scholarly journals Three-dimensional target localization method based on the tensor subspace FDS-MIMO radar with a co-prime frequency offset

2019 ◽  
Vol 49 (1) ◽  
pp. 87-103 ◽  
Author(s):  
Xingxing LI ◽  
Dangwei WANG ◽  
Xiaoyan MA ◽  
Wenqin WANG
Keyword(s):  
Three Dimensional ◽  
Frequency Offset ◽  
Mimo Radar ◽  
Target Localization ◽  
Localization Method ◽  
Tensor Subspace

scholarly journals Wireless Sensor Network Target Localization Algorithm Based on Two- and Three-Dimensional Delaunay Partitions

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Chenguang Shao
Keyword(s):  
Three Dimensional ◽  
Target Localization ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Target Node ◽  
Localization Accuracy ◽  
Localization Method ◽  
Anchor Nodes ◽  
2D And 3D ◽  
The Relationship

The target localization algorithm is critical in the field of wireless sensor networks (WSNs) and is widely used in many applications. In the conventional localization method, the location distribution of the anchor nodes is fixed and cannot be adjusted dynamically according to the deployment environment. The resulting localization accuracy is not high, and the localization algorithm is not applicable to three-dimensional (3D) conditions. Therefore, a Delaunay-triangulation-based WSN localization method, which can be adapted to two-dimensional (2D) and 3D conditions, was proposed. Based on the location of the target node, we searched for the triangle or tetrahedron surrounding the target node and designed the localization algorithm in stages to accurately calculate the coordinate value of the target. The relationship between the number of target nodes and the number of generated graphs was analysed through numerous experiments, and the proposed 2D localization algorithm was verified by extending it the 3D coordinate system. Experimental results revealed that the proposed algorithm can effectively improve the flexibility of the anchor node layout and target localization accuracy.

scholarly journals Iterative Target Localization Method for Distributed MIMO Radar System

2017 ◽  
Vol 28 (10) ◽  
pp. 819-824
Author(s):  
Hyuksoo Shin ◽  
Young-Seek Chung ◽  
Young-Seek Chung ◽  
Hoon-Gee Yang ◽  
Jong-mann Kim ◽  
...  
Keyword(s):  
Mimo Radar ◽  
Radar System ◽  
Target Localization ◽  
Localization Method

scholarly journals Three-Dimensional Target Localization and Cramér-Rao Bound for Two-Dimensional OFDM-MIMO Radar

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Xingxing Li ◽  
Dangwei Wang ◽  
Xiaoyan Ma
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Three Dimensional ◽  
Mimo Radar ◽  
Joint Estimation ◽  
Target Localization ◽  
Two Dimensional ◽  
Frequency Diversity ◽  
Cramer Rao Bound

Target localization using a frequency diversity multiple-input multiple-output (MIMO) system is one of the hottest research directions in the radar society. In this paper, three-dimensional (3D) target localization is considered for two-dimensional MIMO radar with orthogonal frequency division multiplexing linear frequency modulated (OFDM-LFM) waveforms. To realize joint estimation for range and angle in azimuth and elevation, the range-angle-dependent beam pattern with high range resolution is produced by the OFDM-LFM waveform. Then, the 3D target localization proposal is presented and the corresponding closed-form expressions of Cramér-Rao bound (CRB) are derived. Furthermore, for mitigating the coupling of angle and range and further improving the estimation precision, a CRB optimization method is proposed. Different from the existing methods of FDA-based radar, the proposed method can provide higher range estimation because of multiple transmitted frequency bands. Numerical simulation results are provided to demonstrate the effectiveness of the proposed approach and its improved performance of target localization.

scholarly journals A NEAR-FIELD TARGET LOCALIZATION METHOD FOR MIMO RADAR

2017 ◽  
Vol 69 ◽  
pp. 93-98
Author(s):  
Jurong Hu ◽  
Qianru Yuan ◽  
Yu Zhang
Keyword(s):  
Near Field ◽  
Mimo Radar ◽  
Target Localization ◽  
Localization Method

scholarly journals Target Localization in Bistatic EMVS-MIMO Radar Using Tensor Subspace Method

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 163119-163127 ◽  
Author(s):  
Chenxing Mao ◽  
Junpeng Shi ◽  
Fangqing Wen
Keyword(s):  
Mimo Radar ◽  
Target Localization ◽  
Subspace Method ◽  
Tensor Subspace

scholarly journals Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1956
Author(s):  
Jingyu Cong ◽  
Xianpeng Wang ◽  
Xiang Lan ◽  
Mengxing Huang ◽  
Liangtian Wan
Keyword(s):  
Neural Network ◽  
Continuous Wave ◽  
Super Resolution ◽  
Mimo Radar ◽  
Target Localization ◽  
Estimation Accuracy ◽  
Low Resolution ◽  
Range Estimation ◽  
Resolution Image ◽  
Resolution Imaging

The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.

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