scholarly journals Target localisation in multistatic radar using BR, TDOA, and AOA measurements

2019 ◽  
Vol 2019 (19) ◽  
pp. 6052-6056 ◽  
Author(s):  
Jun Wang ◽  
Zhaotao Qin ◽  
Yanxian Bi ◽  
Shaoming Wei ◽  
Feixiang Luo
Keyword(s):  
Target Localisation ◽  
Multistatic Radar

A Power Allocation Approach for 3D Target Tracking in Multistatic Radar Systems

2014 ◽  
Vol 35 (4) ◽  
pp. 901-907
Author(s):  
Jun-kun Yan ◽  
Feng-zhou Dai ◽  
Tong Qin ◽  
Hong-wei Liu ◽  
Zheng Bao
Keyword(s):  
Target Tracking ◽  
Power Allocation ◽  
Radar Systems ◽  
Multistatic Radar ◽  
Allocation Approach

On the Sensitivity of Passive Multistatic Radar Amplitude and Doppler Measurements to Significant Wave Height

2021 ◽  
pp. 1-5
Author(s):  
Jeffrey D. Ouellette ◽  
William T. Bounds ◽  
David J. Dowgiallo ◽  
Jakov V. Toporkov ◽  
Paul A. Hwang
Keyword(s):  
Wave Height ◽  
Significant Wave Height ◽  
Significant Wave ◽  
Multistatic Radar

Target Kinematic State Estimation with Passive Multistatic Radar

2021 ◽  
pp. 1-1
Author(s):  
Hui Ma ◽  
Michail Antoniou ◽  
Andrew G. Stove ◽  
Mikhail Cherniakov
Keyword(s):  
State Estimation ◽  
Multistatic Radar

scholarly journals A Random Antenna Subset Selection Jamming Method against Multistatic Radar System

2021 ◽  
pp. 108126
Author(s):  
Xiangtuan Wang ◽  
Hang Ruan ◽  
Yimin Liu ◽  
Tianyao Huang
Keyword(s):  
Subset Selection ◽  
Radar System ◽  
Antenna Subset Selection ◽  
Multistatic Radar

scholarly journals Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System

2021 ◽  
Vol 13 (4) ◽  
pp. 707
Author(s):  
Yu’e Shao ◽  
Hui Ma ◽  
Shenghua Zhou ◽  
Xue Wang ◽  
Michail Antoniou ◽  
...  
Keyword(s):  
Optimization Methods ◽  
Optimization Method ◽  
Satellite System ◽  
Radar System ◽  
Target Localization ◽  
Computational Load ◽  
Matrix Fusion ◽  
Global Navigation Satellite ◽  
Multistatic Radar ◽  
Combined Algorithm

To cope with the increasingly complex electromagnetic environment, multistatic radar systems, especially the passive multistatic radar, are becoming a trend of future radar development due to their advantages in anti-electronic jam, anti-destruction properties, and no electromagnetic pollution. However, one problem with this multi-source network is that it brings a huge amount of information and leads to considerable computational load. Aiming at the problem, this paper introduces the idea of selecting external illuminators in the multistatic passive radar system. Its essence is to optimize the configuration of multistatic T/R pairs. Based on this, this paper respectively proposes two multi-source optimization algorithms from the perspective of resolution unit and resolution capability, the Covariance Matrix Fusion Method and Convex Hull Optimization Method, and then uses a Global Navigation Satellite System (GNSS) as an external illuminator to verify the algorithms. The experimental results show that the two optimization methods significantly improve the accuracy of multistatic positioning, and obtain a more reasonable use of system resources. To evaluate the algorithm performance under large number of transmitting/receiving stations, further simulation was conducted, in which a combination of the two algorithms were applied and the combined algorithm has shown its effectiveness in minimize the computational load and retain the target localization precision at the same time.

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