Discrimination Of Boosted Trajectories Among Several Radar Observed Objects
- Paper number
IAC-07-C1.4.07
- Author
Prof. Paolo Teofilatto, University of Rome "La Sapienza", Italy
- Year
2007
- Abstract
In the present paper an algorithm is proposed for the identification of a boosted trajectory on the basis of radar measurements. The orbital dynamics in presence of atmosphere and thrust is analyzed to discriminate boosted trajectories among several radar observed objects. Beside orbit dynamics identification, a strategy for optimal optical and radar sensors deployment is discussed to improve data fusion accuracy. An analytic solution of a simplified dynamic model is applied to evaluate parameters and state variables in boost phase. These analytic estimates are used as guess values to initialize a Maximum Likelihood algorithm. Moreover an Extended Kalman Filter is used to investigate the possibility of a real time detection algorithm. It is shown that the filter may have problematic convergence in the boost phase without sufficiently accurate preliminary information on dynamical model. On the other hand, Extended Kalman Filter is shown to be effective in the ballistic and reentry phase, so a blend of the Maximum Likelihood Estimate and Extended Kalman Filter is proposed. Furthermore the Kalman filter allows to propagate in real time the orbit dynamic model, evaluating at the same time discrepancies between actual state variables measurements and orbit dynamical model propagation. The analysis of covariance variation provides a chance to discriminate between boosted objects and other radar signals. While the trajectory covariance variation is mainly due to measurement errors, the jammed signals covariance increases indefinitely since their random motion is unrelated to process dynamical model. Thus, in a multitarget environments, a filters bank permits to achieve further information on target nature beside radar echoes. The last part of the paper focuses on sensors data fusion issue. Some results are the increase accuracy of achieved data and surveillance sector improvement. Of course these aims are not compatible since accuracy augmentation requires sensors with overlapping field of view. Nevertheless different constraints can be assigned to different situations: launch tracking system require augmentation of accuracy since the trajectory is almost known a priori, surveillance system, instead, requires sensors with larger field of view. A cost function to achieve increased data accuracy, dependant on sensors positions, is proposed and discussed.
- Abstract document
- Manuscript document
IAC-07-C1.4.07.pdf (🔒 authorized access only).
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