High-Accuracy DOA Estimation Algorithm at Low SNR Through Exploiting a Supervised Index

Xu, Kaijie; Xing, Mengdao; Zhang, Rui; Hanyu, E; Sha, Minghui; Nie, Weike; Quan, Yinghui

doi:10.1109/taes.2022.3144121

Cited by 14 publications

(2 citation statements)

References 29 publications

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“…In this study, a joint DOD and DOA detection scheme through signal subspace optimal reconstruction is developed. In the developed scheme, we first use the beamforming [23][24][25] to set an objective space in mathematics for the targets to be detected. Then, we randomly select a collection of DODs and DOAs from the objective space to establish a potential signal subspace with the steering matrix of the MIMO array.…”

Section: Introductionmentioning

confidence: 99%

Joint DOD and DOA detection for MIMO radar based on signal subspace reconstruction and matching

Mao²,

Cui³

2023

Front. Phys.

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In this study, the Direction Of Departure (DOD) and Direction Of Arrival (DOA) of signals detection for Multi-Input Multi-Output (MIMO) radar is discussed. A novel signal subspace reconstruction model to match the signal subspace obtained based on the covariance matrix of the array output is developed to enhance the performance of the DOD and DOA detection. In the developed scheme, the technology of beamforming is first introduced to define an objective space in mathematics for the targets to be detected. By considering the orthogonality between the signal subspace and the noise subspace and defining a reconstruction index of the signal subspace, a multi-dimensional objective function of the DOD and DOA is established. Therefore, the problem of DOD and DOA detection is transformed into an optimization of the multi-dimensional objective function. Subsequently, the Quantum-Behaved Particle Swarm Optimization (QPSO) is employed to optimize the multi-dimensional objective function and to determine an optimal signal subspace. At the same time the DOD and DOA can be fast captured. A series of simulations demonstrate that the proposed method provides significant accuracy improvements in DOD and DOA detection, especially for low signal-to-noise ratio thresholds and small snapshots.

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Section: Introductionmentioning

confidence: 99%

Joint DOD and DOA detection for MIMO radar based on signal subspace reconstruction and matching

Mao²,

Cui³

2023

Front. Phys.

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show abstract

“…In addition, the conditions of bistatic MIMO radar are derived from a two-dimensional point of view. In Xu et al’s study, 14 a new and improved signal subspace model is introduced, and the signal subspace is refined using the model, so as to improve the performance of direction of arrival (DOA) estimation. Jirhandeh et al 15 proposed an effective DOA estimation method for broadband sources based on the concept of super-resolution.…”

Section: Introductionmentioning

confidence: 99%

A reduced dimension multiple signal classification–based direct location algorithm with dense arrays

Zhao

et al. 2022

International Journal of Distributed Sensor Networks

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Aiming at the issue of parameter matching in conventional two-step location, a reduced dimension multiple signal classification direct position determination algorithm based on multi-array is proposed. Based on the idea of dimension reduction, the algorithm avoids multi-dimensional search in spatial domain and attenuation coefficient domain and reduces the search complexity. Simulation results show that the performance of the algorithm is better than the traditional angle of arrival two-step localization algorithm and subspace data fusion direct localization algorithm.

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SFA: A Robust Sparse Fractal Array for Estimating the Directions of Arrival of Signals

Goel,

Agrawal,

Kar

2024

Circuits Syst Signal Process

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High-Accuracy DOA Estimation Algorithm at Low SNR Through Exploiting a Supervised Index

Cited by 14 publications

References 29 publications

Joint DOD and DOA detection for MIMO radar based on signal subspace reconstruction and matching

Joint DOD and DOA detection for MIMO radar based on signal subspace reconstruction and matching

A reduced dimension multiple signal classification–based direct location algorithm with dense arrays

SFA: A Robust Sparse Fractal Array for Estimating the Directions of Arrival of Signals

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