High-Level Adaptive Signal Processing Architecture with Applications to Radar Non-Gaussian Clutter. Volume 4. The Problem of Weak Signal Detection.
Final rept. Apr 91-Jun 94,
MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE
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This investigation is motivated by the problem of weak signal detection in a strong clutter background. The concept of the Locally optimum Detector has been used to address this problem. The problem of weak signal detection has been extensively addressed in the literature when the received radar samples can be modeled as independent and identically distributed. However, this issue has not received much attention when the received radar samples are correlated and have a non-Gaussian probability density function. Also, performance analysis is not generally carried out for finite sample sizes. This thesis addresses the performance of Locally Optimum Detectors in radar weak signal detection for finite sample sizes where the radar disturbance is modeled as a correlated non-Gaussian random process. The theory of Spherically Invariant Random Process is used for statistical characterization of non-Gaussian radar clutter. In particular, the K-distribution and the Student-T distributions have been considered as models for radar clutter. A canonical form is established for the Locally Optimum Detector that is a product of the Gaussian linear receiver and a zero memory nonlinearity. The functional form of the zero memory nonlinearity depends on the approximation used for the underlying radar clutter probability density function. Since the weak signal detector is nonlinear, thresholds for specified false alarm probability cannot be established in closed form. Given a specified false alarm probability a new method for threshold estimation based on extreme value theory is derived that reduces by orders of magnitude the computation and sample size required to set the threshold.
- Active and Passive Radar Detection and Equipment