher order statistics are very useful in problems where non-Gaussian, non-minimum phase, phase coupling or nonlinear behavior and robustness to additive noise are important. Detection and classification using higher order statistical and spectral techniques are proposed for using in many areas where pattern recognition is important These methods have the potential to elicit better performance from sensors and sensor networks [5].
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