Switching Multiple Model Filter for Boost-Phase Missile Tracking

This paper introduces a filter for tracking a ballistic missile during its boost phase. This filter includes a new switching algorithm and a modified interacting multiple model unscented filter (IMMUF), where the Markov transition matrix is time variable. Position, velocity, and all unknown parameters of a medium-range ballistic missile model are reconstructed. Simulations demonstrate that the new filter is able to consistently estimate a missile's trajectory and all unknown parameters and to outperform the previous forms of the IMMUF.

[1]  H.F. Durrant-Whyte,et al.  A new approach for filtering nonlinear systems , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[2]  Henrique Marra Menegaz,et al.  A Systematization of the Unscented Kalman Filter Theory , 2015, IEEE Transactions on Automatic Control.

[3]  Yaakov Bar-Shalom,et al.  Estimation and Tracking: Principles, Techniques, and Software , 1993 .

[4]  Chang Liu,et al.  Parallel Interacting Multiple Model-Based Human Motion Prediction for Motion Planning of Companion Robots , 2015, IEEE Transactions on Automation Science and Engineering.

[5]  B. Ristic,et al.  Smoothed state estimation for nonlinear Markovian switching systems , 2008, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Henrique M. T. Menegaz,et al.  Interacting multiple model unscented filter for tracking a ballistic missile during its boost phase , 2017, 2017 IEEE Aerospace Conference.

[7]  Giovanni B. Palmerini,et al.  Parametric analysis of ballistic target-tracking problem by multiple model approach , 2013 .

[8]  Y. Bar-Shalom,et al.  IMM estimator versus optimal estimator for hybrid systems , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[9]  Dean A. Wilkening Airborne Boost-Phase Ballistic Missile Defense , 2004 .

[10]  A. Farina,et al.  Tracking a ballistic target: comparison of several nonlinear filters , 2002 .

[11]  D. Lainiotis Optimal adaptive estimation: Structure and parameter adaption , 1971 .

[12]  A. Farina,et al.  Tracking of a Ballistic Missile with A-Priori Information , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[13]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[14]  Branko Ristic,et al.  Performance bounds and comparison of nonlinear filters for tracking a ballistic object on re-entry , 2003 .

[15]  B. Risfic,et al.  Beyond the kalman filter - Book Review , 2004, IEEE Aerospace and Electronic Systems Magazine.

[16]  S.S. Blackman,et al.  Multiple hypothesis tracking for multiple target tracking , 2004, IEEE Aerospace and Electronic Systems Magazine.

[17]  Amir Averbuch,et al.  Interacting Multiple Model Methods in Target Tracking: A Survey , 1988 .

[18]  B. Fried,et al.  Universal Gravity Turn Trajectories , 1957 .

[19]  W. Farrell,et al.  Interacting multiple model filter for tactical ballistic missile tracking , 2008, IEEE Transactions on Aerospace and Electronic Systems.

[20]  Paul Zarchan,et al.  Boost-Phase Filtering Options: Is Simpler Better? , 2010 .

[21]  Fabrizio Piergentili,et al.  Algorithm for Missile Detection from Radar Data , 2007 .

[22]  Vesselin P. Jilkov,et al.  Survey of maneuvering target tracking: III. Measurement models , 2001 .

[23]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[24]  Y. Bar-Shalom,et al.  The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .

[25]  T. Kirubarajan,et al.  Passive geolocation and tracking of an unknown number of emitters , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[26]  Ye Tian,et al.  Distributed IMM-Unscented Kalman Filter for Speaker Tracking in Microphone Array Networks , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.