DOA Estimation Using Second-Order Differential of Invariant Noise MUSIC (SODIN-MUSIC)

Abstract

Sensor arrays are widely used to locate the radiating sources by estimating the direction of arrival (DOA) of their incident signals. Among the most well-known methods employed for DOA estimation is the multiple signal classification (MUSIC). MUSIC provides outstanding performance in estimating DOAs in good propagation environments; however, it fails to estimate the DOAs in severe environments like low signal-to-noise ratio, small number of snapshots, when the incident waves are coming from close angles or when the sources are highly correlated. In this paper, we propose a new approach for DOA estimation with super-resolution capabilities and high accuracy even in the severe environments. The proposed approach is based on computing the second-order differential of invariant noise MUSIC (SODIN-MUSIC) spectrum. SODIN-MUSIC benefits from extracting useful information provided by the second-order differential of MUSIC spectrum and also utilizing the invariance property of noise subspace with respect to changing the power of the emitted signals. In this paper, the proposed SODIN-MUSIC method is developed to estimate both one-dimensional and two-dimensional DOA with any arbitrary array configurations. The simulation results show that SODIN-MUSIC yields superior performance in all distinct environments if compared to both conventional MUSIC and other newly developed MUSIC-based methods.