Nonlinear Multi-system Interactive Positioning Algorithms

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 571)


The Bayesian probabilistic observation model is established by using the interactive input of multi-system observation data. The positioning information between multi-system is directly interacted. The non-linear problem of the observation system is solved by the extended Kalman filter theory. Moreover, the system probability is updated in real time by using the filtering innovation and variance of each system, and the estimated results are fused with each weight to output. The simulation results show that the proposed algorithm has better stability and adaptability than the traditional location algorithm under the same observation conditions.


Nonlinear Extend kalman filter Multi-system Interactive algorithms 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.University of Chinese Academy of SciencesBeijingChina
  2. 2.Department of Navigation SystemAerospace Information Research Institute of Chinese Academy of SciencesBeijingChina

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