Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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FIR Fixed-Interval Smoothing Filter for Discrete Nonlinear System with Modeling Uncertainty and Its Application to DR/GPS Integrated Navigation System

모델링 불확실성을 갖는 이산구조 비선형 시스템을 위한 유한 임펄스 응답 고정구간 스무딩 필터 및 DR/GPS 결합항법 시스템에 적용

  • Cho, Seong Yun (Convergence Technology Research Laboratory, Electronics and Telecommunications Research Institute) ;
  • Kim, Kyong-Ho (Convergence Technology Research Laboratory, Electronics and Telecommunications Research Institute)
  • 조성윤 (한국전자통신연구원 융합기술연구부문) ;
  • 김경호 (한국전자통신연구원 융합기술연구부문)
  • Received : 2012.11.14
  • Accepted : 2013.03.27
  • Published : 2013.05.01
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Abstract

This paper presents an FIR (Finite Impulse Response) fixed-interval smoothing filter for fast and exact estimating state variables of a discrete nonlinear system with modeling uncertainty. Conventional IIR (Infinite Impulse Response) filter and smoothing filter can estimate state variables of a system with an exact model when the system is observable. When there is an uncertainty in the system model, however, conventional IIR filter and smoothing filter may cause large errors because the filters cannot estimate the state variables corresponding to the uncertain model exactly. To solve this problem, FIR filters that have fast estimation properties and have robustness to the modeling uncertainty have been developed. However, there is time-delay estimation phenomenon in the FIR filter. The FIR smoothing filter proposed in this paper makes up for the drawbacks of the IIR filter, IIR smoothing filter, and FIR filter. Therefore, the FIR smoothing filter has good estimation performance irrespective of modeling uncertainty. The proposed FIR smoothing filter is applied to the integrated navigation system composed of a magnetic compass based DR (Dead Reckoning) and a GPS (Global Positioning System) receiver. Even when the magnetic compass error that changes largely as the surrounding magnetic field is modeled as a random constant, it is shown that the FIR smoothing filter can estimate the varying magnetic compass error fast and exactly with simulation results.

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References

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