Reentry vehicle tracking in a cluttered environment

Abstract

Recently, reentry vehicle (RV) tracking has become an important issue because of its high speed and high-elevation angle. The extended Kalman filter (EKF) with input estimation (IE) was developed for an RV tracking in a clear environment. However, radar tracking suffers from clutters in the form of unwanted, unavoidable and unpredictable signal echoes from sea, land and weather. The Hough transform is a well-known technique for identifying a straight line in noisy environments. This technique can be utilised to design a clutter elimination algorithm for clutters censoring. This study presents a novel tracking algorithm by integrating the EKF, IE and clutter elimination algorithm to track the RVs in cluttered environments. To ensure an RV to be tracked continuously, the estimation errors between the predicted and the real trajectories should be within a range cell. Simulation results reveal that the proposed algorithm can provide an acceptable accuracy to maintain the track. Although clutters cannot be totally censored, the effects of resting clutters can be reduced to an acceptable range. The proposed algorithm may thus help radars in achieving continuous tracking. In conclusion, this algorithm deserves further study and application.

Keywords:

• input estimation
• Hough transform
• tracking algorithm
• reentry vehicle
• trajectory estimation
• tracking
• state estimation
• parameter estimation

Acknowledgement

The authors would like to thank the National Science Council of the Republic of China for financially supporting this research under Contract No. NSC95-2221-E-234-004.