Nonlinear adaptive filtering for a jammer application

This paper studies input signals for a discrete-time nonlinear Volterra adaptive filtering for a specific application: the jammer. The jammer´s system corrupts a signal of interest (SOI) by a known reference signal. But in the same time, the jammer estimates the SOI. The signal-to-reference variances ratio must be low, this introduces non-linearities in the jammer´s system. So this problem consists of identifying a non-linear system for a chosen reference. By using a reference signal which excites as few as possible modes of an unknown system, a Volterra adaptive filtering can extract the SOI from the non-linear system driving by the reference signal. The proposed solution is to use a BPSK (binary phase shift keying) cyclostationary reference. This approach leads to a minimal Volterra model, because all non-exciting terms of the model are rejected