scholarly journals Maximum Likelihood-Based Methods for Target Velocity Estimation with Distributed MIMO Radar

Electronics ◽  
2018 ◽  
Vol 7 (3) ◽  
pp. 29 ◽  
Author(s):  
Zhenxin Cao ◽  
Peng Chen ◽  
Zhimin Chen ◽  
Xinyi He
Keyword(s):  
Maximum Likelihood ◽  
Mimo Radar ◽  
Velocity Estimation ◽  
Target Velocity

Target Velocity Estimation and Antenna Placement for MIMO Radar With Widely Separated Antennas

2010 ◽  
Vol 4 (1) ◽  
pp. 79-100 ◽  
Author(s):  
Qian He ◽  
Rick S. Blum ◽  
Hana Godrich ◽  
Alexander M. Haimovich
Keyword(s):  
Mimo Radar ◽  
Velocity Estimation ◽  
Target Velocity ◽  
Antenna Placement

Cramer-Rao bound for target velocity estimation in MIMO radar with widely separated antennas

2008 ◽  
Author(s):  
Qian He ◽  
Rick S. Blum ◽  
Hana Godrich ◽  
Alexander M. Haimovich
Keyword(s):  
Mimo Radar ◽  
Velocity Estimation ◽  
Target Velocity ◽  
Cramer Rao Bound

2D Maximum Likelihood Angle Estimation for MIMO Radar with Unequally Spaced L-shaped Arrays

2018 ◽  
Author(s):  
Takaaki Kishigami ◽  
Kenta Iwasa ◽  
Hidekuni Yomo ◽  
Akihiko Matsuoka ◽  
Junji Sato
Keyword(s):  
Maximum Likelihood ◽  
Mimo Radar ◽  
Angle Estimation ◽  
Unequally Spaced

scholarly journals DIRECTION FINDING FOR BISTATIC MIMO RADAR USING EM MAXIMUM LIKELIHOOD ALGORITHM

2013 ◽  
Vol 141 ◽  
pp. 99-116 ◽  
Author(s):  
Hao Wen Chen ◽  
Degui Yang ◽  
Hong-Qiang Wang ◽  
Xiang Li ◽  
Zhaowen Zhuang
Keyword(s):  
Maximum Likelihood ◽  
Direction Finding ◽  
Mimo Radar ◽  
Maximum Likelihood Algorithm ◽  
Bistatic Mimo Radar

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

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.

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