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Volume 33 Issue 07
Jul.  2007
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Journal of Beijing University of Aeronautics and Astronautics  > 2007  >  33(07): 798-802.
Wang Wei, Dai Mingqiang, Zhang Zhihuaet al. UT-BLUE filter for target tracking[J]. Journal of Beijing University of Aeronautics and Astronautics, 2007, 33(07): 798-802. (in Chinese)
Citation: Wang Wei, Dai Mingqiang, Zhang Zhihuaet al. UT-BLUE filter for target tracking[J]. Journal of Beijing University of Aeronautics and Astronautics, 2007, 33(07): 798-802. (in Chinese)
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UT-BLUE filter for target tracking

  • School of Science, Navy University of Engineening, Wu Han 430033, China

  • Received Date: 30 Mar 2006
  • Publish Date: 31 Jul 2007
    Abstract
  • In tracking maneuvering targets application with radar, target dynamics are usually modeled in the Cartesian coordinates. In the cases target motion model are always very accurate but nonlinear ,while target measurements are directly available in the original sensor coordinates. By means of combinatin of unscented transformation and best linear unbiased filter , a new filter named unscented transformation-best linear unbiased estimator(UT-BLUE) filter was proposed to solve the above nonlinear tracking problem. In this filter, by way of nonlinear target motion model, unscented transformation was first used to predict state of the true target and its covariance, and then they were directly estimated while keeping the measurement error in sensor (polar) coordinate system. Algorithm analysis and simulation were conducted to compare it with extended Kalman filter(EKF) and unscented Kalman filter(UKF) , and results indicate that the new filter is more effective and available.

     

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  • [1] Wan E A, R van der Merwe. The unscented Kalman filter for nonlinear estimation Proceedings of Symposium 2000 on Adaptive Systems for Signal Processing, Communication and Control(AS-SPCC). :IEEE Press, 2000 [2] Zhao Zhanluo, Li X R. Optimal linear unbiased filtering with polar measurements for target tracking Proceeding of 5th International Conf Information Fusion Annapolis. MD,USA:, 2002:1527-1534 [3] Julier S J, Uhlmann J K. The scaled unscented transformation Proc Amer Control Conf, 2002:4555-4559 [4] Julier S J, Uhlmann J K. A new extension of the Kalman filter to nonlinear systems Proc of AeroSense:the 11th Int Symp, on Aerospace/Defense sensing,Simulation and Controls, 1997 [5] Li X R, Jilkov V P. A survey of maneuvering target tracking-part III:measurement models Proc 2001 SPIE Conf Signal and Data Processing of Small Targets, Vol. 4473. San Diego, CA:, 2001:423-446 [6] Kim Yong-Shik, Hong Keum-Shik ．An IMM algorithm for tracking maneuvering vehicles in an adaptive cruise control environment [J]. International Journal of Control, Automation, and Systems,2004, 2(3):310-318 [7] Li X R, Jilkov V P. A survey of maneuvering target tracking:dynamic models Proc 2000 SPIE Conf Signal and Data Processing of Small Targets , Vol. 4048-22. Orlando, FL:,2000:212-235
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