Abstract
Dealing with noisy time series is an important task in many data-driven real-time applications. In order to improve the veracity of the measured time series data, an effective denoising method is of great significance. For some applications with online requirement, the measurement would need to be processed to get rid of noise as soon as it is obtained. In this paper, a novel method was proposed to process relatively smooth time series data with annoying complex noise based on a second-order adaptive statistics model (SASM). However, in practical process, the nonzero mean measurement noise covariance “R” was unknown, and unfortunately it usually has a huge impact on the denoising effect. Therefore, this paper proposed a self-adjustment algorithm for measurement variance searching, by means of introducing a forgetting factor to estimate “R”. In this way, “R” would be convergent to the real value reasonably fast. The effectiveness of the method was verified by the simulation experiment. The results show that the proposed method can not only make “R” to be convergent to real value but also achieve the favorable denoising effect.
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Acknowledgements
We would like to thank the editors and anonymous referees for their constructive suggestions and valuable comments. This work is partially supported by NSFC under Grant No. 61273002, 61673002, Beijing Natural Science Foundation No. 9162002 and the Key Science and Technology Project of Beijing Municipal Education Commission of China No. KZ 201510011012.
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Su, T., Yi, S., Jin, X., Kong, J. (2018). An Improved Online Denoising Algorithm Based on the Adaptive Noise Covariance. In: Zhu, Q., Na, J., Wu, X. (eds) Innovative Techniques and Applications of Modelling, Identification and Control. Lecture Notes in Electrical Engineering, vol 467. Springer, Singapore. https://doi.org/10.1007/978-981-10-7212-3_8
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DOI: https://doi.org/10.1007/978-981-10-7212-3_8
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