scholarly journals Joint 2D Direction-of-Arrival and Range Estimation for Nonstationary Sources

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jian Chen ◽  
Hui Zhao ◽  
Xiaoying Sun ◽  
Guohong Liu
Keyword(s):  
Factor Analysis ◽  
Near Field ◽  
Direction Of Arrival ◽  
Parallel Factor Analysis ◽  
Observation Data ◽  
Factor Analysis Model ◽  
Time Frequency ◽  
Multidimensional Search ◽  
Parallel Factor ◽  
Nonstationary Sources

Passive localization of nonstationary sources in the spherical coordinates (azimuth, elevation, and range) is considered, and a parallel factor analysis based method is addressed for the near-field parameter estimation problem. In this scheme, a parallel factor analysis model is firstly constructed by computing five time-frequency distribution matrices of the properly chosen observation data. In addition, the uniqueness of the constructed model is proved, and both the two-dimensional (2D) direction-of-arrival (DOA) and range can be jointly obtained via trilinear alternating least squares regression (TALS). The investigated algorithm is well suitable for near-field nonstationary source localization and does not require parameter-pairing or multidimensional search. Several simulation examples confirm the effectiveness of the proposed algorithm.

scholarly journals A New Algorithm for Computing Disjoint Orthogonal Components in the Parallel Factor Analysis Model with Simulations and Applications to Real-World Data

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2058
Author(s):  
Carlos Martin-Barreiro ◽  
John A. Ramirez-Figueroa ◽  
Xavier Cabezas ◽  
Victor Leiva ◽  
Ana Martin-Casado ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Real World ◽  
Parallel Factor Analysis ◽  
Analysis Model ◽  
Real World Data ◽  
Factor Analysis Model ◽  
World Data ◽  
Orthogonality Constraints ◽  
Orthogonal Components ◽  
Parallel Factor

In this paper, we extend the use of disjoint orthogonal components to three-way table analysis with the parallel factor analysis model. Traditional methods, such as scaling, orthogonality constraints, non-negativity constraints, and sparse techniques, do not guarantee that interpretable loading matrices are obtained in this model. We propose a novel heuristic algorithm that allows simple structure loading matrices to be obtained by calculating disjoint orthogonal components. This algorithm is also an alternative approach for solving the well-known degeneracy problem. We carry out computational experiments by utilizing simulated and real-world data to illustrate the benefits of the proposed algorithm.

scholarly journals A novel time–frequency–space method with parallel factor theory for big data analysis in condition monitoring of complex system

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091694 ◽  
Author(s):  
Liu Yang ◽  
Hanxin Chen ◽  
Yao Ke ◽  
Lang Huang ◽  
Qi Wang ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Condition Monitoring ◽  
Spatial Information ◽  
Component Analysis ◽  
Parallel Factor Analysis ◽  
Time Signal ◽  
Dynamic Feature ◽  
Frequency Space ◽  
Time Frequency ◽  
Parallel Factor

The spatial information of the signal is neglected by the conventional frequency/time decompositions such as the fast Fourier transformation, principal component analysis, and independent component analysis. Framing of the data being as a three-way array indexed by channel, frequency, and time allows the application of parallel factor analysis, which is known as a unique multi-way decomposition. The parallel factor analysis was used to decompose the wavelet transformed ongoing diagnostic channel–frequency–time signal and each atom is trilinearly decomposed into spatial, spectral, and temporal signature. The time–frequency–space characteristics of the single-source fault signal was extracted from the multi-source dynamic feature recognition of mechanical nonlinear multi-failure mode and the corresponding relationship between the nonlinear variable, multi-fault mode, and multi-source fault features in time, frequency, and space was obtained. In this article, a new method for the multi-fault condition monitoring of slurry pump based on parallel factor analysis and continuous wavelet transform was developed to meet the requirements of automatic monitoring and fault diagnosis of industrial process production lines. The multi-scale parallel factorization theory was studied and a three-dimensional time–frequency–space model reconstruction algorithm for multi-source feature factors that improves the accuracy of mechanical fault detection and intelligent levels was proposed.

Sign in / Sign up

Export Citation Format

Share Document