Transmit Array Interpolation for DOA Estimation via Tensor Decomposition in 2-D MIMO Radar

Cao, Mengxi; Vorobyov, Sergiy A.; Hassanien, Aboulnasr

doi:10.1109/tsp.2017.2721904

Cited by 48 publications

(21 citation statements)

References 45 publications

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“…Note that transmit and receive array geometries are arbitrary here, and do not need to be uniform. Then TB also performs the function of array interpolation as shown in [111]- [116], where the problem of the 2D transmit array interpolation and beamspace design for mono-static MIMO radar with application to elevation and azimuth estimation has been addressed. The 2D transmit array interpolation has been formulated, for example, as the minimax convex optimization problem with constraints on array interpolation errors within a spatial sector of interest while minimizing the transmit power outside the sector.…”

Section: B Transmit Array Interpolation In Mimo Radarmentioning

confidence: 99%

Tensor Decompositions in Wireless Communications and MIMO Radar

Chen

Ahmad

Vorobyov

et al. 2021

IEEE J. Sel. Top. Signal Process.

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The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions. Harnessing the power of multilinear algebra through tensor analysis in wireless signal processing, channel modeling, and parametric channel estimation provides greater flexibility in the choice of constraints on data properties and permits extraction of more general latent data components than matrix-based methods. Tensor analysis has also found applications in Multiple-Input Multiple-Output (MIMO) radar because of its ability to exploit the inherent higher-dimensional signal structures therein. In this paper, we provide a broad overview of tensor analysis in wireless communications and MIMO radar. More specifically, we cover topics including basic tensor operations, common tensor decompositions via canonical polyadic and Tucker factorization models, wireless communications applications ranging from blind symbol recovery to channel parameter estimation, and transmit beamspace design and target parameter estimation in MIMO radar.

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Section: B Transmit Array Interpolation In Mimo Radarmentioning

confidence: 99%

Tensor Decompositions in Wireless Communications and MIMO Radar

Chen

Ahmad

Vorobyov

et al. 2021

IEEE J. Sel. Top. Signal Process.

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“…Direction-of-arrival (DOA) estimation is the key technology of a MIMO system. In [7], Cao et al applied the tensor-based methods to DOA estimation in a MIMO radar. Ciuonzo et al studied the performance of multiple signal classification (MUSIC) in computational time-reversal applications [8].…”

Section: Introductionmentioning

confidence: 99%

An MIMO Radar System Based on the Sparse-Array and Its Frequency Migration Calibration Method

Miao

Zhao

et al. 2019

Sensors

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In this paper, a Multiple Input Multiple Output (MIMO) radar system based on a sparse-array is proposed. In order to reduce the side-lobe level, a genetic algorithm (GA) is used to optimize the array arrangement. To reduce the complexity of the system, time-division multiplexing (TDM) technology is adopted. Since the signals are received in different periods, a frequency migration will emerge if the target is in motion, which will lead to the lower direction-of-arrival (DOA) performance of the system. To solve this problem, a stretching transformation method in the fast-frequency slow-time domain is proposed, in order to eliminate frequency migration. Only minor adjustments need to be implemented for the signal processing, and the root-mean-square error (RMSE) of the DOA estimation will be reduced by about 90%, compared with the one of an uncalibrated system. For example, a uniform linear array (ULA) MIMO system with 2 transmitters and 20 receivers can be replaced by the proposed system with 2 transmitters and 12 receivers, achieving the same DOA performance. The calibration formulations are given, and the simulation results of the automotive radar system are also provided, which validate the theory.

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“…The beamspace-based methods can design the beamspace matrix to achieve a spatial beampattern that is as close as possible to a certain desired one. For example, tensor modeling is adopted for the mono-static MIMO radar in [10] to design the transmit array interpolation and the beamspace jointly. In the colocated multiple-input and multiple-output (MIMO) radar system, Ref.…”

Section: Introductionmentioning

confidence: 99%

Compressed Sensing-Based DOA Estimation with Unknown Mutual Coupling Effect

et al. 2018

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The performance of a direction-finding system is significantly degraded by the imperfection of an array. In this paper, the direction-of-arrival (DOA) estimation problem is investigated in the uniform linear array (ULA) system with the unknown mutual coupling (MC) effect. The system model with MC effect is formulated. Then, by exploiting the signal sparsity in the spatial domain, a compressed-sensing (CS)-based system model is proposed with the MC coefficients, and the problem of DOA estimation is converted into that of a sparse reconstruction. To solve the reconstruction problem efficiently, a novel DOA estimation method, named sparse-based DOA estimation with unknown MC effect (SDMC), is proposed, where both the sparse signal and the MC coefficients are estimated iteratively. Simulation results show that the proposed method can achieve better performance of DOA estimation in the scenario with MC effect than the state-of-the-art methods, and improve the DOA estimation performance about 31.64 % by reducing the MC effect by about 4 dB.

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Transmit Array Interpolation for DOA Estimation via Tensor Decomposition in 2-D MIMO Radar

Cited by 48 publications

References 45 publications

Tensor Decompositions in Wireless Communications and MIMO Radar

Tensor Decompositions in Wireless Communications and MIMO Radar

An MIMO Radar System Based on the Sparse-Array and Its Frequency Migration Calibration Method

Compressed Sensing-Based DOA Estimation with Unknown Mutual Coupling Effect

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