An INS/UWB joint indoor positioning algorithm based on hypothesis testing and yaw angle
Author:
Funder
National Natural Science Foundation of China
Natural Science Foundation of Hebei Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11276-024-03777-3.pdf
Reference21 articles.
1. Zhang, Y., Tan, X., & Zhao, C. (2020). UWB/INS integrated pedestrian positioning for robust indoor environments. IEEE Sensors Journal, 20(23), 14401–14409.
2. Shi, Q., Cui, X., Zhao, S., & Lu, M. (2020). Sequential TOA-based moving target localization in multi-agent networks. IEEE Communications Letters, 24(8), 1719–1723.
3. Cao, S., Chen, X., Zhang, X., & Chen, X. (2020). Combined weighted method for TDOA-based localization. IEEE Transactions on Instrumentation and Measurement, 69(5), 1962–1971.
4. Yang, B., Guo, L., Guo, R., Zhao, M., & Zhao, T. (2020). A novel trilateration algorithm for RSSI-based indoor localization/. IEEE Sensors Journal, 20(14), 8164–8172.
5. Li, P., Xu, Y., Shen, T., & Bi, S. (2019). INS/UWB integrated AGV localization employing Kalman filter for indoor LOS/NLOS mixed environment. In 2019 International Conference on Advanced Mechatronic Systems (ICAMechS), Kusatsu, Japan, 2019 (pp. 294–298).
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