ANALYSES OF MARGINALIZED PARTICLE FILTERING BLOCK OF NAVIGATION DATA

Authors

  • M. P. Mukhina National Aviation University
  • A. P. Prymak National Aviation University

DOI:

https://doi.org/10.18372/1990-5548.52.11867

Keywords:

Particle filter, marginalized particle filter, filtering block, correlation extreme navigation system

Abstract

The particle filter offers a general numerical tool to approximate the posterior density function for the state in nonlinear and non-Gaussian filtering problems. While the particle filter is fairly easy to implement and tune, its main drawback is that it is quite computer intensive, with the computational complexity increasing quickly with the state dimension. One remedy to this problem is to marginalize out the
states appearing linearly in the dynamics. The result is that one Kalman filter is associated with each particle. Filtering block has been developed with the help of which navigation data received from UAV is filtered. UAV motion with camera on board has been conducted and photos have been captured from it. Photos have been processed by OpenSurf method, with the help of which feature points has been detected,
filtered and compared with previous image. Result of research shows that with help of comparing of two neighboring images we can reconstruct relief above which UAV flew.

Author Biographies

M. P. Mukhina, National Aviation University

Educational & Research Institute of Information and Diagnostic Systems

Doctor of Engineering Science. Professor.

A. P. Prymak, National Aviation University

Educational & Research Institute of Information and Diagnostic Systems

Post-graduate student

References

Thomas Schцn, Fredrik Gustafsson, and Per-Johan Nordlund, “Marginalized Particle Filters for Mixed Linear/Nonlinear State-Space Models,” IEEE Transactions on signal processing, vol. 53, no. 7, July 2005, pp. 2279–2289.

Sebastian Thrun, Dieter Fox, and Wolfram Burgard, Probabilistic Robotics, 1999-2000, 477 p.

Francisco Josй Curado Mendes Teixeira, Terrain-Aided Navigation and Geophysical Navigation of Autonomous Underwater Vehicles, 2007, 131 p.

M. P. Mukhina and I. V. Seden, “Analysis of modern correlation extreme navigation systems,” Electronics and Control Systems, Kyiv, no. 1(39), pp. 95–101, 2014.

Modern information technologies in problems of navigation and targeting maneuverable unmanned aerial vehicles. Еdited by M. N Krasilshikov and G. G Sebryakov, Moscow, Fizmatlit, 2009, 556 p. (In Russian)

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Issue

Vol. 2 No. 52 (2017)

Section

AUTOMATIC CONTROL SYSTEMS

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