NDLI: Bias compensation in the instantaneous frequency estimators based on the time–frequency representations and ICI algorithm

Content Provider	IET Digital Library
Author	Djurović, Igor
Abstract	In this study, the author has proposed a technique for a bias compensation in the intersection-of-the-confidence-intervals (ICI) algorithm for the instantaneous frequency (IF) estimation. Algorithms from the ICI class are based on selection of the signal adaptive window length in the time–frequency representation-based IF estimators giving a trade-off between a bias and variance. The main difficulty in these estimators is sensitivity to a high noise influence that is producing outliers in estimates used for the ICI algorithm initialisation. Several ICI algorithm modifications are proposed in order to achieve robustness to the high noise influence. However, these techniques can suffer from the emphatic bias. The proposed bias compensation algorithm is based on the estimation of high-order IF derivative. It gives improvement in the mean squared error for more than 5 dB with respect to the state-of-the-art variants of the ICI algorithm.
Starting Page	765
Ending Page	770
Page Count	6
ISSN	17519675
Volume Number	11
e-ISSN	17519683
Issue Number	Issue 6, Aug (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/11/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2016.0618
Journal	IET Signal Processing
Publisher Date	2017-04-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Signal Processing Bias Compensation Algorithm Compensation Frequency Estimation High Noise Influence Sensitivity High-order IF Derivative Estimation ICI Algorithm Instantaneous Frequency Estimators Intersection-of-the-confidence-intervals Algorithm Mathematical Analysis Signal Adaptive Window Length Selection Signal Processing And Detection Signal Processing Theory Statistics Time-frequency Analysis Time-frequency Representations
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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