Polarization Difference Smoothing for Direction Finding of Coherent Signals

The subspace-based two-dimensional direction finding with an array of electromagnetic vector sensors for multiple coherent signals amid unknown noise fields is investigated. The noise can be spatially nonuniform and/or correlated. A novel preprocessing method, called polarization difference smoothing (PDS), is proposed. With PDS the unknown noise is removed by using the difference of pairs of electromagnetic component´s data correlation matrices, and the coherent signals are decorrelated by summing these difference correlation matrices. Unlike most other existing preprocessing techniques, such as spatial smoothing and forward-backward averaging, PDS processing does not decrease the array aperture and is applicable to arbitrary array geometry. As an example a uniform rectangular array is considered, and a computationally-efficient propagator-based algorithm (PDS-propagator) is derived. Monte Carlo simulations demonstrate that, with an appropriately chosen PDS matrix, the PDS-based eigenstructure algorithms can offer better performance than the polarization smoothing-based (PS) counterparts. Incidently in the presence of external interference noise, the PDS-based algorithms can underperform the PS-based algorithms.