Coarray Polarization Smoothing for DOA Estimation with Coprime Vector Sensor Arrays

Wang, Yijing; He, Jinhai; Shu, Ting; Truong, Trieu‐Kien

doi:10.1007/s00034-022-02275-1

Cited by 4 publications

(1 citation statement)

References 51 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a study on the 2D DOA estimation of sparse arrays, Liu et al proposed a 2D DOA estimation method based on singular value decomposition [8], taking advantage of the structural characteristics of T-shaped arrays and co-prime array arrays to obtain three signal subspaces without using virtual elements before using the signal subspaces to perform 2D DOA estimation. Wang et al designed a generalized coprime parallel linear array instead of the traditional parallel uniform linear array, then improved the differential virtual array to obtain greater degrees of freedom, and finally simplified the 2D search to two 1D searches to reduce the number of operations [9]. However, the algorithm led to an increase in the influence of the mutual coupling between array elements, and the compression factor needed to be artificially chosen, restricting the performance of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Two-Dimensional Direction of Arrival for Low-Elevation and Non-Low-Elevation Targets Based on Dilated Convolutional Networks

Zhao

Liu

2023

Remote Sensing

View full text Add to dashboard Cite

This paper addresses the problem of the two-dimensional direction-of-arrival (2D DOA) estimation of low-elevation or non-low-elevation targets using L-shaped uniform and sparse arrays by analyzing the signal models’ features and their mapping to 2D DOA. This paper proposes a 2D DOA estimation algorithm based on the dilated convolutional network model, which consists of two components: a dilated convolutional autoencoder and a dilated convolutional neural network. If there are targets at low elevation, the dilated convolutional autoencoder suppresses the multipath signal and outputs a new signal covariance matrix as the input of the dilated convolutional neural network to directly perform 2D DOA estimation in the absence of a low-elevation target. The algorithm employs 3D convolution to fully retain and extract features. The simulation experiments and the analysis of their results revealed that for both L-shaped uniform and L-shaped sparse arrays, the dilated convolutional autoencoder could effectively suppress the multipath signals without affecting the direct wave and non-low-elevation targets, whereas the dilated convolutional neural network could effectively achieve 2D DOA estimation with a matching rate and an effective ratio of pitch and azimuth angles close to 100% without the need for additional parameter matching. Under the condition of a low signal-to-noise ratio, the estimation accuracy of the proposed algorithm was significantly higher than that of the traditional DOA estimation.

show abstract

Section: Introductionmentioning

confidence: 99%