Abstract
For linear discrete random time-varying systems with unknown inputs, an improved two-stage Kalman filter algorithm is presented to simultaneously estimate the state and generalized deviation of the sensors in the micro-electromechanical system. Based on this algorithm, a multi-sensor fusion filter estimation is implemented. First, the offset dynamic model without unknown input is derived by dimension reduction decoupling; secondly, an auxiliary full-row rank matrix is added to decouple the bias noise from the observation noise; finally, a two-stage Kalman filter is constructed with a stateless offset filter and a state filter to estimate the state and bias. Then multi-sensor fusion filtering is performed based on this algorithm. The simulation results show that the system bias, state estimation error and root mean square error of the method and its fusion algorithm are significantly reduced. The improved accuracy proves that this method is very effective.
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References
Xiaoju, Y., Yangwang, F., Youli, W., Pengfei, Y.: An asynchronous sensor bias estimation algorithm utilizing targets positions only. Inf. Fusion 27, 54–63 (2016). https://doi.org/10.1016/j.inffus.2015.05.003
Wenqiang, P., Ya-Feng, L., Junkun, Y., Hongwei, L., Zhiquan, L.: Optimal estimation of sensor biases for asynchronous multi-sensor data fusion. Math. Program. 170(1), 357–386 (2017). https://doi.org/10.1007/s10107-018-1304-2
Fan, W., Zidong, W., Jinling, L., Xiaohui, L.: Recursive state estimation for two-dimensional shift-varying systems with random parameter perturbation and dynamical bias. Automatica 112, 108658 (2020). https://doi.org/10.1016/j.automatica.2019.108658
Xueqin, C., Ming, L.: A two-stage extended Kalman filter method for fault estimation of satellite attitude control systems. J. Frankl. Inst. 354(2), 872–886 (2017). https://doi.org/10.1016/j.jfranklin.2016.06.034
Zhihua, L., Mengyao, Z., Qingwei, Y., Yu, Z.: Performance analysis of two EM-based measurement bias estimation processes for tracking systems. Front. Inf. Technol. Electron. Eng. (2018). https://doi.org/10.1631/FITEE.1800214
Ying, L., Hao, Q., Zhichao, W., Sujuan, L.: Data fusion based multi-rate Kalman filtering with unknown input for on-line estimation of dynamic displacements. Measurement 131, 211–218 (2019). https://doi.org/10.1016/j.measurement.2018.08.057
Fang, J., Hong, L.J.: A simulation-based estimation method for bias reduction. IISE Trans. 50(1),14–26 (2018). https://doi.org/10.1080/24725854.2017.1382751
Zhou, J., Liang, Y., Yang, F., et al.: Multi-sensor consensus estimation of state, sensor biases and unknown input. Sensors 16(9), 1407 (2016). https://doi.org/10.3390/s16091407
Zhou, J., Song, G., Qiang, S., Yu, L., Chaobo, C.: High-accuracy parallel two-stage estimator for generalized bias of micro sensor with unknown input. In: 2019 22th International Conference on Information Fusion (FUSION), Ottawa, ON, Canada, pp. 1–5 (2019)
Acknowledgements
This work is supported in part by the National Key Research and Development Program Project of China (2016YFE0111900), in part by the Chinese National Science Foundation (51705430), in part by Shaanxi Province Key Project of Research and Development Plan (2019GY-066), in part by Natural Science Foundation of Shaanxi Provincial Department of Education (19JK0407), in part by Taicang Science and Technology Planning Project (TC2018DYDS19).
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Jie, Z., Yu, L., Chaobo, C., Song, G. (2022). Two-Stage Estimator for Multi-micro Sensor with Generalized Bias Derived by Unknown Input. In: Yan, L., Duan, H., Yu, X. (eds) Advances in Guidance, Navigation and Control . Lecture Notes in Electrical Engineering, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-15-8155-7_357
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DOI: https://doi.org/10.1007/978-981-15-8155-7_357
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