Adaptive detection of PN-spread PSK waveforms in HF atmospheric noise

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Title: Adaptive detection of PN-spread PSK waveforms in HF atmospheric noise
Author: Wadsworth, M. A.
Abstract: The purpose of this work is to investigate optimal methods for the detection of short duration (burst) PN-spread PSK waveforms in HF atmospheric noise. As has been shown, the optimal detector for any waveform in Gaussian background noise is a matched filter. However, HF atmospheric noise is non-Gaussian, necessitating alternate detector designs. A theoretical approach to an alternate detector design is taken, based on radar clutter modeling techniques and concepts from detection theory. The industry standard model for HF atmospheric noise is contained in COR Report 322-3 (1986). The CCLR 322 noise model is a graphical, empirical model based on observations of HF atmospheric noise taken over the course of many years at numerous worldwide receive sites. In this work, it is shown that the CQR 322 noise model may be approximated by a random process which is a member of the class of non-Gaussian random processes known as spherically-invariant random processes (SIRPs). This analytical, empirical SIRP representation is then shewn to be identical to the Hall model of impulsive phenomena (1966). In a departure from Flail (who uses his analytical representation to derive an optimal, parametric, coherent detector), the locally optimal, parametric, non-coherent detector is derived In addition, a means to estimate the parameters of the Hall model is provided and is used as the basis for an adaptive, locally optimal, parametric, non-coherent detector design. Monte Carlo simulations are performed to evaluate detector performance, and the results are compared to results obtained using two common, sub-optimal, non-parametric approximations to the locally optimum, parametric, non-coherent detector.
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Date: 1998-10

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