New α-β Filters in Terms of an N-step Predicted Position(Radar, Navigation and Communications,ICSANE 2010 (International Conference on Space, Aeronautical and Navigational Electronics))
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概要
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We present new α-β filters that improve the tracking performance in terms of estimated steady state n-step predicted position as compared with conventional α-β filters such as an RA (Random Acceleration) filter, an RV (Random Velocity) filter, an MV (Minimum Variance) filter, and a BV (Best Velocity) filter. Here, an RA filter and an RV filter can be obtained from Kalman filter equations. An MV filter minimizes the steady state variance of estimated position for a constant velocity target and a BV filter minimizes that of estimated velocity for a constant velocity target under the condition that the steady state position error for a constant acceleration target is constant.
- 2010-10-20
著者
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KOSUGE Yoshio
Faculty of Engineering, Nagasaki University
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Kosuge Yoshio
Faculty Information R & D Research Center:mitsubishi Electric Corp.
関連論文
- New α-β Filters in Terms of an N-step Predicted Position(Radar, Navigation and Communications,ICSANE 2010 (International Conference on Space, Aeronautical and Navigational Electronics))
- Target Tracking for Maneuvering Targets Using Multiple Model Filter(Special Section on the Trend of Digital Signal Processing and Its Future Direction)
- Optimal Gains of an α-β Filter in Terms of Estimated Velocity(WSANE2007)
- Tracking Performance Improvement of an α-β Filter in Terms of Steady State Velocity(WSANE 2009 (Workshop for Space, Aeronautical and Navigational Electronics))
- Comparison of Aircraft Tracking Transient Response Between a Kalman Filter and a Linear Least Squares Filter(Technical Session,ICSANE 2011(International Conference on Space, Aeronautical and Navigational Electronics 2011))
- Non-process-noise Tracking Filter Using a 6-Dimensional Target Model(WSANE 2008 (Workshop for Space, Aeronautical and Navigational Electronics))
- Comparison of Aircraft Tracking Transient Response Between a Kalman Filter and a Linear Least Squares Filter