Optimizing Sensor Array DOA Estimation With the Manifold Reconstruction Unitary ESPRIT Algorithm

This letter introduces a new algorithm called manifold reconstruction unitary ESPRIT (MR-UESPRIT) for accurately estimating the direction of arrival (DOA). The algorithm combines manifold reconstruction and unitary ESPRIT to overcome the limitations of traditional DOA estimation methods. In this approach, two smaller overlapping subarrays are used to derive a rotational invariance equation, which helps recover the array manifold matrix. As a result, the DOA estimation problem is divided into $K$-individual single-source DOA estimation problems. Each DOA is estimated by minimizing a nonlinear least squares fitting criterion using a computationally efficient Newton's method. Computer simulations are presented to demonstrate the effectiveness of the MR-UESPRIT method compared with standard DOA estimation methods.

I. Introduction

In numerous critical applications, spanning radar, sonar, wireless communication systems, and sensor arrays, the fundamental challenge at hand revolves around the estimation of the direction of arrival (DOA). Over the years, a myriad of innovative approaches has been developed, each with a shared mission: to provide high-resolution and precise estimates of DOA, all the while effectively managing the computational complexity involved. One pioneering approach introduced by Roy and Kailath [1] is the ESPRIT algorithm. A subsequent advancement by Haardt and Nossek [2] resulted in the unitary ESPRIT (U-ESPRIT) algorithm, enhancing accuracy and decreasing computational load. U- ESPRIT, known for its enhanced accuracy and reduced computational load, finds applications in high-resolution DOA estimation [3], [4] scenarios.