SPECTRAL SELF-COHERENCE RESTORAL - A NEW APPROACH TO BLIND ADAPTIVE SIGNAL EXTRACTION USING ANTENNA-ARRAYS

A new approach to blind adaptive signal extraction using narrowband antenna arrays is presented. This approach has the capability to extract communication signals from co-channel interference environments using only known spectral correlation properties of those signals—in other words, without using knowledge of the content or direction of arrival of the transmitted signal, or the array manifold or background noise covariance of the receiver, to train the antenna array. The class of spectral self-coherence restoral (SCORE) objective functions is introduced, and algorithms for adapting antenna arrays to optimize these objective functions are developed. Using the theory of spectral correlation, it is shown via analysis and simulation that these algorithms maximize the signal-to-interference-and-noise ratio at the output of a narrowband antenna array, when a single communication signal with spectral self-coherence at a known value of frequency separation and an arbitrary number of interferers without spectral self-coherence at that frequency separation are impinging on the array. It is also shown that the SCORE processors can nearly optimally extract communication signals from environments containing multiple signals with spectral self-coherence at the same value of frequency separation. © 1990 IEEE