Robust adaptive CFAR detection using AR-sieve bootstrap and sample autocovariance

Abstract

Radar target detection remains a critically important research area. This paper models the radar echo data within each range cell as a stationary linear process driven by reversible, independent and identically distributed innovations. Exploiting the distinct inter-pulse correlation structures present under target absent and target present conditions, we propose the sample autocovariance as the detection statistic. Under appropriate theoretical conditions, we establish the validity of the autoregressive (AR) sieve bootstrap for approximating the distribution of this statistic. Adopting a single-sample hypothesis testing framework, we develop an adaptive constant false alarm rate (CFAR) detector, termed the Sample Autocovariance Trimmed CFAR (SACT-CFAR). Specifically, this method operates as follows: the numerical distribution of the sample autocovariance statistic is derived using the AR-sieve bootstrap method. The detection threshold for the cell under test is then determined based on a predefined false alarm probability. Through comprehensive numerical experiments on both simulated and real-world radar data, we benchmark the SACT-CFAR against established target detection methods. Key advantages of our approach include: 1. Superior Performance: Demonstrates higher detection probability, particularly in challenging low signal-to-clutter ratio regimes; 2. Model-Free Practicality: Eliminates the need for explicit derivation of theoretical detection thresholds and explicit statistical clutter modeling; 3. Robust Generality: Exhibits significant adaptability across diverse clutter environment distributions, overcoming the limitations of detectors reliant on specific clutter assumptions.