Parametric detection in non-stationary correlated clutter using a sequential approach

Sequential detection provides a powerful solution to minimize the required number of observations for a given performance. Due to the non-stationary nature of clutter, this problem is recurrent in radar applications. In this paper, we develop a sequential parametric adaptive detection algorithm based on the approximation of clutter as an autoregressive process. Stationary segments are considered where both space and time windows are minimized, respectively, by using one secondary cell on each side of the cell under test and by applying a sequential test. We derive the distributions of the considered test statistic and give a closed form expression for the upper threshold whereas, the lower one is given as a simply numerical solution of a proper equation, rather than use the commonly Monte Carlo method based ones. The proposed approach is compared to an existing method based on a fixed sample size. Results obtained using synthetic and real data show that the proposed scheme reduces substantially the required sample size with detection performance close to that of the fixed sample size method.