Abstract
In this paper, the inertial navigation system (INS) is combined with the wireless signal to meet the positioning requirements of the Unmanned Aerial Vehicle (UAV) system under the condition of weak or no GNSS conditions. The inertial measurement unit (IMU) is used to obtain the acceleration and angular velocity of the UAV. Meanwhile, time of arrival (TOA) and angle of arrival (AOA) information are used to measure the distance and Angle from the target to the anchors, which is a UAV with known coordinates. We established the state transition model and observation model based on the measurement information, and the estimation is performed by the extended Kalman Filter (EKF). The experimental results show that the introduction of TOA and AOA measurement information can significantly improve the whole error. The introduction of new information on the improvement of the error is remarkable, but the optimization is not as great as the increase of the number of anchors, add more measurement information not only raise the computations, may also polluted the precision because of the introduction of the new error. AOA error has a great influence on long-term results, while TOA error has a great influence on short-term results, whether it is with fewer anchors or with more anchors. This is mainly related to the topology of system. In the deficiency of anchors, we can consider introducing new information to improve the accuracy, while in the case of sufficient anchors, we need to consider a small amount of measurement information. For long-term, AOA information should be avoided as far as possible, while for short-term, AOA should be introduced to alleviate the effect of error. Therefore, different positioning methods should be adopted for different task requirements.
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References
Rui, K.: Research on formation coordination and control of multi-mobile robot system. Anhui University of Technology (2019)
Hecker, S., Dai, D., Van Gool, L.: End-to-end learning of driving models with surround-view cameras and route planners. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 449–468. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_27
Cai, G.S., Lin, H.Y., Kao, S.F.: Mobile robot localization using GPS, IMU and visual odometry. In: 2019 International Automatic Control Conference (CACS) (2019)
Yu, X.D.: Research on positioning method of autonomous vehicle based on multi-source information fusion. Harbin Institute of Technology (2020)
Liu, Y., Zhen, W.M., Zhang, F.G., et al.: Research on PNT system architecture and new technology development. Global Positioning Syst. 40(2), 48–52 (2015)
Hao, J., Wei, B.G., He, C.L.: Dynamic relative positioning method based on inertial navigation/data link. Comput. Measure. Control 26(10), 197–201 (2018)
Furgale, P., Barfoot, T.D., Sibley, G.: Continuous-time batch estimation. In: IEEE International Conference on Robotics & Automation. IEEE, Mapping and Sensor-to-Sensor Using Temporal Basis Functions, pp. 2088–2095 (2012)
Kelly, J., Sukhatme, G.S.: Visual-Inertial Sensor Fusion: Localization, Self-calibration. Sage Publications Inc, Los Angeles (2011)
Wang, C.Q., Kong, X.Q., Ding, X.H., Lu, Z.Q., Chen, C.T.: Research on fusion localization algorithm of IMU and UWB based on untrace Kalman filter. J. Nanchang Hangkong Univ. (Nat. Sci. Ed.) 34(03), 8–17 (2020)
Wang, J.X., Li, G.L., Cao, H.D.: Research on indoor location algorithm based on UWB and IMU technology fusion. Microcontroller Embed. Syst. Appl. 20(08), 42–44 (2020)
Wei, H., Wang, Z.Y., Hong, Y., Xu, J.Y., Li, M.: Indoor multi-robot autonomous navigation based on UWB positioning [A]. In: China Satellite Navigation System Management Office Academic Exchange Center. S10 PNT System and Multi-source Fusion Navigation. Academic Exchange Center of China Satellite Navigation System, p. 7. Management Office: BeiDouHui (Beijing) Science and Technology Co., Ltd. (2020)
Li, H.X.: Research on indoor positioning method based on multi-sensor combination. Nanchang University (2020)
Yan, T.F.: Research on indoor localization algorithm based on ZigBee and IMU fusion. Shanxi University (2019)
Lu, H.L.: Research on combination positioning system of mobile robot. Transducer Microsyst. 39(12), 53–56+60 (2020)
Xu, T.: Mobile robot synchronous positioning and mapping based on multi-source perception information fusion. Dalian Maritime University (2020)
Li, D.X.: Location of indoor mobile robot based on multi-sensor fusion. Zhejiang University (2018)
Zhang, W.N., Liang, X.P., Qiu, X., Xing, K.X.: Heterogeneous multi-robot cooperative localization based on laser and RGB-D camera. J. Zhejiang Univ. Technol. 47(1), 63–69 (2019)
Hua, C.H.: Research on autonomous and collaborative localization of mobile robot in unknown environment. Beijing Institute of Technology (2016)
Yang, X.Q.: Research on location algorithm based on TOA-AOA data fusion. Electron. Measure. Technol. 43(16), 104–108 (2020)
Tomic, S., Beko, M., Dims, R.: Distributed RSS-AOA based localization with unknown transmit powers. IEEE Wirel. Commun. Lett. 5(4), 392–395 (2016)
Leila, U., Leila, N., Hichem, B.: Selective hybrid RSS/AOA weighting algorithm for NLOS intra cell localization. In: Wireless Communications & Networking Conference. IEEE (2014). https://doi.org/10.1109/WCNC.2014.6952789
Zhou, Y.Q.: Research on time synchronization and location technology of wireless network for clustered robots. Harbin Engineering University (2010)
Liu, Y., Liu, Q., Yang, D.K., et al.: Hybrid TOA-TDOA positioning algorithm for ALS positioning. Adv. Mater. Res. 22(1), 73–84, 655–657, 876–881 (2013)
Ling, M.A.O., Zhenbo, L.I., Jiapin, C.H.E.N.: A short distance relative positioning method for mini-robot cluster. Semicond. Optoelectron. 38(05), 762–766 (2017)
Cheng, Y.H.: Multi-robot collaborative positioning based on sound and dead reckoning. Tianjin University (2017)
Gao, Y.B., Guan, L.W., Wang, T.J., Kuang, H.: Analysis of positioning accuracy of single axis rotating fiber optic strapdown inertial navigation system. Chin. J. Sci. Instrum. 35(04), 794–800 (2014)
He, S.B., Sun, B.G., Zhang, L.: an effective method to improve the accuracy of radio ranging and location. Commun. Technol. 52(02), 304–310 (2019)
Li, N.: Research on Optimization Scheme of AOA Estimation Based on UWB. Hainan University (2020)
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Luo, Z., Deng, Z. (2022). Positioning Method Without GNSS for Unmanned Systems Based on Fusion of IMU, TOA and AOA. In: Wu, M., Niu, Y., Gu, M., Cheng, J. (eds) Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021). ICAUS 2021. Lecture Notes in Electrical Engineering, vol 861. Springer, Singapore. https://doi.org/10.1007/978-981-16-9492-9_60
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