SciFish proposes to utilize Resonance Scattering Theory (RST) modeling to evolve a set of acoustic waveforms that provide optimal material identification while suppressing the affects of clutter and noise. Each waveform will be defined by its bandwidth, pulse width and signal type. Signal types will include FM, Hyperbolic FM, and Phase Shift Key Coded waveforms. An initial set of waveforms will be created from a random selection within each parameter space for each element of the waveform definition. After each of the waveforms has interrogated the target, a figure of merit will be used to rank each solution from best to worst. The best half of the population will be retained; the worst half will be replaced by randomly modifying each of the best half of the population. This process will be repeated until a waveform is evolved that meets our exit criteria. A figure of merit will be derived that emphasizes material identification with simultaneous rejection of clutter.
Keywords: Resonance Scattering Theory, Resonance Scattering Theory, Acoustic Waveform Optimization, Broadband Sonar, Evolutionary Computation, Target Identification
