Issue |
MATEC Web Conf.
Volume 63, 2016
2016 International Conference on Mechatronics, Manufacturing and Materials Engineering (MMME 2016)
|
|
---|---|---|
Article Number | 05034 | |
Number of page(s) | 4 | |
Section | Computer Engineering and Applications | |
DOI | https://doi.org/10.1051/matecconf/20166305034 | |
Published online | 12 July 2016 |
Real-time Compressing Algorithm based on Outer-trajectory Measurement Data
China Satellite Maritime Tracking and Controlling Department, Jiangyin 214413, Jiangsu, China
a Corresponding author: wjm_83@aliyun.com
Since huge sample datum has to be compressed properly in pre-processing to be sent out, a good compression algorithm will evidently improve the precision of the data-processing. In this paper, a compressing algorithm was studied based on polynomial fitting method. During the process of the real-time trajectory data compression, datasets were successively accumulated according to compression ratio. To apply all the information in the dataset, a series of orthogonal polynomial basis were applied to fitting the function, the least square estimation method was used to filter noise, and the estimated values of the position and the speed from differentiation of object datum in the dataset were sent out as compressed datum. And to get the best filter parameters, the mathematical expression of the error expectations and variances were studied. The compressing principle was given by considering the truncation error and random error simultaneously, which showed that, the best filter was the one by 21-point 3-order polynomial for position data compressing, while for speed data the filter by 41-point 2-order polynomial was better. The theoretical analysis and the simulation results were also provided to prove the effectiveness of this algorithm in data-compression and noise filtering.
Key words: Outer-trajectory measurement data / Polynomial fitting / Compressing / Noise filtering
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.