Kalman Filter-based Navigation Algorithm for Multi-Radio Integrated Navigation System
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초록
Since GNSS is easily affected by jamming and/or spoofing, alternative navigation systems can be operated as backup system to prepare for outage of GNSS. Alternative navigation systems are being researched over the world, and a multi-radio integrated navigation system using alternative navigation systems such as KNSS, eLoran, Loran-C, DME, VOR has been researched in Korea. Least Square or Kalman filter can be used to estimate navigation parameters in the navigation system. A large number of measurements of the Kalman filter may lead to heavy computational load. The decentralized Kalman filter and the federated Kalman filter were proposed to handle this problem. In this paper, the decentralized Kalman filter and the federated Kalman filter are designed for the multi-radio integrated navigation system and the performance evaluation result are presented. The decentralized Kalman filter and the federated Kalman filter consists of local filters and a master filter. The navigation parameter is estimated by local filters and master filter compensates navigation parameter from the local filters. Characteristics of three Kalman filters for a linear system and nonlinear system are investigated, and the performance evaluation results of the three Kalman filters for multi-radio integrated navigation system are compared.
Abstract
Since GNSS is easily affected by jamming and/or spoofing, alternative navigation systems can be operated as backup system to prepare for outage of GNSS. Alternative navigation systems are being researched over the world, and a multi-radio integrated navigation system using alternative navigation systems such as KNSS, eLoran, Loran-C, DME, VOR has been researched in Korea. Least Square or Kalman filter can be used to estimate navigation parameters in the navigation system. A large number of measurements of the Kalman filter may lead to heavy computational load. The decentralized Kalman filter and the federated Kalman filter were proposed to handle this problem. In this paper, the decentralized Kalman filter and the federated Kalman filter are designed for the multi-radio integrated navigation system and the performance evaluation result are presented. The decentralized Kalman filter and the federated Kalman filter consists of local filters and a master filter. The navigation parameter is estimated by local filters and master filter compensates navigation parameter from the local filters. Characteristics of three Kalman filters for a linear system and nonlinear system are investigated, and the performance evaluation results of the three Kalman filters for multi-radio integrated navigation system are compared.
참고문헌 (23)
- Andrzej Banachowicz, "A Comparison of the Least Squares with Kalman Filter Methods Used in Algorithms of Fusion with Dead Reckoning Navigation Data", TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, 11(4), pp.691~695 (2017), Faculty of Navigation[DOI]
- Bar-Shalom, Y., "Estimation with Applications to Tracking and Navigation" (2001), John Wiley & Sons
- Brown, R. G., "Introduction to Random Signals and Applied Kalman Filtering" (1997), John Wiley & Sons
- Carlson, N. A., "Federated Filter for Fault-tolerant Integrated Navigation Systems", IEEE PLANS '88., Position Location and Navigation Symposium (1988)
- NEAL A. CARLSON, "Federated Kalman Filter Simulation Results", Navigation, 41(3), pp.297~322 (1994), Wiley[DOI]
- 2019 Federal Radionavigation Plan, "Department of Defense, Department of Homeland Security & Department of Transportation", ~, pp.DOT-VNTSC-OST-R-15-01 (2019)
- Edelmayer, A., "Federated Filtering Revisited: New Directions to Distributed Systems Estimation and Filtering – A Case Study", Acta Technica Jaurinensis, 4, pp.365~391 (2011)
- European Radio Navigation Plan (ERNP) 1.1 with errata, "European Commission", ~ (2018)
- Farrell, J., "The Global Positioning System and Inertial Navigation" (1999), McGraw-Hill
- FAA’s NextGen Implementation Plan, "Federal Aviation Administration", ~ (2011)
- Y. GAO, "Comparison and Analysis of Centralized, Decentralized, and Federated Filters", Navigation, 40(1), pp.69~86 (1993), Wiley[DOI]
- Qi-Tai GU, "Global Optimality for Generalized Federated Filter", Acta Automatica Sinica, 35(10), pp.1310~1316 (2009), Elsevier BV[DOI]
- F. Gustafsson, "Particle filters for positioning, navigation, and tracking", IEEE Transactions on Signal Processing, 50(2), pp.425~437 (2002), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Development of GPS Positioning and GPS, "Dead-Reckoning Integration Algorithm using Federated Kalman Filter for Land Navigation System", Jee, G.-I., 1997, KOSEF 951-0915-038-2
- Euiho Kim, "Analysis of DME", DME Navigation Performance and Ground Network Using Stretched-Front-Leg Pulse-Based DME, 2018, pp.18(10), 3275~[DOI]
- Heyone Kim, "Multi-Radio Integrated Navigation System M&S Software Design for GNSS Backup under Navigation Warfare", Electronics, 8(2), pp.188~ (2019), MDPI AG[DOI]
- 김혜원, "항법전 대응 성능 평가를 위한 다중전파측위 융복합 시스템 M&S 소프트웨어의 항법환경생성 모듈 설계", 전자공학회논문지, 56(11), pp.106~114 (2019), 대한전자공학회
- DIRK KÜGLER, "Integration of GPS and Loran-C", Chayka: A European Perspective, 1999, pp.46(1), 1~11[DOI]
- Lo, S., "Preliminary Assessment of Alternative Navigation Means for Civil Aviation", 2010 International Technical Meeting of The Institute of Navigation, pp.314~322 (2010)
- Lo, S., "Alternative Position Navigation & Timing (APNT) Based on Existing DME and UAT Ground Signals", ION GNSS 2011 (2011)
- 박슬기, "전파간섭환경에서 UAV를 활용한 선박의 백업항법시스템 설계", 한국항행학회논문지, 23(4), pp.289~295 (2019), 한국항행학회
- Wei, M., "Testing a Decentralized Filter for GPS", INS Integration, 1990, ~
- Paul Williams, "e-Navigation and the Case for eLoran", Journal of Navigation, 61(3), pp.473~484 (2008), Cambridge University Press (CUP)[DOI]