Fast DOA Estimation Based on the Transform Domain Weighted Noise Subspace Fitting Algorithm for Generalized Sparse Array

• Published in: International journal of antennas and propagation Vol. 2021; pp. 1 - 10
• Main Authors: Liu, Chunxi, Chen, Zhikun, Peng, Dongliang
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2021; John Wiley & Sons, Inc; Wiley
• Subjects: Accuracy; Algorithms; Antennas; Arrays; Computer simulation; Domains; Eigenvalues; Genetic algorithms; Noise; Parameter estimation; Signal classification; Unmanned aerial vehicles
• ISSN: 1687-5869; 1687-5877; 1687-5877
• DOI: 10.1155/2021/9952575

Compared with uniform arrays, a generalized sparse array (GSA) can obtain larger array aperture because of its larger element spacing, which improves the accuracy of DOA estimation. At present, most DOA estimation algorithms are only suitable for the uniform arrays, while a few DOA estimate algorithms that can be applied to the GSA are unsatisfactory in terms of computational speed and accuracy. To compensate this deficiency, an improved DOA estimation algorithm which can be applied to the GSA is proposed in this paper. First, the received signal model of the GSA is established. Then, a fast DOA estimation method is derived by combining the weighted noise subspace algorithm (WNSF) with the concept of “transform domain” (TD). Theoretical analysis and simulation results show that compared with the traditional multiple signal classification (MUSIC) algorithm and the traditional WNSF algorithm, the proposed algorithm has higher accuracy and lower computational complexity.