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Blind PARAFAC-Based DOA and Polarization Estimation Algorithm for Polarization Sensitive Array

Author: ShiZuo
Tutor: ZhangXiaoFei
School: Nanjing University of Aeronautics and Astronautics
Course: Communication and Information System
Keywords: polarization sensitive array parameters estimation DOA estimation polarization estimation PARAFAC Trilinear model quadrilinear model
CLC: TN911.23
Type: Master's thesis
Year: 2009
Downloads: 53
Quote: 1
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Abstract


Polarization Sensitive Array (PSA) can acquire polarization information of electromagnetic signals. Compared to the common array, PSA has many advantages such as resisting interference effectively, detecting signals robustly, high resolution and polarization multiple access. So it has a good prospect of application in radar, communication, navigation and so on. The field about estimating parameters for polarization sensitive array has flourished in recent years.PARAFAC(parallel factor, which also can be called as trilinear decomposition model)was first introduced as a data analysis tool in the physiology. And most of its application is in chemometrics, spectroscopy and chromatographic scopes. It is a measure of multidimensional data analyses.In signal processing field, PARAFAC can be thought of as a generalization of ESPRIT and joint approximate diagonalization ideas.Recently, PARAFAC technique is becoming widely used in the field of signal processing and communications.The contents of this essay play vital role in the project(60801052:Applications in Polarization Sensitive Arrays with trilinear/multi-decomposition), which’s supported by National Natural Science Fund.The received signal of the Polarization Sensitive Array is proved to have trilinear model characteristics, so blind PARAFAC-based DOA and polarization estimation algorithm for polarization sensitive array is investigated in this paper ,which relies on the uniqueness of low-rank three-way array decomposition and the trilinear alternating least squar(eTALS)algorithm. TALS is used to attain direction matrix and polarization matrix, and then estimate DOA and polarization parameters according to the estimated direction matrix and polarization matrix. The PSA for research includes Uniform-Linear-Array (ULA) , Uniform-Circular-Array (UCA) , L-array and Uniform-Square-Array (USA),all of which are divided into double dipoles and triple dipoles respectively. When examining USA, we further propose blind quadrilinear decomposition-based DOA and polarization estimation algorithm. The identifiabilities of various arrays are also strictly differentiated in the paper. The simulation results reveal that the proposed algorithm, which relies on a fundamental result of the uniqueness of low-rank three-way/four-way data decomposition has better DOA and polarization estimation performance, and even supports small sample sizes. It never depends on any pilot or training sequence and never needs any statistical property. An advantage of our proposed algorithm over the classical subspace based algorithms, such as ESPRIT and MUSIC, is that it does not apply any eigenvalue decomposition (EVD) to the cross spectral matrix or singular value decomposition (SVD) to the received data. In general, our proposed algorithm is a generalization of ESPRIT and joint approximate diagonalization ideas. Besides, it’s iterative method, which only requires fewer iterations for convergence.

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Communication theory > Information Theory > Signal detection and estimation
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