Design of a Digital Array Signal Processing System with Full Array Element

Xiao, Guoyao; Quan, Yinghui; Sun, Zongzheng; Wen, Bo; Liao, Guisheng

doi:10.3390/rs15164043

Cited by 1 publication

(1 citation statement)

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“…Direction of arrival (DOA) estimation is a widely studied topic in the signal processing area, which performs a key role in wireless communications, astronomical observation, and radar applications [1][2][3][4][5]. The conventional beamforming (CBF) method is a classical solution for DOA estimation.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Deep Neural Network with Data Redundancy Removal and Regression for DOA Estimation in Sensor Array

Liu,

Guo,

Arnatovich

et al. 2024

Remote Sensing

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In this paper, a lightweight deep neural network (DNN) for direction of arrival (DOA) estimation is proposed, of which the input vector is designed to remove data redundancy as well as remaining DOA information. By exploring the Vandermonde property of the steering vector of a uniform linear array (ULA), the size of the newly designed input vector is greatly reduced. Furthermore, the DOA estimation is designed as a regression problem instead of a classification problem; that is, the lightweight DNN designs the output vector as the estimated DOAs of sources, of which the size is much shorter than that of the spatial spectrum used as the output vector in the conventional DNN. The reductions in the sizes of input and output vectors lead to a reduction in the sizes of hidden layers, achieving lightweightness of the neural network. The analysis illustrates that when the number of sensors is 22, the number of parameters in the lightweight DNN is three orders of magnitude less than that in the conventional DNN. The simulation results demonstrate the lightweight DNN can provide high DOA estimation accuracy with the shortest testing time. It performs better than the conventional DNN. Furthermore, it is superior to traditional solutions such as the multiple signal classification (MUSIC) method and conventional beamforming (CBF) method in harsh conditions like low signal-to-noise ratios (SNRs), closely spaced sources, and few snapshots.

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