Kaichen Song
Impact in
- Aerospace Engineering top 10%
- Inertial Sensor and Navigation
- GNSS positioning and interference
- Robotics and Sensor-Based Localization
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- Geophysics and Sensor Technology
Papers in
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- Advanced Fiber Optic Sensors 12
- Semiconductor Lasers and Optical Devices 4
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- Inertial Sensor and Navigation 11
- Co-authors
- Lingyun Ye (35 shared papers)Bin Wu (9 shared papers)Weiwei Yin (2 shared papers)Hongyu Shi (2 shared papers)Junwei Liu (2 shared papers)Wei Chen (1 shared paper)Yufei Gao (1 shared paper)Tongtong Zhang (2 shared papers)
- Journals
- Sensors (11 papers)Applied Sciences (3 papers)IEEE Sensors Journal (3 papers)IEEE Photonics Technology Letters (3 papers)Measurement (2 papers)
- Partner nations
- ChinaUnited StatesAustralia
In The Last Decade
Kaichen Song
40 papers receiving 263 citations
Peers
Comparison fields: 5 of 60
- Aerospace Engineering 96
- Ocean Engineering 34
- Electrical and Electronic Engineering 125
- Artificial Intelligence 49
- Oceanography 18
Countries citing papers authored by Kaichen Song
This map shows the geographic impact of Kaichen Song's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kaichen Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kaichen Song more than expected).
Fields of papers citing papers by Kaichen Song
This network shows the impact of papers produced by Kaichen Song. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kaichen Song. The network helps show where Kaichen Song may publish in the future.
Co-authors
The 25 scholars most cited alongside Kaichen Song, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 52 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 31 | |
| 2 | 2013 | 28 | |
| 3 | 2014 | 27 | |
| 4 | 2014 | 26 | |
| 5 | 2019 | 14 | |
| 6 | 2020 | 12 | |
| 7 | 2014 | 10 | |
| 8 | 2019 | 10 | |
| 9 | 2019 | 9 | |
| 10 | 2022 | 9 | |
| 11 | 2019 | 9 | |
| 12 | 2022 | 8 | |
| 13 | 2022 | 7 | |
| 14 | 2021 | 7 | |
| 15 | 2014 | 7 | |
| 16 | 2014 | 6 | |
| 17 | 2006 | 4 | |
| 18 | 2022 | 4 | |
| 19 | 2022 | 3 | |
| 20 | 2022 | 3 |
About Kaichen Song
Kaichen Song is a scholar working on Electrical and Electronic Engineering, Aerospace Engineering, Atomic and Molecular Physics, and Optics, Ocean Engineering and Control and Systems Engineering, having authored 52 papers that have together received 269 indexed citations. Recurring topics across this work include Advanced Fiber Optic Sensors (12 papers), Inertial Sensor and Navigation (11 papers), Geophysics and Sensor Technology (6 papers), Target Tracking and Data Fusion in Sensor Networks (6 papers), Advanced Fiber Laser Technologies (5 papers), Sensor Technology and Measurement Systems (4 papers), Semiconductor Lasers and Optical Devices (4 papers) and Structural Health Monitoring Techniques (4 papers). The work is most often cited by research in Aerospace Engineering (96 citations), Ocean Engineering (34 citations), Electrical and Electronic Engineering (125 citations), Artificial Intelligence (49 citations) and Oceanography (18 citations). Kaichen Song has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Lingyun Ye, Bin Wu, Weiwei Yin, Hongyu Shi, Junwei Liu, Wei Chen, Yufei Gao, Tongtong Zhang, Tiantian Huang and Hui Jiang. Their work appears in journals such as Sensors, Applied Sciences, IEEE Sensors Journal, IEEE Photonics Technology Letters and Measurement.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.