DOA Estimation of Uncorrelated, Partly Correlated and Coherent Signals Using Alternating Oblique Projection

Hou, Huijun; Mao, Xiaowei

doi:10.13164/re.2017.0588

Cited by 3 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, the new method is suitable for any array structure without reducing the effective array aperture. Document [7] proposes an AOP&AP-DOA algorithm for coherent and incoherent mixed signals. The algorithm combines AOPDOA and AP-MLE to reduce the computational complexity of AOP-DOA.…”

Section: Introductionmentioning

confidence: 99%

Two-dimensional DOA estimation of coherent signals for MIMO Radar Based on tensor decomposition

Ren

Hao

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

This paper studies the two-dimensional DOA estimation of coherent targets. Subspace algorithm is usually used to solve this problem. Compared with subspace algorithm, tensor decomposition has significant advantages in estimation accuracy and has been widely used in the field of signal processing. However, the research on tensor decomposition algorithm mainly focuses on incoherent targets. Therefore, we study how to use the advantages of tensor decomposition to improve the estimation accuracy of coherent targets. Firstly, we construct the echo signal model corresponding to the “Sum array” of MIMO radar, and then use the spatial smoothing to process the data to solve the problem of covariance rank deficiency. Then a third-order tensor is constructed according to the position relationship of the data. Finally, the DOA can be estimated from the decomposed tensor according to the position relationship of the data. Simulation results show that proposed algorithm has significant advantages in estimation accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Two-dimensional DOA estimation of coherent signals for MIMO Radar Based on tensor decomposition

Ren

Hao

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…The advantage of the proposed scheme is that it does not require knowing the source number information. In Hou and Mao, 8 an accurate DOA estimation algorithm is presented to estimate the DOAs of uncorrelated, partly correlated, and CS, and the proposed strategy is based on alternating oblique projection. Yang et al 9 introduce the sparse reconstruction theory to DOA estimation of incoherently distributed sources and propose two DOA estimation methods of these sources based on different sparse representations.…”

Section: Introductionmentioning

confidence: 99%

Direction of arrival estimation in multipath environments using deep learning

Harkouss

2021

Int J Communication

View full text Add to dashboard Cite

This article aims to present a novel direction of arrival (DOA) estimation strategy in multipath environments using deep learning. Eigen decompositionbased algorithms, such as multiple signal classification (MUSIC), have highresolution DOA estimation performance, but they fail to work in the case of coherent signals. These algorithms require extensive computation and are difficult to implement in real time. Neural networks (multilayer perceptron [MLP] and radial basis function neural network [RBFNN]) are also applied to DOA estimation problem, and they are found to be faster than the conventional techniques, but they fail to ensure the desired accuracy in multipath environments when the training data set contains a huge number of samples and the number of incident signals was unknown. To enhance the DOA estimation performance, an efficient convolutional neural network (CNN)-based smart antenna is proposed. This smart antenna is composed of a uniform linear array (ULA), an intelligent DOA estimator, and an efficient adaptive beamformer, and the space is decomposed into five space sectors. The intelligent DOA estimator contains six CNN networks. One network is used as a classifier to select one or more space sectors, and five networks are used to calculate the DOAs of unknown number of coherent or noncoherent incident signals that received in the selected space sectors. The simulation results demonstrate that the proposed DOA estimator enables reliable DOA estimation despite very challenging multipath environment, and the CNN substantially reduces the CPU time for the DOA estimation computations especially when the number of incident signals is large.

show abstract

An Efficient Maximum-Likelihood-Like Algorithm for Near-Field Coherent Source Localization

Cheng

Liu

et al. 2022

IEEE Trans. Antennas Propagat.

View full text Add to dashboard Cite

DOA Estimation of Uncorrelated, Partly Correlated and Coherent Signals Using Alternating Oblique Projection

Abstract: Abstract. The problem of direction-of-arrival (DOA) estimation is important in array signal

Cited by 3 publications

References 43 publications

Two-dimensional DOA estimation of coherent signals for MIMO Radar Based on tensor decomposition

Two-dimensional DOA estimation of coherent signals for MIMO Radar Based on tensor decomposition

Direction of arrival estimation in multipath environments using deep learning

An Efficient Maximum-Likelihood-Like Algorithm for Near-Field Coherent Source Localization

Contact Info

Product

Resources

About