Joint DOD and DOA estimation using tensor reconstruction based sparse representation approach for bistatic MIMO radar with unknown noise effect

Baidoo, Evans; Hu, Jurong; Zeng, Bao; Kwakye, Benjamin Danso

doi:10.1016/j.sigpro.2020.107912

Cited by 18 publications

(6 citation statements)

References 31 publications

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“…Therefore, it is also observed that IOGWSBI has better performance compared with RV-OGWSBI under the same simulation condition. In addition, the estimation accuracy of the Initial hyperparameter 0 , , Y Construct weighted vector by ( 13), ( 14) Input Y, A, B, C Unitary transformation to Y by ( 5), (8) Calculate mean matrix (t) and covariance matrix Update 0 , , by ( 18), ( 19) 6 Wireless Communications and Mobile Computing IOGWSBI algorithm is about 50% higher than that of the OGWSBI algorithm.…”

Section: Performance Analysis Of Iogwsbimentioning

confidence: 99%

“…In recent years, the compressed sensing (CS) signal processing method [5,6] has captured the growing attention of scholars thus leading to its wide application in various fields. In view of the sparsity of the array model, scholars apply the CS theory to DOA estimation and proposed a sea of sparsity-driven methods [7][8][9], the most successful of which is L1-SVD [10]. Compared with subspace DOA esti-mation algorithms, the sparse representation methods exhibit many advantages, e.g., improved robustness to noise, limited number of snapshots, and correlation of signals [11].…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Improved OGWSBI Algorithm for Accurate Off‐Grid DOA Estimation of Coherent Signals

Shen

et al. 2021

Wireless Communications and Mobile Computing

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The performance of the weighted sparse Bayesian inference (OGWSBI) algorithm for off-grid coherent DOA estimation is not satisfactory due to the inaccurate weighting information. To increase the estimation accuracy and efficiency, an improved OGWSBI algorithm based on a higher-order off-grid model and unitary transformation for off-grid coherent DOA estimation is proposed in this paper. Firstly, to reduce the approximate error of the first-order off-grid model, the steering vector is reformulated by the second-order Taylor expansion. Then, the received data is transformed from complex value to real value and the coherent signals are decorrelated via utilizing unitary transformation, which can increase the computational efficiency and restore the rank of the covariance matrix. Finally, in the real field, the steering vector higher-order approximation model and weighted sparse Bayesian inference are combined together to realize the estimation of DOA. Extensive simulation results indicate that under the condition of coherent signals and low SNR, the estimation accuracy of the proposed algorithm is about 50% higher than that of the OGWSBI algorithm, and the calculation time is reduced by about 60%.

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Section: Performance Analysis Of Iogwsbimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Improved OGWSBI Algorithm for Accurate Off‐Grid DOA Estimation of Coherent Signals

Shen

et al. 2021

Wireless Communications and Mobile Computing

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“…Among these algorithms, compressed sensing (CS) is a new signal processing theory in recent years; since it was born, the relevant research has been carried out continuously. It can be classified into grid-division and grid-less methods; the first needs to par-tition the airspace into multiple grids before signal recovery [37][38][39], while the idea of grid-less originally presented by Candes and Fernandez [40] can solve the DOA in continuous domain; as a result, it has aroused great interests and attentions [41][42][43], but the problem of positive semidefinite problem is inevitable, so the calculation is heavy.…”

Section: Introductionmentioning

confidence: 99%

Accessorial Locating for Internet of Vehicles Based on DOA Estimation in Industrial Transportation

Zhen

Guo

2021

Wireless Communications and Mobile Computing

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With the improvement of living standard and the development of science and technology, Internet of Vehicle (IOV) will play an important part in industrial transportation as a main research field of Internet of Things. As a result, it is very necessary to grasp the location of vehicle. However, the traditional single global position system is easily affected by the external environment, so an accessorial locating approach based on wideband direction of arrival (DOA) estimation in intelligent transportation is proposed. First, model the array received signal on the road infrastructure. Then, by means of random forest regression (RFR) in the supervised learning, upper triangle elements of the covariance matrix of each frequency and the actual DOA are, respectively, extracted as the input features and output parameters; thus, the corresponding prediction coefficients are solved by training. After that, the trained RFR model can be used to calculate the final direction using test samples. Finally, these vehicles can be located according to the geometrical relation between the vehicle and the infrastructure. The proposed algorithm is not only suitable for uncorrelated signals but also for uncorrelated and correlated mixed signals without wideband focusing. The simulations show that compared with some sparse recovery algorithm, the prediction accuracy and resolution are effectively improved.

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“…MIMO radar generally uses a uniform linear array as the transmitter and receiver array, and combines it with the classic high-resolution DOA estimation method to estimate the direction of arrival [9][10][11][12][13]. Classical high-resolution DOA estimation methods such as Multiple signal classification (MUSIC) method [14][15] or Estimation of signal parameters via rotational invariance techniques (ESPRIT) method [16][17].…”

Section: Introductionmentioning

confidence: 99%

Non-circular Signal DOA Estimation based on Coprime Array MIMO Radar

Zhang

et al. 2021

Preprint

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Aiming at the problems of low degree of freedom, small array aperture, phase ambiguity and other problems of traditional coprime array direction of arrival estimation methods, a non-circular signal DOA estimation method based on expanded coprime array MIMO radar is proposed. Firstly, this method combines the coprime array and the MIMO radar to form transmitter and receiver array. Secondly, the array is expanded using the non-circular signal characteristics to reconstruct the received signal matrix. Then the dimensionality reduction is performed. The two-dimensional spectral peak search is converted into an optimization problem, and the optimization of the two-dimensional MUSIC algorithm is reconstructed using constraints, and a cost function is constructed to solve the problem. In addition, using the power series of the noise eigenvalues to correct the noise subspace to further improve the accuracy of the algorithm. Finally, the problem of no phase ambiguity in the method in this article is derived. Simulation experiments show that the method in this article can effectively avoid phase ambiguity, greatly improve the degree of freedom, and expand the array aperture. Compared with the traditional MUSIC algorithm and the mutual prime array MUSIC algorithm, it has better resolution and DOA estimation accuracy.

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Joint DOD and DOA estimation using tensor reconstruction based sparse representation approach for bistatic MIMO radar with unknown noise effect

Cited by 18 publications

References 31 publications

An Efficient Improved OGWSBI Algorithm for Accurate Off‐Grid DOA Estimation of Coherent Signals

An Efficient Improved OGWSBI Algorithm for Accurate Off‐Grid DOA Estimation of Coherent Signals

Accessorial Locating for Internet of Vehicles Based on DOA Estimation in Industrial Transportation

Non-circular Signal DOA Estimation based on Coprime Array MIMO Radar

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