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The goal of this work is to accurately estimate the modal frequencies and dispersion curves from a measured ocean acoustics signal. A particle filtering approach, a class of sequential Monte Carlo methods, is developed for modal frequency identification and dispersion curves estimation from a time-frequency representation of ocean acoustics signal. The adaptive resampling algorithm for enhancing the quality of a set of particles after likelihood calculation is implemented to improve the accuracy of the modal estimates as well as the dispersion curves of the signal. Results demonstrate the advantages in implementing the adaptive resampling into the conventional sequential importance sampling particle filter (SIS-PF) instead of using the sequential importance resampling (SIR) scheme. The noise robustness of the proposed method is demonstrated through examples where the realizations of different Signal-to-Noise Ratio (SNR) levels were used to test the performance of the adaptive resampling method. The results display the evidences that the adaptive resampling particle filter (AR-PF) is superior to the SIR-PF. Via root mean square error (RMSE), the AR-PF delivers smaller errors than those obtained by the SIR-PF for all SNR levels, emphasizing the benefit in incorporating the adaptive resampling into the PF for modal frequency identification and dispersion curves estimation of ocean acoustics signal.

It has been previously shown using synthetic data that dispersion tracking with particle filtering can be used for sediment sound speed inversion. Here, dispersion tracking is performed with data collected in the Gulf of Mexico for sediment sound speed and thickness and water column depth estimation. In this experiment, sound that propagates a long distance from the source allows the identification of dispersion curves reflecting the different group velocities of modal frequencies within and across modes. Although the data are noisy, dispersion curves are tracked with sequential filtering and used for inversion. Probability density functions of the three unknown parameters are obtained. Water column depth is estimated with little uncertainty. The estimated sound speed is representative of sandy sediment and the sediment thickness matches to a large extent prior knowledge.

This paper presents a stochastic description and evaluation of the ocean acoustics signal for the interpretation of the signal in the frequency domain in order to estimate the modal frequency and dispersion curves of the ocean acoustics signal. Thus, this investigation considered the analysis and simulation of an ocean acoustics time-series in the frequency-domain where the signal in the time-domain was corrupted by white Gaussian noise. With a stochastic property of the measured acoustics signal, we derived the accurate posterior probability distribution of the ocean acoustics modal frequencies and then incorporated a mathematical model of the signal propagating in a dispersive media to obtain a likelihood function for a sequential Bayesian filtering framework that is used for modal frequency and dispersion estimation. Sequential Bayesian filtering, a particle filter in particular, is implemented for frequency and dispersion estimation to test the accuracy of the derived model. Demonstrated via simulation results, the proposed method performs excellent estimation as compared to the conventional MAP estimator especially at high noise level. Finally, evaluated by the Monte Carlo root mean squared errors (MCRMSE), the proposed stochastic model offers excellent tracking results with smaller errors than those provided by the existing methods even in low signal-to-noise ratios (SNRs), illustrating the noise robustness and superiority of the proposed model over the other approaches.

Dispersion curves in ocean environments are accurately estimated from received signals through the extraction of instantaneous modal frequencies and corresponding arrival times for long-range propagation. The ultimate goal is to estimate sediment sound speed using the extracted dispersion pattern. The approach extends work previously conducted in dispersion tracking with sequential filtering, improving on the latter technique. The sequential state-space method that is developed for the extraction of time-frequency information from specific time instances relies on a representation of those as a sum of elemental pulses, resulting from analysis of the received field. The method is tested on synthetic noisy data with different noise levels. After dispersion probability density functions are estimated via a particle filter, they are subsequently employed for sound speed inversion. Correct mode identification is a challenge impacting inversion; this is demonstrated through two examples and a way to remedy the problem is discussed.

Sequential Bayesian filtering methods have been previously used in dispersion curve tracking for long range sound propagation in the ocean. Modal frequency probability density functions were extracted for sound speed inversion. Here, we calculate modal arrival time densities, instead, and employ them for inversion for sediment sound speed and thickness and water column depth. Bayesian mode identification is performed to this end. We investigate two methods for describing the statistical errors in power spectra, which we use in the arrival time density calculation using normal modes. We then link these densities to the parameters of interest. The approaches are tested with synthetic data as well as data collected in the Gulf of Mexico. [Work supported by ONR.]

The goal of this work is to improve our knowledge of the ocean medium using physics of sound propagation and statistical signal processing. Two of the approaches we discuss are based on global and local optimization combined with sequential filtering. A third one falls under the category of direct (“exact”) methods. The first two approaches are based on the extraction and the association between paths/modes and detected arrivals and also produce posterior probability density functions, characterizing the uncertainty structure of modal/multipath arrivals. These are then propagated backwards through an acoustic model for posterior probability density function calculation of environmental parameters. A third method extends Stickler’s exact inverse approach, employing measurements of a reflection coefficient at low frequencies to obtain the sediment sound speed profile. We develop an approximation based on interpolation and a regularization approach. The former improves on results of a previous method for sol...

Bayesian methods provide a wealth of information on acoustic features of a propagation medium and the uncertainty surrounding their estimation. In previous work, we showed how sequential Bayesian (particle) filtering can be used to extract dispersion characteristics of a waveguide. Here, we utilize these characteristics for the estimation of geoacoustic properties of sediments. As expected, the method relies on accurate identification of modes. The effect of correct/erroneous mode identification on geoacoustic estimates is quantified and approaches are developed for robust modal recognition in conjunction with the particle filter. Additionally, the statistical behavior of the noise present in the data measurements is further investigated with more complex noise modeling leading to improved results. The approaches are validated with both synthetic and real data collected during the Gulf of Mexico Experiment. [Work supported by ONR.]

In previous work, we had shown how sequential Bayesian filtering methods can be used for the successful extraction of dispersion curves from broadband long-range acoustic data. Here, we extend this work by tracing carefully the true nature of the noise and the resulting probability density observations of the spectrogram of the received time series, employed in the tracking. The Gaussian model typically used in instantaneous frequency tracking relies on the assumption that noise is additive in the frequency domain. This model is, however, inaccurate. We discuss a chi-squared model of the acoustic data perturbations and its role in dispersion curve tracking. The new method provides much clearer curves than those computed with previous approaches. We demonstrate the potential of the technique by applying it to synthetic and real data for dispersion curve estimation and bathymetry and sediment sound speed inversion. [Work supported by ONR.]

Iterative and sequential Bayesian filtering methods have been previously used in dispersion tracking for long range sound propagation in oceanic environments. The methods rely on accurate modeling of (i) the acoustic field in the frequency domain as a function of time and (ii) the statistical model governing the behavior of errors in the measured data. Normal distributions are typically used for the latter but may not necessarily be the most accurate models for the description of the data. We investigate alternative methods for describing the statistical errors in power spectra, which we then use for linking the extracted time-frequency information to geoacoustic inversion. Probability density functions computed for frequencies and arrival times via filtering are propagated “backwards” through sound propagation models for quantification of the uncertainty in the estimation process. [Work supported by ONR].