Model based adaptive detection algorithm 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. To solve this problem, we have modified a well known adaptive detector (Kelly's GLRT) in four different forms. In this detector an estimation of covariance matrix is needed. In order to estimate the covariance matrix, we estimate the AR parameters based on secondary data and use the results in covariance matrix estimation. Then, we use the estimated matrix in the detector structure. In order to estimate the AR parameters using more than one set of data, we have extended four classical AR parameter estimation techniques to use more data sets. The performance of the proposed detectors have been evaluated using Monte-Carlo simulations and compared with each other.