Adaptive Radar Signal Detection in Autoregressive Interference using Kalman-based Filters

This paper deals with the adaptive detection of radar target signal with unknown amplitude embedded in Gaussian interference which has been modelled as an AR process. Considering such model for the interference decreases the number of parameters that must be estimated and therefore less or even no secondary data is needed to obtain a detector with desired performance. Herein the detection is based on only the primary data. The authors resorting to the modern Kalman filtering technique develop the conventional GLRT-based detection in the presence of AR interference and propose two new detectors; AREKF based on extended Kalman filter and ARUKF based on unscented Kalman filter. The performance assessment conducted by Monte Carlo simulation compares the proposed detectors with the existing detectors based on generalised likelihood ratio test and Kalman filter. The results show that the ARUKF detector significantly has better detection performance than that of other detectors for the low number of primary data and high signal to noise ratio (SNR).