THE present paper is devoted to the problem of identification of the characteristics of a missile and of its trajectory on the basis of few radar measurements that are affected by rather large errors. If radar measurements are accurate, it is proved that four observations are enough to give a precise estimate of the unknown parameters of the boost phase. For radar measurements corrupted by noise, the boost parameter estimates provided by the method proposed are used as first guess for the solution of a Maximum Likelihood Estimator (MLE), and the combined use of the parameters estimate and MLE allows the detection of missile characteristics and trajectory even with rather large error measurements. For some applications it is important to have on time tracking of the observed object. Then an Extended Kalman Filter (EKF) is applied to the present problem, and the performances of the method are found to be: 1) the EKF seems unable to identify all the missile parameters , and the state vector on the basis of few observations. The convergence of the algorithm is possible if not large variations of the nominal missile parameters are considered, as it is also shown in Ref.1. 2) The EKF is successful in the determination of the state vector that determines the trajectory in the ballistic phase. A combined use of the two methods (Parameter Estimate/MLE in the boost phase and EKF in the subsequent phases) seems to be effective. Of course it is important to know the switching time from one method to the other, and, in fact, the algorithm proposed here is able to detect the missile burn out time.

Algorithm for Missile Detection from Radar Data

PIERGENTILI, FABRIZIO;
2007

Abstract

THE present paper is devoted to the problem of identification of the characteristics of a missile and of its trajectory on the basis of few radar measurements that are affected by rather large errors. If radar measurements are accurate, it is proved that four observations are enough to give a precise estimate of the unknown parameters of the boost phase. For radar measurements corrupted by noise, the boost parameter estimates provided by the method proposed are used as first guess for the solution of a Maximum Likelihood Estimator (MLE), and the combined use of the parameters estimate and MLE allows the detection of missile characteristics and trajectory even with rather large error measurements. For some applications it is important to have on time tracking of the observed object. Then an Extended Kalman Filter (EKF) is applied to the present problem, and the performances of the method are found to be: 1) the EKF seems unable to identify all the missile parameters , and the state vector on the basis of few observations. The convergence of the algorithm is possible if not large variations of the nominal missile parameters are considered, as it is also shown in Ref.1. 2) The EKF is successful in the determination of the state vector that determines the trajectory in the ballistic phase. A combined use of the two methods (Parameter Estimate/MLE in the boost phase and EKF in the subsequent phases) seems to be effective. Of course it is important to know the switching time from one method to the other, and, in fact, the algorithm proposed here is able to detect the missile burn out time.
F. Piergentili; P. Teofilatto
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/39749
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