Colin W. Jemmott

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

Single-Hydrophone Model-Based Passive Sonar Source Depth Classification

This paper introduces a source depth classification technique applicable to passive low-frequency narrowband sonar signals received by a single hydrophone in shallow water. The classifier is based upon Rice probability density functions with model-derived parameters for the received amplitude of a tonal signal that has been modulated by propagation through the ocean. Differences in modal excitation due to source depth result in different probability density functions of received amplitude that allow for source depth classification. The performance of the technique is demonstrated using data from the SWellEx-96 experiment.

The impact of reverberation on active sonar optimum frequency

For many active sonar problems there exists an optimum frequency for detection. In this paper, the optimum frequency is defined as the frequency which maximizes the signal excess of a monostatic active sonar system in shallow water. The optimum frequency is a function of range and can be calculated using the sonar equation which includes several frequency dependent terms. Active sonar in shallow water may be reverberation limited rather than noise limited, which impacts the optimum frequency. Standard Lambert bottom interface scattering strength models are frequency independent, however more accurate data-derived bottom backscatter strength models are frequency dependent. The bottom scattering strength model choice is important for determining the optimum frequency. Finally, volume reverberation due to biological activity (scattering from fish) has a complicated frequency dependence, which changes the optimum frequency. In addition to calculating the optimum frequencies for these cases, this work also qua...

An information theoretic performance bound for passive sonar localization of a moving source

Several passive sonar signal processing methods have previously been developed for determining the location of a source radiating tonal acoustic energy while moving through a shallow water environment. These localization algorithms rely on the complex interference pattern resulting from multipath acoustic propagation. By treating passive sonar localization as a communications problem, an information theoretic upper bound on performance can be derived. The bound is based on acoustic propagation, and depends on radial distance the source travels through the waveguide, signal to noise ratio, frequency of the radiated acoustic tone, and minimum sound speed of the problem, and resolution of the localization. An example using parameters from the SWellEx-96 experiment is shown.

Survey of Ambient Noise in Aquariums

Owning and maintaining an aquarium is a common hobby, but some aspects of proper animal husbandry in the hobby community have received little scientific attention. Specifically, the ambient noise in aquariums resulting from pumps, filters, bubblers and other equipment is not well studied, yet elevated ambient noise levels have been shown to adversely affect fish and marine invertebrates. Anecdotal evidence suggests that this may be a problem in aquariums as well. Salt water aquariums designed to maintain coral reefs require high water flow and pristine water conditions, which in turn require pumps and filters that contribute to underwater noise. A survey of ambient noise in both fresh and saltwater aquariums ranging in size from 10 to 500 gallons was conducted. The aquariums differed in construction material, number, size and type of pumps, and presence of other equipment, and their ambient noise broadband levels are shown to vary widely.

What do cognitive models and human judgments suggest about the desired structure of automatic classifiers

Past studies probed human listeners’ efficacy at classification of impulsive sonar echoes by using paired‐comparison ratings to measure perceptual dissimilarity. Interpreting these ratings requires a cognitive model comprising both a representation of the stimuli and a process operating on that representation. Initially, perceived dissimilarities were represented as distances in Euclidean space via multidimensional scaling. This assumed a continuous and spatial cognitive representation and proved difficult to relate to a linear vector space of features. Later work by the authors suggested a discrete (categorical) cognitive representation may better reflect perception of these stimuli. While similarity‐based classifiers can bypasses feature extraction [S. Philips and J. Pitton, J. Acoust. Soc. Am. 123, 3344 (2008) (A)], the form of the similarity measure reflects the assumed cognitive representation [L. Cazzanti and M. R. Gupta, Proc. IEEE Intl. Symposium Info. Theory, pp. 1836–1849 (2006)]. Here, findings...

The impact of bottom backscatter strength modeling on active sonar optimal frequency calculations.

For many active sonar problems, there exists an optimal frequency—one at which detection range maximized. The optimal frequency can be calculated using a frequency dependent analytical sonar equation to solve for the detection range, defined as the largest range with positive signal excess. The frequency with the maximum detection range is the optimal frequency. An advantage of this approach is that it combines all of the sonar equation terms into a single quantity. It might seem that such a frequency will not exist because propagation loss tends to increase with increasing frequencies, so performance might decrease monotonically with frequency. However, other terms in the sonar equation are also frequency dependent. For example, Knudsen or Wenz curves show that noise levels generally decrease with frequency, while source level and target strength may be complicated functions of frequency. In shallow water, active sonar may be reverberation limited rather than noise limited, but standard Lambert scatterin...

Improved signal detection in non‐Gaussian deep ocean noise.

DeepSound is an autonomous, high‐bandwidth acoustic recording system designed to profile ambient noise to depths of 9 km. Recent ambient noise measurements recorded by the DeepSound probe well below the conjugate depth of the channel exhibit significant non‐Gaussianity (the hypothesis of Gaussianity is rejected for 97% of 5 s samples using the large sample size corrected Anderson–Darling test at a 95% confidence level). A common sonar signal processing task is the automatic detection of a signal corrupted by ambient ocean noise. The standard solution is matched filtering, which is the optimal detector in the maximum likelihood sense assuming additive white Gaussian noise. Because matched filter is derived under the assumption of Gaussian noise, its performance suffers when implemented in real world non‐Gaussian noise. This paper describes a signal detection algorithm for non‐Gaussian noise based on the generalized Gaussian probability density function. The generalized Gaussian detector is locally optimal ...

Source range and depth estimation using passive sonar, horizontal arrays, and knowledge of the environment.

Target detection, classification, localization, and tracking (DCLT) using horizontal arrays and passive sonar is a well‐studied problem. Good detection results can be obtained using narrow beams that maximize signal to noise ratio. Beamforming also provides azimuth angle, but is generally not reliable for range and depth estimation. Matched field processing (MFP) makes use of environmental knowledge to predict the received signal at the array, and correlates predicted and received signals to estimate target range and depth. However, poor or inaccurate environmental knowledge degrades MFP performance, and in general, MFP suffers from high side lobes or ambiguity in the range/depth probability surface. Received signal amplitude statistics have been used to estimate source depth, but the method has not been studied extensively to date. Recently several methods have been developed involving received signal amplitude statistics predicted using environmental parameter statistics to construct statistically valid...

Comparison of fading statistics for shallow and deep acoustic sources in a continental shelf environment.

Previous work has shown a noticeable difference in the effects of the ocean environment on signals from shallow and deep moving sources. These effects are seen in received amplitude statistics, and such statistics can be used in passive acoustic depth classification. This talk presents a statistical analysis of signals from a September 2007 shallow water acoustic transmission test performed along the continental shelf off the coast of southeast Florida. The data used include low frequency (between 25 and 450 Hz), continuous‐wave signals from a towed source at 100 m depth, as well as tones from surface ships in the area. Using statistical class models in a Minimum Hellinger Distance Classifier, the usefulness of received signal amplitude statistics for passive acoustic source‐depth classification is discussed. [Work supported by ONR Undersea Signal Processing.]