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基于5阶球面最简相径的改进型容积卡尔曼滤波在SINS/DVL组合导航中的应用 被引量:12

Improved fifth-degree spherical simplex sadial cubature Kalman filter in SINS/DVL integrated navigation
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摘要 为提高水下SINS/DVL组合导航系统的精度,建立了捷联惯性导航系统(SINS)的非线性误差模型,并建立多普勒测速仪的误差方程,以SINS为主导航设备建立SINS/DVL组合导航系统模型。设计了5阶球面最简相径容积卡尔曼滤波器,采用了球面最简相径采样规则改进容积卡尔曼滤波,并应用于SINS/DVL组合导航系统中。通过数学平台仿真验证了5阶球面最简相径容积卡尔曼滤波方法有效性,仿真结果表明:该方法能够有效提高SINS/DVL组合导航系统的精度,且稳定性好。 In order to improve the accuracy of SINS/DVL integrated navigation system, a SINS/DVL integrated navigation system model is built based on establishing the nonlinear error model of SINS and the error equation of the Doppler velocity log(DVL). To further improve the navigation accuracy, a fifth-degree cubature Kalman filter is applied. In view that conventional fifth-degree cubature Kalman filter has too large computation amount and relative poor robustness, a fifth-degree spherical simplex radial(SSR) cubature Kalman filter is designed, which adopts the SSR rule to change the sampling rule and reduce the number of sampling points. Simulation results show that the proposed method can effectively improve the accuracy of the SINS/DVL integrated navigation system and has better stability.
出处 《中国惯性技术学报》 EI CSCD 北大核心 2017年第3期343-348,共6页 Journal of Chinese Inertial Technology
基金 国家自然科学基金项目(51175082 61473085)
关键词 组合导航 非线性系统 球面最简相径 容积卡尔曼滤波 integrated navigation nonlinear system spherical simplex radial cubature Kalman filter
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  • 1以光衢.惯性导航原理[M].北京:航空工业出版社,1987..
  • 2[3]Oleg S, Salychev. Inertial Systems in Navigation and Geophysics [M]. MOSCOM: Bauman MSTU Press,1998.30- 41.
  • 3[5]Oleg S, Salychev. Navigation complex prototype structure and rsults of field testing [ M]. MOSCOM:Bauman MSTU Press,2002.40 - 43.
  • 4Ali J,Mirza M R U B. Performance comparison among some nonlinear filters for a low cost SINS/GPS integrated solution[J].{H}NONLINEAR DYNAMICS,2010,(03):491-502.
  • 5Hayes M P,Gough P T. Synthetic aperture sonar:A review of current status[J].IEEE Journal of Oceanic Engineering,2009,(03):207-224.
  • 6Scharcanski J,Oliveira A B,Cavalcanti P G,Yari Y. A particle filtering approach for vehicular tracking adaptive to occlusions[J].IEEE Transactions on Vehicular Techno-logy,2011,(02):381-389.
  • 7Stordal A S,Karlsen H A,N?vdal G. Bridging the ensemble Kalman filter and particle filters:the adaptive Gaussian mixture filter[J].COMPUTATIONAL GEOSCIENCES,2011,(02):293-305.
  • 8Kim J,Vaddi S S,Menon P K,Ohlmeyer E J. Comparison between nonlinear filtering techniques for spiraling ballistic missile state estimation[J].IEEE Transactions on Aeros-pace and Electronic Systems,2012,(01):313-328.
  • 9Julier S J. The spherical simplex unscented transformation[A].2003.2430-2430.
  • 10秦永元编著.惯性导航[M]. 科学出版社, 2006

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