Knowledge-aided adaptive detection with multipath exploitation radar

Radar is a system that performs functions such as detection, tracking and imaging about the target and region by sending electromagnetic waves to the interested target or region and receiving reflecting electromagnetic waves from the target. Radar consists of many elements and one of these elements is the detector. Detector decides on the target detection once it receives the reflected electromagnetic waves from the target. Depending on the effects of unwanted external signals and the noise in the environment, detectors may misoperation and have huge performans loss. Therefore, designing an adaptive detector that can operate under harsh conditions is of great importance in modern electronic warfare systems. In this thesis, unlike existing adaptive detectors in the literature, a new approach to exploit multipath returns for adaptive target detection is proposed and a new detector that exploits multipath returns, estimates the unknown noise parameters of the environment and adjust itself accordingly is designed. The performance of the obtained detector is compared with various existing adaptive detectors in terms of probability of detection. It is shown that better detection performance is achieved when there is a significant resolvable multipath returns.