Research Article
GNSS/Low-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM
Algorithm 3
GNSS/MEMS-INS integration based on ERELM.
Input: samples . | Output: final Position prediction . | (1) Initialize: | initialize the parameter of | Algorithm 2. | (2) Assign the distribution . | (3) repeat | (4) for do ( is the total | number of samples) | (5) Use steps (1)–(12) to compute the | final prediction. | (6) end for | (7) until GPS outages happen. |
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