Fig. 1. Schematic diagram of Risley-prism system

Fig. 2. Principle of Risley-prism target tracking method based on virtual system

Fig. 3. Principle of estimating prism rotation angle based on PSO algorithm

Fig. 4. Flow chart of estimating prism rotation angle based on PSO algorithm

Fig. 5. Principle of estimating prism rotation angle based on RPSO algorithm

Fig. 6. Variation of the estimated and real errors in the process of static target pointing. (a) Estimated error; (b) real error

Fig. 7. Convergence characteristics of the pointing process of point A. (a) Variation of camera field-of-view (FOV) center; (b) variation of prism rotation angle

Fig. 8. Convergence characteristics of prism rotation angle estimation methods based on PSO and RPSO for tracking dynamic target.

Fig. 9. Influence of particle population size on the performance of target tracking method based on virtual system. (a) Influence on convergence characteristics of estimated error; (b) influence on convergence characteristics of real error

Fig. 10. Convergence characteristics of the prism angle estimation method based on RPSO algorithm when tracking dynamic targets continuously. (a) Convergence characteristics of estimated error; (b) convergence characteristics of real error

Fig. 11. Results of the prism rotation angle estimation method based on the RPSO algorithm for tracking the dynamic target. (a) Process of camera FOV center approaching the dynamic target; (b) estimation result of prism rotation angle

Fig. 12. Experimental prototype (1: prism; 2: camera; 3: servo motor 1; 4: servo motor 2; 5: computer)

Fig. 13. Static target pointing accuracy. (a) Error distribution range; (b) total pixel error of 60 pointing tests

Fig. 14. Total pixel error between the camera FOV center and the target center in the dynamic tracking process