By Topic

Impulsive interference detection method based on Morlet wavelet and maximum likelihood estimation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Yuan Yuan He ; Research Institute of Electronics and Information, Harbin Institute of Technology, 150001, China ; Chang Jun Yu ; Tai Fan Quan ; Xin Jin

The performance of the frequency monitor system (FMS) of high frequency (HF) radar is degraded by signal corruption due to impulsive interferences such as lightning and meteor echoes. These interferers raise the FMS spectrum noise level, as a result, FMS canpsilat pick out the real minimum disturbance frequency for HF radar. It is desirable to extract the impulsive interference components from the FMS data without removing any other information. However, current wavelet detecting impulsive interference techniques, not matching impulsive interference very well, always lead to non-impulsive interference components degraded and lost. In this paper, we construct a more suitable impulsive interference detection method, by using the Morlet wavelet, matching well the shape of the impulsive interference signal, decomposition algorithm and maximum likelihood estimation thresholding rule, specifically designed to detect impulsive interference, so as to less disruption to the non-impulsive interference information when extracting the impulsive interference. The effectiveness of the proposed technique has been proved by both simulated and practical experiments.

Published in:

2008 6th IEEE International Conference on Industrial Informatics

Date of Conference:

13-16 July 2008