A regularized tensor decomposition method with adaptive rank adjustment for Compressed-Sensed-Domain background subtraction

2018 ◽  
Vol 62 ◽  
pp. 149-163
Author(s):  
Senlin Xia ◽  
Huaijiang Sun ◽  
Beijia Chen
Keyword(s):  
Background Subtraction ◽  
Decomposition Method ◽  
Tensor Decomposition

Efficient tensor decomposition method for noncircular source in colocated coprime MIMO radar

2020 ◽  
Vol 29 (5) ◽  
pp. 054304
Author(s):  
Qian-Peng Xie ◽  
Xiao-Yi Pan ◽  
Shun-Ping Xiao
Keyword(s):  
Decomposition Method ◽  
Tensor Decomposition ◽  
Mimo Radar

scholarly journals Joint Range-Doppler-Angle Estimation for OFDM-Based RadCom System via Tensor Decomposition

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Bo Kong ◽  
Yuhao Wang ◽  
Xiaohua Deng ◽  
Dong Qin
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Decomposition Method ◽  
Tensor Decomposition ◽  
High Energy ◽  
Local Extremum ◽  
Joint Estimation ◽  
Superior Performance ◽  
Green Communications ◽  
Tensor Power ◽  
Doppler Angle

Radar and communication (RadCom) systems have received increasing attention due to their high energy efficiency and spectral efficiency. They have been identified as green communications. This paper is concerned with a joint estimation of range-Doppler-angle parameters for an orthogonal frequency division multiplexing (OFDM) based RadCom system. The key idea of the proposed method is to derive different factor matrices by the tensor decomposition method and then extract parameters of the targets from these factor matrices. Different from the classical tensor decomposition method via alternating least squares or higher-order singular value decomposition, we adopt a greedy based method with each step constituted by a rank-1 approximation subproblem. To avoid local extremum, the rank-1 approximation is solved by using a multiple random initialized tensor power method with a comparison procedure followed. A parameterized rectification method is also proposed to incorporate the inherent structures of the factor matrices. The proposed algorithm can estimate all the parameters simultaneously without parameter pairing requirement. The numerical experiments demonstrate superior performance of the proposed algorithm compared with the existing methods.

scholarly journals Generalized orthonormal moment tensor decomposition and its source-type diagram

2020 ◽  
Author(s):  
Ting-Chung Huang ◽  
Yih-Min Wu
Keyword(s):  
Seismic Moment ◽  
Decomposition Method ◽  
Moment Tensor ◽  
Tensor Decomposition ◽  
New Method ◽  
Seismic Moment Tensor ◽  
Linear Vector ◽  
Source Type ◽  
Sign Problem ◽  
Double Couple

Abstract Moment tensor decomposition is a method for deriving the isotropic (ISO), double-couple (DC), and compensated linear vector dipole (CLVD) components from a seismic moment tensor. Currently, there are two families of methods, namely, standard moment tensor decomposition and Euclidean moment tensor decomposition. Although both methods can usually provide workable solutions, there are some minor inconsistencies between the two methods: an equality inconsistency that occurs in standard moment tensor decomposition and the pure CLVD unity and flip basis inconsistency encountered in Euclidean moment tensor decomposition. Moreover, there is a sign problem when disentangling the CLVD component from a DC-dominated case. To address these minor inconsistencies, we propose a new moment tensor decomposition method inspired by both previous methods. The new method can not only avoid all these minor inconsistencies but also withstand deviations in ISO- or CLVD-dominated cases when using source-type diagrams.

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