Adaptive range-spread maneuvering target detection in compound-Gaussian clutter

Adaptive detection of range spread maneuvering target embedded in compound-Gaussian clutter is an important challenge for radar engineers. For the long integration, maneuvering target suffers from inevitable range walks across cells as well as unpredictable phase change at individual range cells. Therefore, the traditional adaptive normalized matched filter detectors are ineffective in long integration duration. In this paper, combining adaptive normalized matched filter with sliding high-order cross-correlation integration, a new adaptive range-spread target detector is proposed. Firstly, the long integration duration is segmented into short disjoint subintervals. In each subinterval, it is assumed that no range walking across cells happens and target's complex returns accord with the traditional rank-one parameter models. In each subinterval, the coherent integration output vector is obtained by the adaptive normalized matched filter along the slow-time dimension in different range cells. If a target is present, the coherent integration outputs of each subinterval share similar waveforms except for unknown range walks. Otherwise, when a target is absent, the coherent integration output vectors estimated from individual subintervals are uncorrelated positive random vectors without similarity. Further, the sliding high-order cross-correlation integration of these coherent integration output vectors is calculated for target detection. The new detector combines the short-time coherent integration and long-time similarity integration. The experimental results using raw radar target data and simulated clutter data show that the new detector achieves better detection performance for range-spread maneuvering targets than the existing detectors.