Trajectory estimation and impact point prediction of a ballistic object from a single fixed passive sensor

Kaipei Yang; Yaakov Bar-Shalom; Peter Willett; R. Ben-Dov; B. Milgrom

doi:10.1117/12.2305114

27 April 2018 Trajectory estimation and impact point prediction of a ballistic object from a single fixed passive sensor

Kaipei Yang, Yaakov Bar-Shalom, Peter Willett, R. Ben-Dov, B. Milgrom

Author Affiliations +

Proceedings Volume 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII; 1064609 (2018) https://doi.org/10.1117/12.2305114
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

For a thrusting/ballistic target, works have shown that a single fixed sensor with 2-D angle-only measurements (azimuth and elevation angles) is able to estimate the target’s 3-D trajectory. In previous works, the measure- ments have been considered as starting either from the launch point or with a delayed acquisition. In the latter case, the target is in flight and thrusting. The present work solves the estimation problem of a target with delayed acquisition after burn-out time (BoT), i.e. in the ballistic stage. This is done with a 7-D parameter vector (velocity vector azimuth angle and elevation angle, drag coefficient, 3-D acquisition position and target speed at the acquisition time) assuming noiseless motion. The Fisher Information Matrix (FIM) is evaluated to prove the observability numerically. The Maximum Likelihood (ML) estimator is used for the motion parameter estimation at acquisition time. The impact point prediction (IPP) is then carried out with the ML estimate. Simulation results from the scenarios considered illustrate that the MLE is efficient.

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Citation Download Citation

Kaipei Yang, Yaakov Bar-Shalom, Peter Willett, R. Ben-Dov, and B. Milgrom "Trajectory estimation and impact point prediction of a ballistic object from a single fixed passive sensor", Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 1064609 (27 April 2018); https://doi.org/10.1117/12.2305114

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