In wireless communications, including cellular communication systems, spread spectrum overlay systems, and signals intelligence applications, the degradation caused by rapidly time-varying multipath and unknown co-channel interference can be reduced by adaptive spatial filtering using antenna arrays. In this paper we propose a flexible framework for adapting a spatial filter to extract signals of interest (SOIs) while rejecting signals not of interest (SNOIs) without using a training signal, array calibration data, or knowledge of spatial characteristics of the desired or interfering signals. The generic method within this framework exploits user-selected properties of the SOIs and/or SNOIs to transform the received data set into a new set in which the SOIs are more highly correlated between the two sets than are the SNOIs. The framework then applies the method of canonical correlation analysis to the original and transformed data sets to obtain a linear combining weight vector that performs the spatial filtering. Simulation results illustrate the performance of algorithms developed within the new framework.>

An algorithm for the estimation of time difference of arrival (TDOA) of cyclostationary signals is presented. The algorithm is derived as a generalization of the SPECCOA algorithm. Simulation results indicate that the performance of the new algorithm can greatly exceed that of SPECCOA.>

The Cramer-Rao lower bound on the variance of estimates of directions of arrival, signal strengths, and carrier frequencies and phases of cyclostationary signals arriving at an array is considered. Numerical evaluations of the bound for the estimates of directions of arrival show that it can be orders of magnitude lower than the corresponding bound for stationary signals, despite the great number of unknown parameters in the former case. Comparisons with the mean squared error of direction estimates obtained by the cyclic least squares algorithm show that the bound is not overly optimistic.>

An algorithm that combines the signal selectivity of cyclic MUSIC with the ability to function well in the presence of fully correlated multipath is presented. Analytical results and computer simulations are presented to support the claims, which include the ability to estimate directions of arrival of desired signals in the presence of multipath and arbitrary numbers of arbitrarily closely spaced interfering signals. Furthermore, the algorithm can estimate the number of spectrally correlated signals with any particular cycle frequency, and it implicitly obtains maximum signal-to-interference-plus-noise ratio (SINR) estimates of those signals.>

A new approach to blind adaptive signal extraction using narrowband antenna arrays is presented. The approach has the capability to extract communication signals from cochannel interference environments using only known spectral correlation properties of those signals, i.e. 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 by analysis and simulation that these algorithms maximize the signal-to-interference-and-noise ratio at the output of the narrowband antenna array when a single communication signal with spectral self-coherence at a known value of frequency separation, along with an arbitrary number of interferers without spectral self-coherence at that frequency separation, are impinging on the array.

Signal-selective direction-finding algorithms that overcome many of the limitations of existing techniques are presented. The algorithms automatically classify signals as desired or undesired on the basis of their known spectral correlation properties and estimate only the desired signals' directions of arrival. The signal-selective nature of the techniques eliminates the need for knowledge of the characteristics of the noise or interference in the environment and makes it possible to resolve a number of desired signals not exceeding the number of sensors in the presence of arbitrary noise and a virtually unlimited number of unknown interferers. For example, the interferers can exhibit an arbitrarily high degree of correlation among themselves and can arrive from directions arbitrarily close to those of the desired signals.>