Fast and Efficient Two-Dimensional DOA Estimation for Signals with Known Waveforms Using Uniform Circular Array

Abstract

This paper addresses the two-dimensional (2D) direction-of-arrival (DOA) estimation issue for signals with known waveforms but unknown amplitudes using uniform circular array (UCA). Unlike maximum likelihood (ML) methods such as decoupled maximum likelihood (DEML), parallel decomposition (PADEC) and so forth, which estimate DOA by spectrum peak search, we propose an efficient interferometer-based method with known waveforms. The proposed method first estimates spatial signature matrix based on ML method whose each column contains 2D DOA information corresponding to a source. Then, an interferometer procedure is performed to obtain 2D DOA estimate of each source from a closed-form solution separately and in parallel. Several simulation results show that the proposed method can achieve a significantly improvement in performance that coincides with the ML method as well as Cramér-Rao Bound (CRB), especially under some poor conditions, such as low SNR, fewer sensors or small snapshots. In addition, the performance will not degrade as the number of sources increases if the source signals are uncorrelated with each other. Meanwhile, it reduces a great amount of computational complexity without loss of too much accuracy. Thus, the proposed method is quite suitable for 2D DOA estimation with known waveforms in practice.

Keywords

• two-dimensional direction of arrival
• known waveform
• uniform circular array
• interferometer
• Cramér-Rao Bound