Abstract
The rational vector space generalization of the signal subspace approach is presented and applied to the estimation of multiple wide-band emitter locations from the signals received at multiple sensors. The signal subspace and array manifold concepts first introduced by Schmidt are generalized to rational vector space. These concepts are used to develop the rational signal subspace theory and prove the signal subspace theorem, on which signal subspace algorithms are based. The theory is applied in discrete time to derive a class of rational signal subspace algorithms for source location and spectral estimation using unit circle eigendecomposition of multivariate rational models of sensor outputs. Simulation results are presented for an algorithm in this class, including sample statistics from Monte Carlo trials and comparisons with the Cramer-Rao bound.
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