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Evolutionary Optimization for Tuning Barometer-Aided Inertial Navigation System Vertical Channel Mechanization

EasyChair Preprint no. 11238

8 pagesDate: November 3, 2023


This work revisits the vertical channel mechanization problem of barometer-aided Inertial Navigation Systems (INS) and proposes a new approach to tune the gains of such a control loop. As a main contribution, it presents a performance analysis of a novel tuning method based on multi-objective optimization using evolutionary algorithms. To validate the performance of the optimized tunings, signal norms, and statistical measures are used to describe and analyze the vertical channel errors of the vertical channel control loop, which are then compared with a traditional empirical solution and with the one obtained via a recently proposed method based on Linear Quadratic Regulator (LQR). According to the experimental results, the proposed optimization technique demonstrates its reliability/robustness over multiple different data sets, as long as the data set used in the optimization procedure has dynamical characteristics that correlate to the maneuver in the evaluation data sets.

Keyphrases: Barometer, Genetic Algorithm, Inertial Navigation System, multi-objective optimization, Vertical channel damping

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Vinícius M. G. B. Cavalcanti and Felipe O. Silva and Álvaro H. A. Maia and Danilo A. de Lima and Alexandre C. Leite and Jay A. Farrell},
  title = {Evolutionary Optimization for Tuning Barometer-Aided Inertial Navigation System Vertical Channel Mechanization},
  howpublished = {EasyChair Preprint no. 11238},

  year = {EasyChair, 2023}}
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