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Filtering techniques using Markov jump processes applied to maneuvering target tracking

Author(s):
Victor Baptista Frencl
Total Authors: 1
Document type: Master's Dissertation
Institution: Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação
Defense date:
Examining board members:
Karl Heinz Kienitz; Wagner Caradori do Amaral
Advisor: João Bosco Ribeiro do Val
Abstract

The dissertation's theme is the study of the maneuvering target tracking problem from dynamic systems modeling using markovian jumps on the transitions between models, recursive stochastic filters and filtering techniques. Surveys and analysis of two types of dynamic models were made: the constant velocity model and the constant turn model. Based on these models, some variations were prepared. Observations models were also studied, proposing the inclusion of the radial velocity in the target observations. The studied filters were the extended Kalman filter, which deals with nonlinear mathematical models, and the BLUE filter, which deals with linear dynamics and observations models which envolves coordinates conversions. The filtering techniques of the interacting multiple models, which involves the switching between models, and the particle filter, which is based on Monte Carlo simulations, were studied, proposing some variation of these techniques. We developed a methodology, using numerical simulations on MATLAB software, to compare performances of some of the filtering techniques based on these studies (AU)

FAPESP's process: 07/06292-2 - Filtering Techniques Based on Markov Jump Processes Applied to Manuevering Target Tracking
Grantee:Victor Baptista Frencl
Support type: Scholarships in Brazil - Master