Array signal processing in the known waveform and steering vector case

The amplitude estimation of a signal whose waveform is known (up to an unknown scaling factor) in the presence of interference and noise is of interest in several applications including using the emerging quadrupole resonance (QR) technology for explosive detection. In such applications a sensor array is often deployed for interference suppression. This paper considers the complex amplitude estimation of a known waveform signal whose array response is also known a priori. We study a practical scenario where the interference and noise is both spatially and temporally correlated. We model the interference and noise vector as a multichannel autoregressive (AR) random process. A cyclic iterative ML (IML) method is presented. We show that in most cases the IML method is superior to its simple ML counterpart that ignores the temporal correlation of the interference and noise.