Methodology for the Implementation of Kalman Filters on Real Applications
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-86702-7_35
Reference6 articles.
1. Azam, S.E., Chatzi, E., Papadimitriou, C., Smyth, A.: Experimental validation of the Dual Kalman filter for online and real-time state and input estimation. JVC/J. Vib. Control 23(15), 2494–2519 (2015). https://doi.org/10.1177/1077546315617672
2. Boada, B., Boada, M., Diaz, V.: Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm. Mech. Syst. Sign. Process. 72–73, 832–845 (2016)
3. Feng, B., Fu, M., Ma, H., Xia, Y., Wang, B.: Kalman filter with recursive covariance estimation-Sequentially estimating process noise covariance. IEEE Trans. Ind. Electron. 61(11), 6253–6263 (2014). https://doi.org/10.1109/TIE.2014.2301756
4. Ge, Q., Shao, T., Duan, Z., Wen, C.: Performance analysis of the kalman filter with mismatched noise covariances. IEEE Trans. Autom. Control 61(12), 4014–4019 (2016). https://doi.org/10.1109/TAC.2016.2535158
5. Kozlowski, K.: Computational requirements for a discrete Kalman filter in robot dynamics algorithms. Robotica 11(1), 27–36 (1993). https://doi.org/10.1017/S0263574700015411
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