Joint Angle and Range Estimation for Bistatic FDA-MIMO Radar via Real-valued Subspace Decomposition

2021 ◽  
pp. 108065
Author(s):  
Feilong Liu ◽  
Xianpeng Wang ◽  
Mengxing Huang ◽  
Liangtian Wan
Keyword(s):  
Joint Angle ◽  
Mimo Radar ◽  
Range Estimation ◽  
Subspace Decomposition

Joint Angle, Velocity, and Range Estimation Using 2D MUSIC and Successive Interference Cancellation in FMCW MIMO Radar System

2020 ◽  
Vol E103.B (3) ◽  
pp. 283-290
Author(s):  
Jonghyeok LEE ◽  
Sunghyun HWANG ◽  
Sungjin YOU ◽  
Woo-Jin BYUN ◽  
Jaehyun PARK
Keyword(s):  
Interference Cancellation ◽  
Joint Angle ◽  
Mimo Radar ◽  
Radar System ◽  
Successive Interference Cancellation ◽  
Range Estimation

scholarly journals Maximum Likelihood Angle-Range Estimation for Monostatic FDA-MIMO Radar with Extended Range Ambiguity Using Subarrays

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Kaikai Yang ◽  
Sheng Hong ◽  
Qi Zhu ◽  
Yanheng Ye
Keyword(s):  
Maximum Likelihood ◽  
Joint Angle ◽  
Likelihood Estimation ◽  
Mimo Radar ◽  
Estimation Accuracy ◽  
High Dimensional ◽  
Search Problem ◽  
Range Estimation ◽  
Detection Range ◽  
Range Ambiguity

In this paper, we consider the joint angle-range estimation in monostatic FDA-MIMO radar. The transmit subarrays are first utilized to expand the range ambiguity, and the maximum likelihood estimation (MLE) algorithm is first proposed to improve the estimation performance. The range ambiguity is a serious problem in monostatic FDA-MIMO radar, which can reduce the detection range of targets. To extend the unambiguous range, we propose to divide the transmitting array into subarrays. Then, within the unambiguous range, the maximum likelihood (ML) algorithm is proposed to estimate the angle and range with high accuracy and high resolution. In the ML algorithm, the joint angle-range estimation problem becomes a high-dimensional search problem; thus, it is computationally expensive. To reduce the computation load, the alternating projection ML (AP-ML) algorithm is proposed by transforming the high-dimensional search into a series of one-dimensional search iteratively. With the proposed AP-ML algorithm, the angle and range are automatically paired. Simulation results show that transmitting subarray can extend the range ambiguity of monostatic FDA-MIMO radar and obtain a lower cramer-rao low bound (CRLB) for range estimation. Moreover, the proposed AP-ML algorithm is superior over the traditional estimation algorithms in terms of the estimation accuracy and resolution.

Joint Angle and Range Estimation in the Fda-Mimo Radar: The Reduced-Dimension Root Music Algorithm

2020 ◽  
Vol 115 (3) ◽  
pp. 2515-2533
Author(s):  
Cheng Wang ◽  
Wang Zheng ◽  
Pan Gong ◽  
Zheng Li
Keyword(s):  
Joint Angle ◽  
Mimo Radar ◽  
Music Algorithm ◽  
Range Estimation ◽  
Reduced Dimension

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.

FDA-MIMO radar for DOD, DOA, and range estimation: SA-MCFO framework and RDMD algorithm

2021 ◽  
pp. 108209
Author(s):  
Cheng Wang ◽  
Xiaofei Zhang ◽  
Jianfeng Li
Keyword(s):  
Mimo Radar ◽  
Range Estimation

A Gridless Joint DOA and Range Estimation Method for FMCW MIMO Radar

2022 ◽  
pp. 315-321
Author(s):  
Jingyu Cong ◽  
Tao Lu ◽  
Zhang Bin ◽  
Xiaoli Jing ◽  
Xianpeng Wang
Keyword(s):  
Estimation Method ◽  
Mimo Radar ◽  
Range Estimation

Joint Angle and Doppler Frequency Estimation for MIMO Radar with One-Bit Sampling: A Maximum Likelihood-Based Method

2020 ◽  
Vol 56 (6) ◽  
pp. 4734-4748
Author(s):  
Feng Xi ◽  
Yijian Xiang ◽  
Zhen Zhang ◽  
Shengyao Chen ◽  
Arye Nehorai
Keyword(s):  
Maximum Likelihood ◽  
Joint Angle ◽  
Frequency Estimation ◽  
Doppler Frequency ◽  
Mimo Radar ◽  
Doppler Frequency Estimation

Joint Angle and Range Estimation With Signal Clustering in FMCW Radar

2020 ◽  
Vol 20 (4) ◽  
pp. 1882-1892
Author(s):  
Wen-Hsien Fang ◽  
Li-Der Fang
Keyword(s):  
Joint Angle ◽  
Range Estimation ◽  
Fmcw Radar
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