Estimation and Tracking of a Ballistic Target Using Sequential Importance Sampling Method

This paper deals with an efficient tracking of a ballistic target by using certain measurements from radar. An efficient non-linear model for the target along with observed error is developed. Since different targets need different models, a specific target with known properties is chosen. Here the target chosen is 9000 mm air launched ballistic missile. This generally weigh more than 5000 kg and its velocity is 2000 m/s. Since these missiles are highly accurate, a 2-D space is chosen as its path. The radar gives the range and the angle of elevation of the missile. The input data processed by state approximation is called as state estimation. Particle filter is used for this non-linear model. Here the observed noise, the processed noise and the radar noise are taken into account. The performance of particle filter is tested and verified with the simulation. By using this particle filter, the range and altitude of this ballistic target can be predicted in advance. The main reason of particle filter’s popularity is that it is very flexible and adaptive. In practical, all non-linear systems has accurate filters.

[1]  M. Jayakumar,et al.  Nonlinear Tracking of Target Submarine Using Extended Kalman Filter (EKF) , 2016, SSCC.

[2]  Ravi Kumar Jatoth,et al.  Evolutionary Computational Tools Aided Extended Kalman Filter for Ballistic Target Tracking , 2010, 2010 3rd International Conference on Emerging Trends in Engineering and Technology.

[3]  Smita Sadhu,et al.  Adaptive state estimation for ballistic object tracking with nonlinear model and state dependent process noise , 2016, 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES).

[4]  R. Mehra.  A comparison of several nonlinear filters for reentry vehicle tracking , 1971 .

[5]  S. Immediata,et al.  Impact of ballistic target model uncertainty on IMM-UKF and IMM-EKF tracking accuracies , 2006, 2006 14th European Signal Processing Conference.

[6]  Chongzhao Han,et al.  Strong tracking finite-difference extended Kalman filtering for ballistic target tracking , 2007, 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[7]  Chiman Kwan,et al.  Comparison of several ballistic target tracking filters , 2006, 2006 American Control Conference.

[8]  Ioan Domuta,et al.  Adaptive Kalman Filter for target tracking in the UWB networks , 2016, 2016 13th Workshop on Positioning, Navigation and Communications (WPNC).

[9]  Behrouz Safarinejadian,et al.  Distributed weighted averaging-based robust Cubature Kalman Filter for state estimation of nonlinear systems in wireless sensor networks , 2016, 2016 6th International Conference on Computer and Knowledge Engineering (ICCKE).

[10]  Chun-Liang Lin,et al.  Estimating evasive acceleration for ballistic targets using an extended state observer , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Shovan Bhaumik,et al.  A comparison of several nonlinear filters for ballistic missile tracking on re-entry , 2016, 2016 IEEE First International Conference on Control, Measurement and Instrumentation (CMI).

[12]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

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

[14]  M. Jayakumar,et al.  Tracking Inbound Enemy Missile for Interception from Target Aircraft Using Extended Kalman Filter , 2016, SSCC.

[15]  A. Farina,et al.  Estimation accuracy of a landing point of a ballistic target , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).