Target detection in a nonstationary noise field: a comparison-based approach

By comparing the second-order statistics of the resolution cells under test with those obtained from their neighboring range cells, a detection scheme for detecting a target in a locally stationary two-dimensional random noise field is derived. The assumptions made are: (1) the noise field is uncorrelated in range but correlated in azimuth; (2) the correlation of the data in azimuth can be modeled by a Gaussian autoregressive process of relatively low order. The detection scheme exploits the full information of the second-order statistics in the data to make a detection decision. Its performance is superior to those schemes based on the amplitude or on Doppler shift information only. The proposed scheme is a constant false-alarm rate processor; its threshold can be evaluated by using a central chi-square probability distribution factor and its detection probability can be predicted by a non-central one