Radar technology has evolved into a versatile and robust tool for critical air traffic control, meteorology, surveillance, and defence applications. In surveillance radar, the need for continuous monitoring of large areas, often cluttered by ground or sea reflections, presents significant challenges for multitarget detection. This clutter can obscure true targets, complicating detection in environments where standard radar noise assumptions fall short.

This thesis introduces a novel implementation based on the relevance vector machine (RVM) to address the complexities of multitarget detection in cluttered environments. Unlike conventional approaches that assume white Gaussian noise, the proposed method jointly estimates a clutter covariance matrix, allowing it to adapt to the estimated clutter model over subsequent iterations. Performance evaluations using simulated data in one-dimensional (range or angle) and two-dimensional (range-angle) settings demonstrate that the framework achieves accurate AR parameter estimation. Results indicate a marked improvement in reducing false and missed detections compared to the white-noise-based model. Notably, the framework performs multitarget detection without prior knowledge of target locations and the need for guard cells, underscoring its adaptability to real-world scenarios.