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An improved time support estimation method for overlapping automatic dependent surveillance‐broadcast signals in low signal‐to‐noise ratio region
Author(s) -
Ren Peng,
Wang Jianxin,
Zhang Peixin,
Tian Da
Publication year - 2020
Publication title -
international journal of satellite communications and networking
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.388
H-Index - 39
eISSN - 1542-0981
pISSN - 1542-0973
DOI - 10.1002/sat.1388
Subject(s) - computer science , noise (video) , algorithm , false alarm , filter (signal processing) , variance (accounting) , signal (programming language) , constant (computer programming) , signal to noise ratio (imaging) , projection (relational algebra) , constant false alarm rate , eigendecomposition of a matrix , pattern recognition (psychology) , telecommunications , artificial intelligence , eigenvalues and eigenvectors , computer vision , accounting , business , image (mathematics) , programming language , physics , quantum mechanics
Summary In the satellite‐based application, due to the low signal‐to‐noise ratio (SNR) of received signals, it is highly difficult to estimate the time support of overlapping automatic dependent surveillance‐broadcast (ADS‐B) signals, resulting in the failure of many separation algorithms. Besides, it is troublesome to determine the detection threshold for unknown noise variance. In this paper, we first present three estimation methods of noise variance based on eigenvalue decomposition, which are suitable for constant and slowly varying noise variance, respectively. The proposed time support estimation is therefore of constant false alarm rate property. Moreover, according to the characteristic of ADS‐B signals, we design a matched filter and propose two estimation schemes of direct form and indirect form, which significantly improves the estimation accuracy and detection probability. Finally, in combination of the well‐known projection algorithm (PA), it is concluded that with the proposed estimation method, PA exhibits a satisfactory separation performance at low SNRs.