Joint Sparse Modeling For Target Parameter Estimation In Distributed MIMO Radar

2013 ◽  
Author(s):  
Tao Yu ◽  
Ben De ◽  
Zhang Gong
Keyword(s):  
Parameter Estimation ◽  
Mimo Radar ◽  
Sparse Modeling ◽  
Target Parameter

Moving target parameter estimation for MIMO radar based on the improved particle filter

2016 ◽  
Vol 29 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Jinfeng Hu ◽  
Hanwen Chen ◽  
Yang Li ◽  
Huiyong Li ◽  
Julan Xie
Keyword(s):  
Parameter Estimation ◽  
Particle Filter ◽  
Mimo Radar ◽  
Moving Target ◽  
Target Parameter

scholarly journals Low Complexity Moving Target Parameter Estimation for MIMO Radar Using 2D-FFT

2017 ◽  
Vol 65 (18) ◽  
pp. 4745-4755 ◽  
Author(s):  
Seifallah Jardak ◽  
Sajid Ahmed ◽  
Mohamed-Slim Alouini
Keyword(s):  
Parameter Estimation ◽  
Low Complexity ◽  
Mimo Radar ◽  
Moving Target ◽  
Target Parameter ◽  
2D Fft

scholarly journals Tensor-Based Reduced-Dimension MUSIC Method for Parameter Estimation in Monostatic FDA-MIMO Radar

2021 ◽  
Vol 13 (18) ◽  
pp. 3772
Author(s):  
Tengxian Xu ◽  
Xianpeng Wang ◽  
Mengxing Huang ◽  
Xiang Lan ◽  
Lu Sun
Keyword(s):  
Parameter Estimation ◽  
Mimo Radar ◽  
Small Samples ◽  
Computation Complexity ◽  
Vector Method ◽  
Steering Vector ◽  
Target Parameter ◽  
Dimensional Spectrum ◽  
Spectrum Function ◽  
Reduced Dimension

Frequency diverse array (FDA) radar has attracted much attention due to the angle and range dependence of the beam pattern. Multiple-input-multiple-output (MIMO) radar has high degrees of freedom (DOF) and spatial resolution. The FDA-MIMO radar, a hybrid of FDA and MIMO radar, can be used for target parameter estimation. This paper investigates a tensor-based reduced-dimension multiple signal classification (MUSIC) method, which is used for target parameter estimation in the FDA-MIMO radar. The existing subspace methods deteriorate quickly in performance with small samples and a low signal-to-noise ratio (SNR). To deal with the deterioration difficulty, the sparse estimation method is then proposed. However, the sparse algorithm has high computation complexity and poor stability, making it difficult to apply in practice. Therefore, we use tensor to capture the multi-dimensional structure of the received signal, which can optimize the effectiveness and stability of parameter estimation, reduce computation complexity and overcome performance degradation in small samples or low SNR simultaneously. In our work, we first obtain the tensor-based subspace by the high-order-singular value decomposition (HOSVD) and establish a two-dimensional spectrum function. Then the Lagrange multiplier method is applied to realize a one-dimensional spectrum function, estimate the direction of arrival (DOA) and reduce computation complexity. The transmitting steering vector is obtained by the partial derivative of the Lagrange function, and automatic pairing of target parameters is then realized. Finally, the range can be obtained by using the least square method to process the phase of transmitting steering vector. Method analysis and simulation results prove the superiority and reliability of the proposed method.

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