Range-spread target detection in white Gaussian noise via two-dimensional non-linear shrinkage map and geometric average integration

摘要: In this study, a practically efficient method is proposed to detect range-spread targets of manoeuvring flight in white Gaussian noise. High-resolution range profiles (HRRPs) from multiple consecutive pulses are stacked into two-dimensional (2D) greyscale range-pulse image along cells and pulses. Based on the local statistics images, 2D non-linear shrinkage map designed for reducing noise while preserving target echoes before integration. Then, geometric average (GA) integrator, operating filtered HRRPs, detection. The GA integration exploits waveform similarity HRRPs. Finally, experimental results raw radar data comparisons with other detectors reported, showing that detector can efficiently