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      Application of Mixed Kalman Filter to Passive Radar Target Tracking

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          Abstract

          To improve the estimation accuracy of the error covariance matrix in Unscented Kalman Filter (UKF). With the passive radar target tracking model, a novel Mixed Kalman Filter (MKF) is proposed, Firstly, the UKF is used to conduct a posteriori estimate for target state, and then re-establish a measurement equation, the posteriori estimated value of state by UKF is transformed into a measured value of the new measurement equation, and through linear Kalman Filter the state is best estimated secondly, improving the precision of target state estimation. Experimental results indicate that MKF algorithm significantly improves the performance of passive radar target tracking, compared with the Extended Kalman Filter (EKF) and UKF.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 January 2015
          : 3
          : 6
          : 652-659
          Affiliations
          [1 ] National Key Laboratory of Radar Signal Processing, Xidian University
          Article
          d82b010c78ed45ada8e7b25de5383d5c
          10.12000/JR14113

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

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