• Title/Summary/Keyword: RHKF filter

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Modified RHKF Filter for Improved DR/GPS Navigation against Uncertain Model Dynamics

  • Cho, Seong-Yun;Lee, Hyung-Keun
    • ETRI Journal
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    • v.34 no.3
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    • pp.379-387
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    • 2012
  • In this paper, an error compensation technique for a dead reckoning (DR) system using a magnetic compass module is proposed. The magnetic compass-based azimuth may include a bias that varies with location due to the surrounding magnetic sources. In this paper, the DR system is integrated with a Global Positioning System (GPS) receiver using a finite impulse response (FIR) filter to reduce errors. This filter can estimate the varying bias more effectively than the conventional Kalman filter, which has an infinite impulse response structure. Moreover, the conventional receding horizon Kalman FIR (RHKF) filter is modified for application in nonlinear systems and to compensate the drawbacks of the RHKF filter. The modified RHKF filter is a novel RHKF filter scheme for nonlinear dynamics. The inverse covariance form of the linearized Kalman filter is combined with a receding horizon FIR strategy. This filter is then combined with an extended Kalman filter to enhance the convergence characteristics of the FIR filter. Also, the receding interval is extended to reduce the computational burden. The performance of the proposed DR/GPS integrated system using the modified RHKF filter is evaluated through simulation.

Tightly Coupled INS/GPS Navigation System using the Multi-Filter Fusion Technique

  • Cho, Seong-Yun;Kim, Byung-Doo;Cho, Young-Su;Choi, Wan-Sik
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • v.1
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    • pp.349-354
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    • 2006
  • For robust INS/GPS navigation system, an efficient multi-filter fusion technique is proposed. In the filtering for nonlinear systems, the representative filter - EKF, and the alternative filters - RHKF filter, SPKF, etc. have individual advantages and weak points. The key concept of the multi-filter fusion is the mergence of the strong points of the filters. This paper fuses the IIR type filter - EKF and the FIR type filter - RHKF filter using the adaptive strategy. The result of the fusion has several advantages over the EKF, and the RHKF filter. The advantages include the robustness to the system uncertainty, temporary unknown bias, and so on. The multi-filter fusion technique is applied to the tightly coupled INS/GPS navigation system and the performance is verified by simulation.

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Multi-Filter Fusion Technique for INS/GPS (INS/GPS를 위한 다중 필터 융합 기법)

  • 조성윤;최완식;김병두;조영수
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.34 no.10
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    • pp.48-55
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    • 2006
  • A multi-filter fusion technique is proposed and this technique is applied to the INS/GPS integrated system. IIR-type EKF and FIR-type RHKF filter are fused to provide the advantages of these filters based on the adaptive mixing probability calculated by the residuals and the residual covariance matrices of the filters. In the INS/GPS, this fusion filter can provide more robust navigation information than the conventional stand-alone filter.

Improving the Performance of DR/GPS Integrated System For Land Navigation Using Sigma Point Based RHKF Filter (시그마 포인트 기반 RHKF 필터를 사용한 지상합법용 DR/GPS 결합시스템의 성능 향상)

  • Choi, Wan-Sik;Cho, Seong-Yun
    • Journal of Institute of Control, Robotics and Systems
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    • v.12 no.2
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    • pp.174-185
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    • 2006
  • This paper describes a DR construction for land navigation and the sigma point based receding horizon Kalman FIR (SPRHKF) filter for DR/GPS hybrid navigation system. A simple DR construction is adopted to improve the performance both of the pure DR navigation and the DR/GSP hybrid navigation system. In order to overcome the flaws of the EKF, the SPKF is merged with the receding horizon strategy. This filter has several advantages over the EKF, the SPKF, and the RHKF filter. The advantages include the robustness to the system model uncertainty, the initial estimation error, temporary unknown bias, and etc. The computational burden is reduced. Especially, the proposed filter works well even in the case of exiting the unmodeled random walk of the inertial sensors, which can be occurred in the MEMS inertial sensors by temperature variation. Therefore, the SPRHKF filter can provide the navigation information with good quality in the DR/GPS hybrid navigation system for land navigation seamlessly.

Performance Improvement of Low-cost DR/GPS for Land Navigation using Sigma Point Based RHKF Filter

  • Cho, Seong-Yun;Choi, Wan-Sik
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.1450-1455
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    • 2005
  • This paper describes a DR construction for land navigation and the sigma point based receding horizon Kalman FIR (SPRHKF) filter for DR/GPS hybrid navigation system. A simple DR construction is adopted to improve the performance both of the pure land DR navigation and the DR/GSP hybrid navigation system. In order to overcome the flaws of the EKF, the SPKF is merged with the receding horizon strategy. This filter has several advantages over the EKF, the SPKF, and the RHKF filter. The advantages include the robustness to the system model uncertainty, the initial estimation error, temporary unknown bias, and etc. The computational burden is reduced. Especially, the proposed filter works well even in the case of exiting the unmodeled random walk of the inertial sensors, which can be occurred in the MEMS inertial sensors by temperature variation. Therefore, the SPRHKF filter can provide the navigation information with good quality in the DR/GPS hybrid navigation system for land navigation seamlessly.

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IIR/FIR 필터 융합에 의한 INS/GPS 성능향상

  • Jo, Seong-Yun;Kim, Byeong-Du;Jo, Yeong-Su;Choe, Wan-Sik
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • v.2
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    • pp.439-442
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    • 2006
  • 본 논문에서는 IIR/FIR 필터 융합을 위한 기법을 제안하고 이를 INS/GPS 결합항법장치에 적용한다. IIR 형태의 EKF와 FIR 형태의 RHKF 필터를 각 필터의 잉여값과 잉여값 공분산을 이용한 적응형 융합 확률을 계산하여 융합함으로써 두 필터의 장점을 갖도록 한다. 이 융합 기법을 적용한 INS/GPS 결합항법장치는 기존의 단독 필터를 사용하는 것보다 강인한 특성을 갖고 항법정보를 제공한다.

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