Heterogeneous Multi-Task Learning for Multiple Pseudo-Measurement Estimation to Bridge GPS Outages
文献类型：期刊论文
| 作者 | Lu, Shuangqiu2; Gong, Yilin2; Luo, Haiyong3; Zhao, Fang2; Li, Zhaohui2; Jiang, Jinguang1 |
| 刊名 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
 |
| 出版日期 | 2021 |
| 卷号 | 70页码:16 |
| 关键词 | Artificial intelligence (AI) and neural networks (NNs) global position system (GPS) outages inertial navigation system (INS)/GPS integrated navigation multi-task learning (MTL) multiple pseudo-measurement estimation |
| ISSN号 | 0018-9456 |
| DOI | 10.1109/TIM.2020.3028438 |
| 英文摘要 | To enhance the performance of the inertial navigation system (INS)/global position system (GPS) integrated navigation system for the land vehicle during GPS outages is an extremely challenging task. Though existing researches have made reasonable progress in positioning accuracy, they largely ignore sophisticated vehicle stopping events, and the further improvement of positioning performance is urgently needed in complex urban environments. In this article, we propose a heterogeneous multi-task learning (MTL) structure with a shared de-noising process to conduct pseudo-GPS position prediction and zero-velocity detection. The raised model builds upon three vital parts: 1) a shared de-noising convolutional autoencoder (CAE), which can effectively filter the measurement noises in the original inputs and provide more clean data for subsequent calculations without the ground-truth sensor data; 2) a predictor that uses a deep temporal convolutional network (TCN) to predict pseudo-CPS position to bridge CPS gaps; and 3) a robust zero-velocity detector that utilizes a l-D deep convolutional neural network to accurately detect the vehicle stationary pattern, allowing for timely correcting the velocity and heading. Our proposed MTL model is evaluated on extensive practical road data and achieves a root mean square error of 3.794 m for 120-s GPS outages under long-term vehicle stopping scenarios, which obviously outperforms the stand-alone long short-term memory, TCN, and TCN + CAE. Experimental results also demonstrate that our proposed MTL method yields a remarkable accuracy of over 99.0% for vehicle stationary detection. |
| 资助项目 | National Key Research and Development Program[2018YFB0505200] ; Action Plan Project of the Beijing University of Posts and Telecommunications from the Fundamental Research Funds for the Central Universities[2019XD-A06] ; Special Project for Youth Research and Innovation, Beijing University of Posts and Telecommunications, the Fundamental Research Funds for the Central Universities[2019PTB-011] ; National Natural Science Foundation of China[61872046] ; Key Research and Development Project from Hebei Province[19210404D] ; Key Research and Development Project from Hebei Province[20313701D] ; Science and Technology Plan Project of Inner Mongolia Autonomous Region[2019GG328] ; Open Project of the Beijing Key Laboratory of Mobile Computing and Pervasive Device ; Joint Research Fund for Beijing Natural Science Foundation and Haidian Original Innovation[L192004] |
| WOS研究方向 | Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000597200000007 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/16547]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Luo, Haiyong; Zhao, Fang |
| 作者单位 | 1.Wuhan Univ, GNSS Res Ctr, Wuhan 430072, Peoples R China 2.Beijing Univ Posts & Telecommun, Sch Software Engn, Beijing 100876, Peoples R China 3.Chinese Acad Sci, Beijing Key Lab Mobile Comp & Pervas Device, Inst Comp Technol, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lu, Shuangqiu,Gong, Yilin,Luo, Haiyong,et al. Heterogeneous Multi-Task Learning for Multiple Pseudo-Measurement Estimation to Bridge GPS Outages[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2021,70:16. |
| APA | Lu, Shuangqiu,Gong, Yilin,Luo, Haiyong,Zhao, Fang,Li, Zhaohui,&Jiang, Jinguang.(2021).Heterogeneous Multi-Task Learning for Multiple Pseudo-Measurement Estimation to Bridge GPS Outages.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,70,16. |
| MLA | Lu, Shuangqiu,et al."Heterogeneous Multi-Task Learning for Multiple Pseudo-Measurement Estimation to Bridge GPS Outages".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 70(2021):16. |
入库方式： OAI收割
来源：计算技术研究所
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