A Novel 2-D DOA Estimation Algorithm with Superior Resolution and Reduced Sidelobes

摘要

We present a 2-D Direction of arrival algorithm whose resolution is superior to that of the subspace class of algorithms and sidelobes are reduced compared to most algorithms. The algorithm is based on the 2-D AR Power Spectral Density (2-D ARPSD) applied to a uniformly spaced data set (space-time) which transforms the space-time data to spatial frequency (wavenumber, which is a function of the direction of arrival) and temporal frequency in a high resolution context. This is done by modeling the sensor array data with a 2-D AR model. The 2-D AR parameters are then used in a specialized form of a 2-D FFT to create an enhanced wavenumber-frequency image. A wavenumber vector for a specific narrowband temporal frequency is extracted and compared to other high resolution algorithm such as MUSIC. Our results exhibit superior performance in low SNR and short sample sized scenarios and when mismatch occurs in the subspace techniques. Our technique also exhibits reduced sidelobes as compared with traditional methods.