Angle estimation and mutual coupling self-calibration for ULA-based bistatic MIMO radar

Wen, Fangqing; Zhang, Zijing; Wang, Ke; Sheng, Guanqun; Zhang, Gong

doi:10.1016/j.sigpro.2017.09.021

Cited by 109 publications

(57 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The computational complexity of the exhaustive search beam selection can also be reduced by estimating and tracking the channel parameters such as the direction of arrival and direction of departure [17]. The estimation of angular information is essential in radar communications [18], [19] to enable big data transmission. Reducing the complexity of the exhaustive search algorithm based on parameter estimation are beyond the scope of this paper.…”

Section: Introductionmentioning

confidence: 99%

SINR Maximization Beam Selection for mmWave Beamspace MIMO Systems

et al. 2020

View full text Add to dashboard Cite

Beamspace multiple-input multiple-output (MIMO) systems employ beam selection algorithms based on the sparsity of the beamspace channel to reduce the number of radio-frequency chains required in multiuser systems. In this paper, we address the problem of selecting multiple beams to each user. Specifically, we propose a beam selection scheme subject to signal to interference plus noise ratio (SINR) maximization. The proposed beamspace beam selection algorithm is developed in two stages. In the first stage, each user is assigned a non-overlapping beam determined by Kuhn-Munkres assignment algorithm. In the second stage, the selected beams in the previous stage are augmented with additional beams from an M-dominant beam set subject to a sum-rate maximization criterion. The performance of the proposed scheme is verified by simulations, and the results show a near-optimal performance in terms of the achievable sum-rate. Also, a significant improvement in terms of energy efficiency is achieved compared to the conventional beam selection scheme. INDEX TERMS Beamspace, massive MIMO, millimeter wave communications, multiuser interference, beam selection, beamforming.

show abstract

Section: Introductionmentioning

confidence: 99%

SINR Maximization Beam Selection for mmWave Beamspace MIMO Systems

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Comparing with common target detection methods [1][2][3], magnetic anomaly detection is an effective method for hidden ferromagnetic target detection [4]. It possesses the advantages of good performance, crypticity and high antiinterference capability and extensively used in prospection, archaeological investigation and vehicle tracking [5][6].…”

Section: Introductionmentioning

confidence: 99%

Magnetic Anomaly Signal Detection Using Parallel Monostable Stochastic Resonance System

Liu

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The nonlinear stochastic resonance (SR) system possesses the ability of taking advantage of noise to enhance the weak signal when the SR system, signal and noise reach to the matching relation. It provides an effective approach to detect the weak magnetic anomaly signal in low signal-to-noise ratio. However, in practical applications, the measured magnetic anomaly signal may be a peak signal, a trough signal, or a combination of the two due to the uncertainty of magnetic target orientation. Hence it is difficult to maintain a good detection performance with single SR system because the SR system output is directly influenced by signal waveforms. Aiming to this, a new strategy using the parallel monostable SR (PMSR) system is proposed, which can ensure the good detection performance regardless of a peak signal, a trough signal, or a combination of the two. Besides, we take the kurtosis index as the criterion and search the optimal system parameters in SR system. The simulation and experiment results indicate its availability, validity and that it can achieve a good detection performance in different waveforms. It can be expected to be widely used in the field of magnetic anomaly detection with PMSR system. INDEX TERMS Magnetic anomaly detection, parallel monostable stochastic resonance, signal waveforms.

show abstract

“…e theory of compressive sensing (CS) uses a small amount of measurement information to reconstruct the original signal with a large probability through optimization, which is the research hot spot in the field of signal processing [3][4][5][6]. It has been used in ISAR imaging [7,8], MIMO radar signal processing [9,10], and radar parameter estimation [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

ISAR Imaging Based on Multiple Measurement Vector Model Sparse Signal Recovery Algorithm

Feng

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

A multiple measurement vector (MMV) model blocks sparse signal recovery. ISAR imaging algorithm is proposed to improve ISAR imaging quality. Firstly, the sparse imaging model is built, and block sparse signal recovery algorithm-based MMV model is applied to ISAR imaging. Then, a negative exponential function is proposed to approximately block L0 norm. The optimization solution of smoothed function is obtained by constructing a decreasing sequence. Finally, the correction steps are added to ensure the optimal solution of the block sparse signal along the fastest descent direction. Several simulations and real data simulation experiments verify the proposed algorithm has advantages in imaging time and quality.

show abstract

Angle estimation and mutual coupling self-calibration for ULA-based bistatic MIMO radar

Cited by 109 publications

References 22 publications

SINR Maximization Beam Selection for mmWave Beamspace MIMO Systems

SINR Maximization Beam Selection for mmWave Beamspace MIMO Systems

Magnetic Anomaly Signal Detection Using Parallel Monostable Stochastic Resonance System

ISAR Imaging Based on Multiple Measurement Vector Model Sparse Signal Recovery Algorithm

Contact Info

Product

Resources

About