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摘要
An orientation state estimator is proposed for smart weapons applications using feedback from wide field-of-view thermal sensors and magnetometers. The state estimation system possesses several key advantages over current designs in that it maintains low cost, lacks moving parts, and is free from reliance on GPS or other state feedback. In order to enhance robustness against disturbances that reduce thermopile performance, the filter incorporates heuristics feedback, leveraging preflight predictions of the projectile flight path. Realistic simulations demonstrate estimation performance during conditions in which terrain and weather phenomena distort or obscure the horizon. The article begins with a discussion of sensor modeling techniques and descriptions of how weather and terrain effects are captured. Then, an Extended Kalman filter is developed to estimate Euler angles and projectile spin rate. Example results, incorporating realistic sensor errors and environmental disturbances, demonstrate estimator performance. Monte Carlo results verify filter robustness and effective use of heuristics feedback.
- 出版日期2012
