Abstract
Multiple source information-based autonomous navigation is essentially an estimation fusion problem. This chapter will go deep into the estimation fusion algorithm. Section 3.1 presents linear models and algorithms, mainly including linear unified model and its fusion algorithm as well as covariance intersection algorithm in the distributed fusion, which can be used to estimate the parameters of a static system and states of a dynamic system. Sections 3.2 and 3.3 describe the centralized Kalman filter and distributed Kalman filter of a dynamic system, respectively, and discuss several implementation models of centralized filtering and typical models of distributed filtering.
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Wang, D., Li, M., Huang, X., Zhang, X. (2021). Estimation Fusion Algorithm. In: Spacecraft Autonomous Navigation Technologies Based on Multi-source Information Fusion. Space Science and Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-15-4879-6_3
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DOI: https://doi.org/10.1007/978-981-15-4879-6_3
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