Date of Award

Spring 2026

Document Type

Open Access Thesis

Department

Electrical Engineering

First Advisor

David W. Matolak

Abstract

This thesis provides a statistically based method of simulating K- distributed sea clutter for littoral X-band radar object detection. Littoral radar systems must detect small objects in cluttered environments dominated by return from the sea surface. Accurate modeling of sea clutter is fundamental for improving radar detection performance. The clutter synthesizer presented here is an effort to enable computer modeling of realistic sea clutter even with restricted knowledge of sea clutter behavior.

The K-distribution is widely used to model sea clutter in X-band radar systems. Its parameters 𝜈 and 𝑏 are closely tied to third and fourth statistical moments and the sea surface produces time-correlated radar returns.

This motivates the work in this thesis, which develops a MATLAB-based simulation framework that generates (synthesizes) time-correlated K-distributed samples with controllable statistical properties. Time correlation is induced via a sample-sorting method that preserves marginal distributions. Distribution parameters 𝜈 and 𝑏 are estimated using a moment-matching approach based on skewness and kurtosis. Validating this simulation requires real-world sea clutter which was collected on an expedition using a commercial X-band marine radar.

The statistical properties of the synthesized clutter were compared to the experimentally gathered radar data via histogram shape, the first four statistical moments, and autocovariance behavior. Parameter estimation was validated by an average error of ~6% for 𝜈 and ~13.5% for 𝑏. Despite these estimation errors, synthesized clutter closely matches radar data in statistical moments, histogram shape, and autocovariance behavior. Mean-, variance-, and skewness-matching had errors under 3%, while kurtosis-matching the largest deviation (~14%) due to quantization artifacts during clutter extraction. Time correlation matched within one sample.

This study also revealed that the estimated K-distribution shape parameter 𝜈 can exceed commonly assumed ranges when derived from experimentally collected radar data. These results suggest that statistically validated clutter synthesis is a viable tool for evaluating and improving littoral radar detection performance.

Rights

Β© 2026, Hudson Richard Dye

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