Linear complex iterative frequency estimation of sparse and non-sparse pulse and point processes. Bernhard, H. & Springer, A. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 1110-1114, Aug, 2017.
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Paper doi abstract bibtex
Paper doi abstract bibtex Clock frequency estimation is a key issue in many signal processing applications, e.g. network clock estimation in wireless sensor networks. In wireless systems or harsh environments, it is likely that clock events can be missed and, therefore, the observed process has to be treated as a sparse periodic process. To parameterize the clock, current research is applying periodogram estimators at a complexity of at least O(N log N). We introduce a highly accurate iterative frequency estimator for pulse signals with low computational complexity. An unbiased frequency estimator is presented with a complexity of O(N). Furthermore the mean square error (MSE), which is proportional to O(N-3) is derived and it is shown by theory and simulation that this estimator performs as well as periodogram based methods. The work concludes with simulations on sparse and non-sparse processes including a discussion of the application of the method.
@InProceedings{8081380,
author = {H. Bernhard and A. Springer},
booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
title = {Linear complex iterative frequency estimation of sparse and non-sparse pulse and point processes},
year = {2017},
pages = {1110-1114},
abstract = {Clock frequency estimation is a key issue in many signal processing applications, e.g. network clock estimation in wireless sensor networks. In wireless systems or harsh environments, it is likely that clock events can be missed and, therefore, the observed process has to be treated as a sparse periodic process. To parameterize the clock, current research is applying periodogram estimators at a complexity of at least O(N log N). We introduce a highly accurate iterative frequency estimator for pulse signals with low computational complexity. An unbiased frequency estimator is presented with a complexity of O(N). Furthermore the mean square error (MSE), which is proportional to O(N-3) is derived and it is shown by theory and simulation that this estimator performs as well as periodogram based methods. The work concludes with simulations on sparse and non-sparse processes including a discussion of the application of the method.},
keywords = {computational complexity;frequency estimation;iterative methods;mean square error methods;signal processing;wireless systems;harsh environments;clock events;sparse periodic process;periodogram estimators;highly accurate iterative frequency estimator;pulse signals;low computational complexity;unbiased frequency estimator;nonsparse processes;linear complex iterative frequency estimation;clock frequency estimation;signal processing applications;network clock estimation;wireless sensor networks;mean square error;Frequency estimation;Estimation;Clocks;Complexity theory;Random variables;Signal processing algorithms},
doi = {10.23919/EUSIPCO.2017.8081380},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570342970.pdf},
}Downloads: 0
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