Joint angle, range and velocity estimation for bi-static FDA-MIMO radar

2017 ◽  
Author(s):  
Zhihao Zhao ◽  
Zhimin Wang ◽  
Yang Sun
Keyword(s):  
Joint Angle ◽  
Mimo Radar ◽  
Velocity Estimation

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

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.

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

Noncoherent MIMO Radar for Location and Velocity Estimation: More Antennas Means Better Performance

2010 ◽  
Vol 58 (7) ◽  
pp. 3661-3680 ◽  
Author(s):  
Qian He ◽  
Rick S. Blum ◽  
Alexander M. Haimovich
Keyword(s):  
Mimo Radar ◽  
Velocity Estimation

A new method for velocity estimation in multi-carrier-frequency MIMO radar

2009 ◽  
Author(s):  
G.D. Qin ◽  
B.X. Chen ◽  
D.F. Chen
Keyword(s):  
Carrier Frequency ◽  
Mimo Radar ◽  
Velocity Estimation ◽  
New Method
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