Statistical Analysis of the Extensive Cancellation Algorithm for Passive Radar Using an Imperfect Reference Signal

Abstract

Passive radar systems have received tremendous attention over the past few decades, due to their low cost and ability to remain covert during operation. Such systems rely on a so-called Illuminator-of-Opportunity (IO), for example, a commercial TV station. We consider a network of Receiving Nodes (RN) without spatial resolution capability, which receives the direct signal and reflections from both stationary objects (clutter) and possible targets. After suitable preprocessing, the RNs transmit information to a Fusion Center (FC) that performs the final target detection, localization and tracking. Several methods for target localization have been proposed in the literature, and our focus is on the seminal Extensive Cancellation Algorithm (ECA). In this approach, each RN collects information about target parameters, while canceling interference using a projection. This is done by exploiting a separate Reference Channel (RC), which captures the IO signal without interference apart from receiver noise. We derive the statistical properties of the ECA parameter estimates under the assumption of a high Signal-to-Noise Ratio (SNR), and we give a sufficient condition for the SNR in the RC to enable statistically efficient estimates. The theoretical results are corroborated through computer simulations, which indicate that the theory agrees well with empirical results under practical operating conditions. The contributions of this paper can be used, for example, to design experimental setups for feasibility studies and to inform system design for achieving a desired localization accuracy.