Parametric Adaptive Subspace 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 (Adaptive Subspace Detector or ASD) in four different forms. First of all, we estimate the AR parameters based on secondary data and use the results in covariance matrix estimation. In order to estimate the AR parameters using more than a 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.

Keywords:

Adaptive Detection , Model Based Detection , AR Parameter Estimation