The signal processing concept of signal-to-noise ratio (SNR), in its role as a performance measure, is recast within the more general context of information theory, leading to a series of useful insights. Establishing generalized SNR (GSNR) as a rigorous information theoretic measure inherent in any set of observations significantly strengthens its quantitative performance pedigree while simultaneously providing a specific definition under general conditions. In turn, this directly leads to consideration of the log likelihood ratio (LLR): first, as the simplest possible information-preserving transformation (i.e., signal processing algorithm) and subsequently, as an absolute, comparable measure of information for any specific observation exemplar. The information accounting methodology that results permits practical use of both GSNR and LLR as diagnostic scalar performance measurements, directly comparable across alternative system/algorithm designs, applicable at any tap point within any processing string, in a form that is also comparable with the inherent performance bounds due to information conservation.
The design of any new sensing array is a complex endeavor, demanding clear understanding of both signal exploitation options and the underlying noise environment (including ambient, structural, flow and electronic components), including cross-channel characteristics. The goal of this effort is to outline an approach for assessing potential advantages of vector sensors to provide ocean gliders with the acoustic sensory input necessary to execute ISR, characterize the environment, and support other mission-enhancing behaviors. At the heart of the approach is development of a noise audit model (NAM) which (coupled with appropriate signal characterizations) enables realistic and comparable evaluation of optimal processing performance for different classes of sensors (e.g., scalar, vector, or tensor). The NAM is a design tool that permits balanced design of the various array components so that the array output is ambient noise limited in the quietest operating environment. Noise components are broken into different source-transmission path chains; correct transfer functions are applied along each path; and sensor/array outputs are then obtained by incoherent sum. Properly structured, rapid evaluation of performance limits can also be supported. The approach will be discussed, and examples of both NAM components and associated performance evaluations will be presented. [Work supported by ONR.]
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