An Adaptive CFAR Target Detector Based on the Quadratic Sum of Sample Autocovariances

Abstract

In the context of pulse compression radar target detection, this letter assumes that the echo data from each range cell within a coherent processing interval is derived from a stationary random process. We utilize the temporal correlation differences between pulses to determine if a target is present in the cell to be detected. This difference is represented by the quadratic sum of sample autocovariances. We demonstrate the autoregressive-sieve bootstrap validity of this statistic and subsequently design an ordered statistic adaptive constant false alarm rate (CFAR) detector based on this theory. Notably, the proposed detector exhibits a certain degree of generalization to clutter backgrounds, eliminating the need for complex clutter modeling and removing the convoluted process of deriving theoretical threshold. Detection results from measured data indicate that our detector outperforms several matrix CFAR and traditional CFAR methods. Additionally, the detector is not easily affected by the multi-target environment, and can detect the target well.