@inproceedings{4fd8c158231b4d4b8281a4138bb955a7,
title = "DOA Tracking Algorithm for the Time-varying Number of Signal Sources",
abstract = "At present, sparse Bayesian learning (SBL) is introduced into direction of arrival (DOA) estimation for both coherent and incoherent signals. Instead of directly extending DOA estimation to DOA tracking, we construct an array data model including DOA change of adjacent time to decrease computational complexity and avoid grid effect. Thus, we regard DOA tracking as a parameter estimation problem in terms of Taylor expansion and Bayesian rule. Owing to the existence of hidden variables, we adopt the Expectation Maximization (EM) algorithm to calculate the DOA change. More importantly, we realize DOA tracking by utilizing the estimated signal power and noise power when the number of signal sources varies with time. The proposed method is numerically evaluated with an assumption of uniform linear array. The results show that the proposed algorithm has higher tracking accuracy over conventional methods.",
keywords = "Bayesian estimation, DOA tracking, Taylor expansion, time-varying number of signal sources.",
author = "Yulong Gao and Yanping Chen and Huang Wang and Shaochuan Wu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 30th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2019 ; Conference date: 08-09-2019 Through 11-09-2019",
year = "2019",
month = sep,
doi = "10.1109/PIMRC.2019.8904306",
language = "英语",
series = "IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2019",
address = "美国",
}