IEICE TRANSACTIONS on Fundamentals
Sparse High-Noise GPS Trajectory Data Compression and Recovery Based on Compressed Sensing
Summary :
With the extensive use of location based devices, trajectories of various kinds of moving objects can be collected and stored. As time going on, the volume of trajectory data increases exponentially, which presents a series of problems in storage, transmission and analysis. Moreover, GPS trajectories are never perfectly accurate and sometimes with high noise. Therefore, how to overcome these problems becomes an urgent task in trajectory data mining and related applications. In this paper, an adaptive noise filtering trajectory compression and recovery algorithm based on Compressed Sensing (CS) is proposed. Firstly, a noise reduction model is introduced to filter the high noise in GPS trajectories. Secondly, the compressed data can be obtained by the improved GPS Trajectory Data Compression Algorithm. Thirdly, an adaptive GPS trajectory data recovery algorithm is adopted to restore the compressed trajectories to their original status approximately. Finally, comprehensive experiments on real and synthetic datasets demonstrate that the proposed algorithm is not only good at noise filtering, but also with high compression ratio and recovery performance compared to current algorithms.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E101-A No.5 pp.811-821
- Publication Date
- 2018/05/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E101.A.811
- Type of Manuscript
- PAPER
- Category
- Mobile Information Network and Personal Communications
Authors
Guan YUAN
China University of Mining and Technology
Mingjun ZHU
China University of Mining and Technology
Shaojie QIAO
Chengdu University of Information Technology
Zhixiao WANG
China University of Mining and Technology
Lei ZHANG
China University of Mining and Technology
Keyword
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Guan YUAN, Mingjun ZHU, Shaojie QIAO, Zhixiao WANG, Lei ZHANG, "Sparse High-Noise GPS Trajectory Data Compression and Recovery Based on Compressed Sensing" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 5, pp. 811-821, May 2018, doi: 10.1587/transfun.E101.A.811.
Abstract: With the extensive use of location based devices, trajectories of various kinds of moving objects can be collected and stored. As time going on, the volume of trajectory data increases exponentially, which presents a series of problems in storage, transmission and analysis. Moreover, GPS trajectories are never perfectly accurate and sometimes with high noise. Therefore, how to overcome these problems becomes an urgent task in trajectory data mining and related applications. In this paper, an adaptive noise filtering trajectory compression and recovery algorithm based on Compressed Sensing (CS) is proposed. Firstly, a noise reduction model is introduced to filter the high noise in GPS trajectories. Secondly, the compressed data can be obtained by the improved GPS Trajectory Data Compression Algorithm. Thirdly, an adaptive GPS trajectory data recovery algorithm is adopted to restore the compressed trajectories to their original status approximately. Finally, comprehensive experiments on real and synthetic datasets demonstrate that the proposed algorithm is not only good at noise filtering, but also with high compression ratio and recovery performance compared to current algorithms.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.811/_p
Copy
@ARTICLE{e101-a_5_811,
author={Guan YUAN, Mingjun ZHU, Shaojie QIAO, Zhixiao WANG, Lei ZHANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Sparse High-Noise GPS Trajectory Data Compression and Recovery Based on Compressed Sensing},
year={2018},
volume={E101-A},
number={5},
pages={811-821},
abstract={With the extensive use of location based devices, trajectories of various kinds of moving objects can be collected and stored. As time going on, the volume of trajectory data increases exponentially, which presents a series of problems in storage, transmission and analysis. Moreover, GPS trajectories are never perfectly accurate and sometimes with high noise. Therefore, how to overcome these problems becomes an urgent task in trajectory data mining and related applications. In this paper, an adaptive noise filtering trajectory compression and recovery algorithm based on Compressed Sensing (CS) is proposed. Firstly, a noise reduction model is introduced to filter the high noise in GPS trajectories. Secondly, the compressed data can be obtained by the improved GPS Trajectory Data Compression Algorithm. Thirdly, an adaptive GPS trajectory data recovery algorithm is adopted to restore the compressed trajectories to their original status approximately. Finally, comprehensive experiments on real and synthetic datasets demonstrate that the proposed algorithm is not only good at noise filtering, but also with high compression ratio and recovery performance compared to current algorithms.},
keywords={},
doi={10.1587/transfun.E101.A.811},
ISSN={1745-1337},
month={May},}
Copy
TY - JOUR
TI - Sparse High-Noise GPS Trajectory Data Compression and Recovery Based on Compressed Sensing
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 811
EP - 821
AU - Guan YUAN
AU - Mingjun ZHU
AU - Shaojie QIAO
AU - Zhixiao WANG
AU - Lei ZHANG
PY - 2018
DO - 10.1587/transfun.E101.A.811
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2018
AB - With the extensive use of location based devices, trajectories of various kinds of moving objects can be collected and stored. As time going on, the volume of trajectory data increases exponentially, which presents a series of problems in storage, transmission and analysis. Moreover, GPS trajectories are never perfectly accurate and sometimes with high noise. Therefore, how to overcome these problems becomes an urgent task in trajectory data mining and related applications. In this paper, an adaptive noise filtering trajectory compression and recovery algorithm based on Compressed Sensing (CS) is proposed. Firstly, a noise reduction model is introduced to filter the high noise in GPS trajectories. Secondly, the compressed data can be obtained by the improved GPS Trajectory Data Compression Algorithm. Thirdly, an adaptive GPS trajectory data recovery algorithm is adopted to restore the compressed trajectories to their original status approximately. Finally, comprehensive experiments on real and synthetic datasets demonstrate that the proposed algorithm is not only good at noise filtering, but also with high compression ratio and recovery performance compared to current algorithms.
ER -
English
