Model based adaptive detector with low secondary data support

In this paper the problem of adaptive target detection in structured Gaussian clutter is considered. The clutter is modeled as an auto-regressive process with known order but unknown parameters. Here a detection algorithm which can manage the cases with few secondary data is proposed. We have modified a well known adaptive detector in four different forms. First of all, we estimate the AR parameters based on secondary data and use the results in covariance matrix estimation. The performance of the proposed detectors have been evaluated using Monte-Carlo simulations and compared with each other.