Polarization-Angle-Frequency Estimation With Linear Nested Vector Sensors

This paper considers the problem of multiple dimensional parameter estimation of radar signals using a linear nested vector sensor array. We propose a computationally efficient polarization-angle-frequency estimation algorithm based on spatial-temporal nested sampling. Radar cross-sections diversity in multiple coherent processing intervals is exploited to construct a virtual polarization-spatial-temporal manifold with extended degrees of freedom. Then, a computational efficient method without eigen-decomposition is derived to estimate Khatri-Rao signal subspace. Automatically paired polarization, azimuth-elevation angles, and doppler frequency estimates are finally obtained by exploiting the idea of the estimation of signal parameters via rotational invariance techniques algorithm. The effectiveness of the proposed method is verified through numerical examples.