The frequency invariability of the warped modal signal and the warped signal autocorrelation function in shallow water is discussed.A method is proposed for passive source-range estimation based on the frequency invariability and warping transform of signal autocorrelation function received by a single hydrophone in a range-independent or weak range-dependent shallow water environment.In the method,a guided source with a known range is employed to provide the crucial and relative invariant scaled features.The experimental data in shallow water with an iso-speed profile and a fluctuated thermocline are used to verify this approach.The relative errors of the source range estimation are basically less than 10%.
An approach for long-range passive impulsive source ranging with a single receiver in shallow water is proposed,which utilizes the frequency spectrum of the warped signal autocorrelation function via warping transform.For an ideal waveguide,there are invariable frequency features both in the frequency spectrum of the warped signal corresponding to modal cut-off frequencies and the warped signal autocorrelation function due to modal interference.These intrinsic frequency features can be used to passive source ranging.So,the approximate relationship between the frequency of warped signal at an unknown source range and the intrinsic frequency extracted by the time warping transform is derived.These rules can be generalized to an actual shallow water waveguide.Employing an acoustic model to offer the invariable frequency spectrum features,the impulsive signal data collected by a single hydrophone in the North Yellow Sea in December 2011 are analyzed to verify the proposed source ranging approach.The estimated ranges are in good agreement with the ranges measured by GPS,and the mean relative error of range estimation is less than 10%.
In order to improve the ability to localize a source in an uncertain acoustic environment,a Bayesian approach,referred to here as Bayesian localization is used by including the environment in the parameter search space.Genetic algorithms are used for the parameter optimization.This method integrates the a posterior probability density(PPD) over environmental parameters to obtain a sequence of marginal probability distributions over source range and depth,from which the most-probable source location and localization uncertainties can be extracted.Considering that the seabed density and attenuation are less sensitive to the objective function of matched field processing,we utilize the empirical relationship to invert those parameters indirectly.The broadband signals recorded by a vertical line array in a Yellow Sea experiment in 2000 are processed and analyzed.It was found that,the Bayesian localization method that incorporates the environmental variability into the processor,made it robust to the uncertainty in the ocean environment.In addition,using the empirical relationship could enhance the localization accuracy.
