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A ULA-Based MWC Discrete Compressed Sampling Structure for Carrier Frequency and AOA Estimation
Author(s) -
Tao Chen,
Lizhi Liu,
Dapeng Pan
Publication year - 2017
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2017.2730223
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
In this paper, we propose a new wideband digital receiver based on the modulated wideband converter (MWC) discrete compressed sampling (CS) structure, and we further propose a uniform linear array (ULA)-based MWC discrete CS structure to estimate the carrier frequency and angle-of-arrival (AOA). The proposed receiver and ULA-based system use a bank of pseudorandom sequences to mix signals to baseband and other sub-bands. The product is then low-pass filtered and down-sampled at a low rate to obtain the baseband CS data. Meanwhile, the cross-channel signal problem can be solved flexibly, since the frequency spectrum of the cross-channel signal is all mixed to the baseband. And the sensitivity can be increased a lot, because the bandwidth of the baseband is very small. However, the CS data lose the true carrier frequency and phase differences information, because of the mixing operation.Therefore, we propose to utilize the cyclic-shifted pseudorandom sequences in the ULA-based system in order to design a special phase relationship for the CS data, and on this basis, we can get the carrier frequency estimation. Then, we correct the phase differences of the CS data to estimate AOA. Finally, simulation experiments show that the proposed systems are effective and demonstrate the superior estimation performance in the case of small numbers of snapshots and low signal-to-noise ratios.

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