@article{(Open Science Index):https://publications.waset.org/pdf/10007418,
	  title     = {Adaptive Extended Kalman Filter for Ballistic Missile Tracking},
	  author    = {Gaurav Kumar and  Dharmbir Prasad and  Rudra Pratap Singh},
	  country	= {},
	  institution	= {},
	  abstract     = {In the current work, adaptive extended Kalman filter (AEKF) is presented for solution of ground radar based ballistic missile (BM) tracking problem in re-entry phase with unknown ballistic coefficient. The estimation of trajectory of any BM in re-entry phase is extremely difficult, because of highly non-linear motion of BM. The estimation accuracy of AEKF has been tested for a typical test target tracking problem adopted from literature. Further, the approach of AEKF is compared with extended Kalman filter (EKF). The simulation result indicates the superiority of the AEKF in solving joint parameter and state estimation problems.},
	    journal   = {International Journal of Electrical and Computer Engineering},
	  volume    = {11},
	  number    = {4},
	  year      = {2017},
	  pages     = {475 - 480},
	  ee        = {https://publications.waset.org/pdf/10007418},
	  url   	= {https://publications.waset.org/vol/124},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 124, 2017},
	}