Optimal Coverage Area Analysis For Target Localization in Multistatic Radar

Author(s):  
Valarmathi Jayaraman ◽  
Dillip Dash ◽  
Devika Jena ◽  
Kodanada Dhar Sa
2021 ◽  
Vol 13 (4) ◽  
pp. 707
Author(s):  
Yu’e Shao ◽  
Hui Ma ◽  
Shenghua Zhou ◽  
Xue Wang ◽  
Michail Antoniou ◽  
...  

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.


Author(s):  
Xiaoqiang Li ◽  
Jianfeng Chen ◽  
Rongrong Zhang ◽  
Yang Wen ◽  
Weijie Tan

Efficient localization of multiple targets is one of the basic technical problems in wireless sensor networks (WSN). The traditional sparse representation method based on greedy class is not efficient in multi-target positioning. Aiming at this problem, a multi-target localization algorithm based on QR decomposition for fast orthogonal matching pursuit is proposed. The algorithm meshes the wireless sensor coverage area to design an over-complete dictionary, which transforms the multi-target localization problem into a sparse signal recovery problem. The method utilizes the sensor to receive the sparse characteristics of the target signal strength, and then uses fast orthogonal matching pursuit to recover the measured values, thereby localization the target by sparsity. Through the QR decomposition of the column full rank matrix, the recursive form is used to invert the sub-dictionary matrix, thus avoiding the direct inversion of the matrix in the traditional method, so that the computational complexity is greatly reduced. The simulation results show that compared with the traditional orthogonal matching pursuit compressed sensing reconstruction method, this method does not lose the localization accuracy and improves the computational efficiency.


2018 ◽  
Vol 56 (8) ◽  
pp. 4808-4819 ◽  
Author(s):  
Hui Ma ◽  
Michail Antoniou ◽  
Andrew G. Stove ◽  
Jon Winkel ◽  
Mikhail Cherniakov

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