Adaptive Radar Signal Detection in Gaussian Clutter with Autoregressive Model Using Kalman Fillter

In this paper, we deal with the problem of adaptive coherent signal detection in Gaussian interference (clutter plus noise) for surveillance pulse radars. Some of the adaptive radar detectors exploit the AR model for clutter. Most of these detectors have been obtained using the GLR test. This test relies on the maximum likelihood estimation whose accuracy depends on the number of data. Whereas, the length of data is not too long in practical applications, and because of the recursive nature of AR model, in this paper we suggest two methods for estimating the clutter parameters using the Kalman filter based on the initial and secondary data respectively. According to these methods, we propose two adaptive detectors called ARKD and ARKSD. Our investigations show that the proposed Kalman filter-based detectors improve the detection performance especially in low number data.