# Non-Iterative Subspace-Based DOA Estimation in the Presence of Nonuniform Noise

## TL;DR

This paper introduces a non-iterative, two-phase subspace-based method for DOA estimation that effectively handles non-uniform noise, improving computational efficiency and performance over existing iterative approaches.

## Contribution

The paper proposes a novel non-iterative two-phase approach for DOA estimation in non-uniform noise environments, avoiding convergence issues of prior methods.

## Key findings

- Better DOA estimation accuracy than existing methods
- Reduced computational complexity due to non-iterative process
- Effective handling of non-uniform noise without estimating full covariance

## Abstract

The uniform white noise assumption is one of the basic assumptions in most of the existing directional-of-arrival (DOA) estimation methods. In many applications, however, the non-uniform white noise model is more adequate. Then the noise variances at different sensors have to be also estimated as nuisance parameters while estimating DOAs. In this letter, different from the existing iterative methods that address the problem of non-uniform noise, a non-iterative two-phase subspace-based DOA estimation method is proposed. The first phase of the method is based on estimating the noise subspace via eigendecomposition (ED) of some properly designed matrix and it avoids estimating the noise covariance matrix. In the second phase, the results achieved in the first phase are used to estimate the noise covariance matrix, followed by estimating the noise subspace via generalized ED. Since the proposed method estimates DOAs in a non-iterative manner, it is computationally more efficient and has no convergence issues as compared to the existing methods. Simulation results demonstrate better performance of the proposed method as compared to other existing state-of-the-art methods.