Abstract

An alternating optimization algorithm is presented and analyzed for identifying low-rank signal components, known in factor analysis terminology as common factors, that are correlated across two multiple-input multiple-output (MIMO) channels. The additive noise model at each of the MIMO channels consists of white uncorrelated noises of unequal variances plus a low-rank structured interference that is not correlated across the two channels. The low-rank components at each channel represent uncommon or channel-specific factors.

Highlights

  • Standard second-order factor analysis aims to identify a low-rank plus diagonal covariance model from multiple realizations of a multivariate measurement

  • In [6], multi-channel factor analysis has been applied to the problem of detecting correlation between two multiple-input multiple-output (MIMO) channels, and applied to passive radar

  • The resulting problem of identifying common factors between the two MIMO channels leads to a maximum likelihood problem

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Summary

INTRODUCTION

Standard second-order factor analysis aims to identify a low-rank plus diagonal covariance model from multiple realizations of a multivariate measurement. The diagonal covariance component models additive noise, or errors in measurements This problem may be extended to multiple channels of multivariate measurements, as in the inter-battery work of Tucker [4] and Browne [5]. We further generalize these results by allowing for low-rank plus diagonal correlation models for the additive noise in two MIMO channels. The resulting problem of identifying common factors between the two MIMO channels leads to a maximum likelihood problem. This problem is solved with an alternating optimization algorithm, consisting of three steps per iteration. Simulation results demonstrate the efficacy of the method for identifying and tracking both common and uncommon factors

Single-channel FA
Two-channel FA
ML approach
ALTERNATING OPTIMIZATION ALGORITHM
Step 1
Step 2
Step 3
Initialization and convergence
SIMULATION RESULTS
CONCLUSIONS

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