TR2020-097

Optimal Measurement Projections with Adaptive Mixture Kalman Filtering for GNSS Positioning


    •  Greiff, M., Berntorp, K., "Optimal Measurement Projections with Adaptive Mixture Kalman Filtering for GNSS Positioning", American Control Conference (ACC), DOI: 10.23919/ACC45564.2020.9147675, July 2020, pp. 4435-4441.
      BibTeX TR2020-097 PDF
      • @inproceedings{Greiff2020jul,
      • author = {Greiff, Marcus and Berntorp, Karl},
      • title = {Optimal Measurement Projections with Adaptive Mixture Kalman Filtering for GNSS Positioning},
      • booktitle = {American Control Conference (ACC)},
      • year = 2020,
      • pages = {4435--4441},
      • month = jul,
      • doi = {10.23919/ACC45564.2020.9147675},
      • url = {https://www.merl.com/publications/TR2020-097}
      • }
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  • Research Areas:

    Machine Learning, Optimization, Signal Processing

Accurate carrier-phase integer ambiguity resolution is fundamental for high precision global navigation satellite systems (GNSSs). In this paper we extend a recently proposed mixture Kalman filter solution to integer ambiguity resolution. We utilize the Fisher information matrix to project the acquired measurements into a lower-dimensional subspace, formulating an optimization program to find the projected measurement that minimally degrades filter performance with respect to the mean squared error (MSE) of the estimate. Using the projected measurements, our method achieves a significant computational speedup while retaining the performance of the original filter. Theoretical results are presented regarding the optimal projection computation, and the claims are subsequently illustrated by simulation examples in a Monte Carlo study

 

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