Antenna placement optimisation for compressed sensing‐based distributed MIMO radar

Chen, Peng; Qi, Chenhao; Wu, Lenan

doi:10.1049/iet-rsn.2016.0176

Cited by 28 publications

(27 citation statements)

References 43 publications

(45 reference statements)

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“…where is adopted to control the estimation accuracy and can be usually set as = σ 2 w , and x denotes the sparse vector, the non-zero entries are the target scattering coefficients, and the positions of the non-zero entries indicate the DoA and DoD [16][17][18]24,25].…”

Section: System Model For Radar Subsystemmentioning

confidence: 99%

“…To reconstruct the sparse vector x, the orthogonal matching pursuit (OMP) method [24,26,27] can be adopted. Algorithm 1 shows the details of the OMP algorithm to estimate the target scattering coefficients, DoD and DoA.…”

Section: System Model For Radar Subsystemmentioning

confidence: 99%

“…then, using (24) to adjust w m r will result in w 1 = w 2 = · · · = w N r . To avoid this situation, we can calculate the eignvalue λ r 1 ≥ λ r 2 ≥ · · · ≥ λ r n r and the corresponding eignvectors v r 1 , v r 2 , .…”

Section: Analog Beamformer Design For the Receivermentioning

confidence: 99%

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DoA and DoD Estimation and Hybrid Beamforming for Radar-Aided mmWave MIMO Vehicular Communication Systems

Chen

Cao

He³

et al. 2018

Electronics

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Abstract:In millimeter wave (mmWave) communications, the feature of relatively large signal absorption and directional transmission render new challenges for wireless communications and signal processing. To further improve the performance of mmWave communications, a novel radar-aided mmWave communication (RAMC) approach is proposed, which can be used in vehicular communications. There are two parts in the proposed RAMC system, including the radar subsystem and the mmWave communication subsystem. In the radar subsystem, the bistatic co-prime multi-input and multi-output (MIMO) arrays are considered. With the radar antenna arrays, both the directions of departure (DoD) and the directions of arrival (DoA) are estimated. Additionally, the compressed sensing (CS)-based method is proposed to obtain the target positions. Using the estimated angle and position information, the channel estimation and feedback link of the mmWave communication subsystem can be eliminated. Moreover, a hybrid beamforming algorithm is proposed in the mmWave communication subsystem, which can overcome the shortage of the analog-only beamforming. Simulation results show that the better estimation performance can be achieved by the bistatic co-prime MIMO arrays than that by the traditional uniform linear arrays (ULA), and with the radar aided, the mmWave communication subsystem can reduce the beam search time, and the cell discovery time is improved significantly.

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Section: System Model For Radar Subsystemmentioning

confidence: 99%

Section: System Model For Radar Subsystemmentioning

confidence: 99%

See 1 more Smart Citation

DoA and DoD Estimation and Hybrid Beamforming for Radar-Aided mmWave MIMO Vehicular Communication Systems

Chen

Cao

He³

et al. 2018

Electronics

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“…In multiple-input multiple-output (MIMO) radar systems [1,2], the independent waveforms are adopted in different transmitting antennas, so compared with the traditional array radars, the better performance of target estimation and detection can be achieved by using more spatial and waveform diversities [3][4][5]. Usually, the MIMO radar systems can be categorized into the following two types with different antenna distances: (1) Colocated MIMO radar system: The antennas in receiver and transmitter are close to each other, so the waveform diversity can be exploited to improve the radar performance [1,6,7]; (2) Distributed MIMO radar system: The antennas in receiver and transmitter are widely separated, so the radar performance can be improved by exploiting the diversity of target's radar cross-section (RCS) [2,8].…”

Section: Introductionmentioning

confidence: 99%

Sparse DOD/DOA Estimation in a Bistatic MIMO Radar With Mutual Coupling Effect

et al. 2018

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The unknown mutual coupling effect between antennas significantly degrades the target localization performance in the bistatic multiple-input multiple-output (MIMO) radar. In this paper, the joint estimation problem for the direction of departure (DOD) and direction of arrival (DOA) is addressed. By exploiting the target sparsity in the spatial domain and formulating a dictionary matrix with discretizing the DOD/DOA into grids, compressed sensing (CS)-based system model is given. However, in the practical MIMO radar systems, the target cannot be precisely on the grids, and the unknown mutual coupling effect degrades the estimation performance. Therefore, a novel CS-based DOD/DOA estimation model with both the off-grid and mutual coupling effect is proposed, and a novel sparse reconstruction method is proposed to estimate DOD/DOA with updating both the off-grid and mutual coupling parameters iteratively. Moreover, to describe the estimation performance, the corresponding Cramér–Rao lower bounds (CRLBs) with all the unknown parameters are theoretically derived. Simulation results show that the proposed method can improve the DOD/DOA estimation in the scenario with unknown mutual coupling effect, and outperform state-of-the-art methods.

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“…The compressed sensing (CS)-based methods have been proposed to estimate the directions by exploiting the signal sparsity in the spatial domain [6][7][8][9][10][11][12][13][14]. Notably, the sparse Bayesian learning (SBL) and the relevance vector machine (RVM) proposed in [15] can achieve better estimation performance in the CS-based direction finding methods, where the directions are estimated by reconstructing the sparse signals in the spatial domain with the corresponding distribution assumptions of unknown parameters.…”

Section: Introductionmentioning

confidence: 99%

SBL-Based Direction Finding Method with Imperfect Array

Chen

Zhang

et al. 2018

Electronics

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The imperfect array degrades the direction finding performance. In this paper, we 1 investigate the direction finding problem in uniform linear array (ULA) system with unknown 2 mutual coupling effect between antennas. By exploiting the target sparsity in the spatial domain, 3 sparse Bayesian learning (SBL)-based model is proposed and converts the direction finding problem 4 into a sparse reconstruction problem. In the sparse-based model, the off-grid errors are introduced 5 by discretizing the direction area into grids. Therefore, an off-grid SBL model with mutual coupling 6 vector is proposed to overcome both the mutual coupling and the off-grid effect. With the distribution 7 assumptions of unknown parameters including the noise variance, the off-grid vector, the received 8 signals and the mutual coupling vector, a novel direction finding method based on SBL with unknown 9 mutual coupling effect named DFSMC is proposed, where an expectation-maximum (EM)-based 10 step is adopted by deriving the estimation expressions for all the unknown parameters theoretically. 11Simulation results show that the proposed DFSMC method can outperform state-of-the-art direction 12 finding methods significantly in the array system with unknown mutual coupling effect. 13

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Antenna placement optimisation for compressed sensing‐based distributed MIMO radar

Cited by 28 publications

References 43 publications

DoA and DoD Estimation and Hybrid Beamforming for Radar-Aided mmWave MIMO Vehicular Communication Systems

DoA and DoD Estimation and Hybrid Beamforming for Radar-Aided mmWave MIMO Vehicular Communication Systems

Sparse DOD/DOA Estimation in a Bistatic MIMO Radar With Mutual Coupling Effect

SBL-Based Direction Finding Method with Imperfect Array

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