Cumulative detection probability of stationary and moving targets by MIMO radar

2011 ◽  
Author(s):  
Jingxu Han ◽  
Michael Raymond Inggs
Keyword(s):  
Detection Probability ◽  
Mimo Radar ◽  
Moving Targets

scholarly journals Joint Design of Space-Time Transmit and Receive Weights for Colocated MIMO Radar

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2722 ◽  
Author(s):  
Ze Yu ◽  
Shusen Wang ◽  
Wei Liu ◽  
Chunsheng Li
Keyword(s):  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Space Time ◽  
Moving Targets ◽  
Joint Design ◽  
Waveform Diversity ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Single Input ◽  
Space Time Adaptive Processing

Compared with single-input multiple-output (SIMO) radar, colocated multiple-input multiple-output (MIMO) radar can detect moving targets better by adopting waveform diversity. When the colocated MIMO radar transmits a set of orthogonal waveforms, the transmit weights are usually set equal to one, and the receive weights are adaptively adjusted to suppress clutter based on space-time adaptive processing technology. This paper proposes the joint design of space-time transmit and receive weights for colocated MIMO radar. The approach is based on the premise that all possible moving targets are detected by setting a lower threshold. In each direction where there may be moving targets, the space-time transmit and receive weights can be iteratively updated by using the proposed approach to improve the output signal-to-interference-plus-noise ratio (SINR), which is helpful to improve the precision of target detection. Simulation results demonstrate that the proposed method improves the output SINR by greater than 13 dB.

scholarly journals A Super-Resolution DOA Estimation Method for Fast-Moving Targets in MIMO Radar

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Song Liu ◽  
Lan Tang ◽  
Yechao Bai ◽  
Xinggan Zhang
Keyword(s):  
Fast Moving ◽  
Estimation Method ◽  
Super Resolution ◽  
Doa Estimation ◽  
Spatial Spectrum ◽  
Mimo Radar ◽  
Radar System ◽  
Moving Targets ◽  
Dual Norm ◽  
Atomic Norm

Direction of arrival (DOA) estimation is an essential problem in the radar systems. In this paper, the problem of DOA estimation is addressed in the multiple-input and multiple-output (MIMO) radar system for the fast-moving targets. A virtual aperture is provided by orthogonal waveforms in the MIMO radar to improve the DOA estimation performance. Different from the existing methods, we consider the DOA estimation method with only one snapshot for the fast-moving targets and achieve the super-resolution estimation from the snapshot. Based on a least absolute shrinkage and selection operator (LASSO), a denoise method is formulated to obtain a sparse approximation to the received signals, where the sparsity is measured by a new type of atomic norm for the MIMO radar system. However, the denoise problem cannot be solved efficiently. Then, by deriving the dual norm of the new atomic norm, a semidefinite matrix is constructed from the denoise problem to formulate a semidefinite problem with the dual optimization problem. Finally, the DOA is estimated by peak-searching the spatial spectrum. Simulation results show that the proposed method achieves better performance of the DOA estimation in the MIMO radar system with only one snapshot.

scholarly journals The PARAFAC-MUSIC Algorithm for DOA Estimation with Doppler Frequency in a MIMO Radar System

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Nan Wang ◽  
Wenguang Wang ◽  
Fan Zhang ◽  
Yunneng Yuan
Keyword(s):  
Doppler Frequency ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Radar System ◽  
Signal Classification ◽  
Moving Targets ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Parallel Factor ◽  
Simulation Results

The PARAFAC-MUSIC algorithm is proposed to estimate the direction-of-arrival (DOA) of the targets with Doppler frequency in a monostatic MIMO radar system in this paper. To estimate the Doppler frequency, the PARAFAC (parallel factor) algorithm is firstly utilized in the proposed algorithm, and after the compensation of Doppler frequency, MUSIC (multiple signal classification) algorithm is applied to estimate the DOA. By these two steps, the DOA of moving targets can be estimated successfully. Simulation results show that the proposed PARAFAC-MUSIC algorithm has a higher accuracy than the PARAFAC algorithm and the MUSIC algorithm in DOA estimation.

scholarly journals A Sparse Bayesian Learning-Based DOA Estimation Method With the Kalman Filter in MIMO Radar

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 347
Author(s):  
Song Liu ◽  
Lan Tang ◽  
Yechao Bai ◽  
Xinggan Zhang
Keyword(s):  
Kalman Filter ◽  
Fast Moving ◽  
Bayesian Learning ◽  
Estimation Method ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Radar System ◽  
Estimation Problem ◽  
Sparse Bayesian Learning ◽  
Moving Targets

The direction of arrival (DOA) estimation problem as an essential problem in the radar system is important in radar applications. In this paper, considering a multiple-input and multiple-out (MIMO) radar system, the DOA estimation problem is investigated in the scenario with fast-moving targets. The system model is first formulated, and then by exploiting both the target sparsity in the spatial domain and the temporal correlation of the moving targets, a sparse Bayesian learning (SBL)-based DOA estimation method combined with the Kalman filter (KF) is proposed. Moreover, the performances of traditional sparse-based methods are limited by the off-grid issue, and Taylor-expansion off-grid methods also have high computational complexity and limited performance. The proposed method breaks through the off-grid limit by transforming the problem in the spatial domain to that in the time domain using the movement feature. Simulation results show that the proposed method outperforms the existing methods in the DOA estimation problem for the fast-moving targets.

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