Summated Singular Vectors Based Passive Bi-static RADAR Detection

Target detection is a fundamental problem in radar systems. One of the most common techniques used in passive radars for detection is Cross-correlation of data received from the surveillance channel and the reference channel. However, the optimality of this kind of detection gets shaky when the reference channel is noisy. Here in this research, singular vectors based detector has been proposed by using the combination of all the singular vectors present in the received data matrices. Furthermore, a simple closed-form expression is derived for the threshold of the detector and compared with the results obtained from computational simulation using the Monte-Carlo method. Moreover, as the proposed detector is a random matrix theory (RMT) inspired detector, so the performance of this detector which we will now call Summated Singular Vectors based Detector (SSVD) is also compared with other RMT based detectors. Our results show improved detection probability and hence an improved performance as compared to existing known methods.