Adaptive radar signal detection in autoregressive interference using Kalman-based flters

The present study deals with adaptive detection of radar target signal with an unknown amplitude embedded in Gaussian interference that has been modeled as an AR process. Application of such a model to the interference decreased the number of parameters to be estimated; therefore, less or even no secondary data were required to obtain a detector with the desired performance. Herein, detection was accomplished based on only the primary data. The authors resorting to the modern Kalman filtering technique developed the conventional GLRT-based detection in the presence of AR interference and proposed two new detectors: AREKF based on extended Kalman filter and ARUKF based on unscented Kalman filter. The performance assessment conducted by Monte Carlo simulation compared the proposed detectors with the existing ones based on the generalised likelihood ratio test and Kalman fillter. The results revealed that the ARUKF detector could signigcantly outperform other detectors in terms of detection for both small number of primary datasets and high Signal-to-Noise Ratio (SNR).