Class Of Signal Processing Algorithms Research Articles

Information-theoretic quantification of underwater acoustic source localization performance

Metrics that historically have been applied to quantify the performance of signal processing for source localization are algorithm-dependent. For example, performance of conventional beamforming or matched-field processing is usually quantified by main-peak width and secondary-peak levels of the beam response or spatial ambiguity function, while performance of Bayesian localization may be quantified by measures of the statistical dispersion of the a posteriori pdf of source location. While algorithm-dependent performance metrics permit comparisons within a given class of signal processing algorithms, they do not provide comparability across algorithm classes. The present work identifies fundamental information-theoretic quantities that can be used as metrics to quantify the source localization performance of diverse signal processing algorithms and thus provide for performance comparisons across signal-processor classes. These quantities include conditional entropy of source location given processor output, mutual information of source location and processor output, and cross-entropy of actual and posterior source-location probability distributions. Applications of these information-theoretic metrics are illustrated in examples of Bayesian localization, conventional beamforming, and matched-field processing of a time-harmonic source in a range-independent shallow-water acoustic waveguide. The results are interpreted in the light of the data processing inequality of information theory. [Work supported by ONR.]

The Wagstaff’s integration silencing processor filter: A method for exploiting fluctuations to achieve improved sonar signal processor performance

There is a class of signal processing algorithms that achieves gain by exploiting the amplitude fluctuations in the spectrum analyzed acoustic power. One of those processors is presented. The causes of fluctuations that influence the performance of such processors are discussed, and processed results from a fluctuation-based processor are presented. Gains relative to the average power processor in signal-to-noise ratio (SNR) of as much as 10 dB, and gains in spatial and spectral resolution, minimum detectable levels, and other measures of processor performance have been achieved. For the most part, the additional gains from exploiting fluctuations are independent of the frequency resolution and the array aperture (or number of elements if the noise is “white”) and depend on the number of averages and percent of overlap of consecutive time-to frequency fast Fourier transforms (FFT).

Environmentally sensitive adaptive fluctuation-based processing

Signals that propagate from submerged sources to submerged receivers in the ocean generally interact less with the fluctuation generation mechanisms near the sea surface, e.g., internal and sea-surface waves, than signals originating near the surface, e.g., surface ship clutter and sea-surface noise. Therefore, submerged source signals generally have smaller fluc- tuations than clutter signals and noise. The differences in amplitudes of fluctuations have been used to devise a class of signal processing algorithms that provides preferential gains for signals having fluctuation amplitudes less than those of clutter and noise. Gains include increases in signal-to-noise ratio, clutter suppression, and unalerted automatic detection. The AWSUM filter is an example of such a processor [R. A. Wagstaff, IEEE J. Oceanic Eng. 22(1) (1997)]. Similar gains can be achieved by exploiting the order dependence of fluctuation amplitudes. By combining amplitude dependence and order dependence, an algorithm has been devised that is both sensitive to fluctuations and adapts to the input data. The resulting algorithm is designated the AWSUM Environmentally Sensitive Adaptation (ESA). The AWSUM ESA algorithm is described and results from processing real data are presented. [Work supported by ONR and NRL.]