Richard Lee Culver

Minimum Hellinger Distance based classification of underwater acoustic signals

Source classification and localization in the underwater environment is a challenging problem in part because propagation through the space- and time-varying medium introduces uncertainty in the signal. Coupled with uncertainty in environmental parameters, this uncertainty in received signals leads to a statistical treatment. Minimum Hellinger Distance (MHD) methods provide a robust and efficient framework for making classification decisions in this context.

Received Signal Parameter Statistics in Random/Uncertain Oceans

A Monte Carlo-based method has been developed to estimate parameter statistics for acoustic signals that have propagation through random and uncertain ocean environments. The method uses physics-based models for relevant environmental parameters and utilizes available environmental measurements. Statistical moments and covariance functions of the environmental parameters are used with the Maximum Entropy (Max-Ent) method to construct parameter probability density functions (pdfs). Random but properly correlated realizations of the environment are constructed from the pdfs. An acoustic propagation code is used to propagate acoustic energy through each realization of the environment in a Monte Carlo simulation. From the ensemble of received signals, signal parameters are estimated and the MaxEnt method used to construct signal parameter pdfs at all ranges and depths of interest. The method is demonstrated using 250 Hz acoustic propagation measurements and comprehensive environmental characterization from a 1996 experiment in the Strait of Gibraltar. This is a particularly complicated region dominated by strong tidal fluctuations and internal waves. Pdfs of rms received pressure calculated from the acoustic measurements are compared with simulated pdfs obtained using the Monte Carlo method. The agreement is generally good and the method appears promising

The Estimated Signal Parameter Detector: Incorporating Signal Parameter Statistics Into the Signal Processor

Acoustic propagation through a time-varying and spatially varying environment can produce variations in the received signal over multiple observations, possibly degrading receiver performance. This paper presents a signal processing structure that utilizes knowledge of received signal statistics to recoup lost performance. Recent research has shown that received signal parameter statistics can be calculated using Monte Carlo simulation and knowledge of ocean environment properties and processes. The processor possesses an estimator-correlator structure, and is referred to in this paper as the estimated signal parameter detector (ESPD). To demonstrate ESPD performance, the derivation is implemented to distinguish between monotone sinusoids with Gaussian-distributed amplitudes with identical means but different variances, embedded in zero-mean white Gaussian noise. In general, the amplitude distributions can possess any form and the noise distribution must belong to a general class of probability density functions (pdfs). The present assumptions allow for analytical results, and performance of the ESPD is seen to depend upon the signal-to-noise ratio (SNR) as well as the difference between the amplitude variances. Larger SNR and greater difference in amplitude variance result in better receiver performance, eventually leading to an asymptotic performance bound prediction.

Bistatic ocean surface reverberation simulation

Bistatic ocean surface reverberation is simulated using the bistatic framework (transmitter and receiver not collocated) of the generic sonar model (GSM) modified to incorporate the frequency spreading effects of transmitter, receiver, and scatterer motion and produce time-varying reverberation spectra. A computationally straightforward algorithm has been developed for computing high-frequency bistatic ocean surface scattering strength. The algorithm implements the Kirchoff approximation for near specular angles and composite roughness theory for angles away from the specular. The absorption and scattering effects of subsurface bubbles are also modeled. Simulation results are compared to high-frequency bistatic reverberation measurements made using a directional transmitter and omnidirectional receiver.<<ETX>>

Effects of scattering by air bubbles on performance of an underwater acoustic array

Analytical and numerical calculations of the effects of nearby air bubbles on performance of an underwater acoustic array are presented. Array performance is characterized by the array gain. Two effects of air bubbles on array gain are considered: (1) attenuation of the direct path signal of interest and (2) additive, correlated interference due to scattering by nearby bubbles. The incident acoustic field is taken to be locally planar. Bubble scattering contributions to the interference field are calculated using the single scattering approximation of Ishimaru (1977, Chap. 6) assuming contributions by resonant bubbles only. Comparison is made between the acoustic effects of homogeneous and inhomogeneous (clumpy) distributions of bubbles. One feature of this work is that bubble distribution can be generated using computational fluid dynamics calculations for a fluid field and momentum transport equations for the bubbles. [Work sponsored by Office of Naval Research, Code 321 Undersea Signal Processing.]

Symmetric small-aperture arrays for three-dimensional bearing estimation

Small aperture arrays (size less than a wavelength) can be used for passive direction of arrival (DOA) estimation of both broadband and narrowband signals in the frequency domain. Phase differences across the array are measured in the frequency domain and can be spectrally averaged for stationary DOA and frequencies if desired. Data windowing will bias the DOA measurement toward the center of the FFT data buffer and is useful to prevent spectral leakage from strong target signals overshadowing weak target signals. The array is capable of measuring multiple target DOAs so long as each target produces unique frequencies. Broadband signals from targets can be collected from the FFT bins and grouped by arrival angle using a bearing histogram. The Cramer-Rao lower bound (CRLB) for bearing accuracy is presented as a function of frequency, aperture, and signal-to-noise ratio (SNR). The reduction in DOA accuracy due to a small aperture can be overcome if the SNR is sufficiently high. Using symmetry in 2D and 3D a...

Near- and far-field beam forming using a linear array in deep and shallow water

Underwater sources are typically characterized in terms of a source level based on measurements made in the free-field. Measurements made in a harbor environment, where multiple reflections, high background noise and short propagation paths are typical, violates these conditions. The subject of this paper is estimation of source location and source level from such measurements. Data from a test conducted at the US Navy Acoustic Research Detachment in Bayview, Idaho during the summers of 2010 and 2011 are analyzed. A line array of omnidirectional hydrophones was deployed from a barge in both deep and shallow water using calibrated acoustic sources to evaluate the effectiveness of post-processing techniques, as well as line array beamforming, in minimizing reflected path contributions and improving signal-to-noise ratio. A method of estimating the location of the sources while taking into account a real, non-linear array based on these measurements is presented. [Work supported by the Applied Research Labor...

Overview of Signal Processing in Acoustics

The Signal Processing Technical Committee (SPTC) of the ASA provides a forum for discussion of signal processing techniques that transcend one acoustic application. Signal processing research typically presented at ASA meetings includes techniques that show promise in one application—say underwater acoustics—but may also have application to other areas, for example, speech processing or room acoustics. There are several good reasons to get involved in the SP TC. First, since signal processing is an important aspect of many acoustic research areas, you will have the opportunity to better understand new and potentially useful tools. Second, Signal Processing is a small technical committee and you can make an immediate contribution. This talk provides an overview of some of the current topics in Signal Processing.

Exploiting differences in underwater acoustic signal and noise distributions to improve signal detection in low signal-to-noise ratio

Traditional models for acoustic signals and noise in underwater detection utilize assumptions about the underlying distributions of these quantities to make algorithms more analytically and computationally tractable. Easily estimated properties of the signal, like the mean amplitude or power, are then calculated and used to form predictions about the presence or absence of these signals. While appropriate for high SNR, quantities like the mean amplitude may not give reliable detection for SNR at or below 0 dB. Fluctuation based processors, utilizing additional statistics of received pressure, offer an alternative form of detection when features of the received signal beyond changes in mean amplitude are appreciably altered by the presence of a signal. An overview of fluctuation based processing will be given, with a focus on the underlying statistical phenomena that grant this method efficacy. Work sponsored by the Office of Naval Research in Undersea Signal Processing.

Modeling Correlation for Passive Sonar Bayesian Localization Techniques

Low frequency acoustic signals in shallow water are strongly affected by interference between multiple paths resulting from boundary interactions. As an acoustic source or receiver moves through this interference pattern, the spatial variation in transmission loss can result in strong temporal modulation of the received signal, which can be used to localize the source. Acoustic propagation models can produce accurate transmission loss predictions, but are sensitive to ocean environmental parameters such as bottom composition, bathymetry and sound speed profile. If the uncertainty in the undersea environment can be described by probability density functions of these parameters, Monte Carlo forward models can be used to produce an ensemble of possible transmission loss realizations. A probabilistic model representing this ensemble must include a density function of transmission loss at each location, as well as correlation of transmission loss between locations. In addition, the choice of probabilistic mode...