Chinese Journal of Ship Research, Volume. 17, Issue 4, 12(2022)
Ship trajectory tracking based on IMM-SCKF algorithm
Figures & Tables(16)
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Table 1. Setting of experimental initialization parameters
View tableView in ArticleTable 1. Setting of experimental initialization parameters
参数 CVM CSM CTM 改进的CTM ${{\boldsymbol{Q}}}$ $ q=0.25 $ ${a}_{\mathrm{m}\mathrm{a}\mathrm{x} }=0.01\;\mathrm{m}/{\mathrm{s} }^{2}$ $ q=0.01 $ $ q=0.01 $ ${{\boldsymbol{R}}}$ ${\sigma }_{x}={\sigma }_{y}=10\;\mathrm{m}$ ${{{\boldsymbol{P}}} }_{0}$ ${{{\boldsymbol{I}}} }_{4\times 4}$ ${{{\boldsymbol{I}}} }_{6\times 6}$ ${{{\boldsymbol{I}}} }_{4\times 4}$ ${{{\boldsymbol{I}}} }_{4\times 4}$ $ \Delta t $ /$ \mathrm{s} $ $ 10 $
Table 2. ARMSEs of local position data at different time periods in Trajectory 4 using Combined Model 1 and single motion model
View tableView in ArticleTable 2. ARMSEs of local position data at different time periods in Trajectory 4 using Combined Model 1 and single motion model
模型 ARSME/m 0~120 s 120~220 s 220~370 s CVM 7.536 1 27.569 6 7.514 9 CTM 30.211 0 7.464 0 28.728 8 组合模型1 6.780 0 5.509 0 5.333 9
Table 3. ARMSEs of position data at different time periods in Trajectory 5 using Combined Model 1 and 2
View tableView in ArticleTable 3. ARMSEs of position data at different time periods in Trajectory 5 using Combined Model 1 and 2
模型 ARMSE/m 0~250 s 250~400 s 400~600 s 600~770 s 组合模型1 6.1244 5.7552 8.6727 5.7986 组合模型2 5.3135 4.9598 5.1775 5.2239
Table 4. ARMSEs of position data in Trajectory 6 using three combined models with Kalman filtering algorithm
View tableView in ArticleTable 4. ARMSEs of position data in Trajectory 6 using three combined models with Kalman filtering algorithm
模型及搭配算法 ARMSE/m 组合模型1+EKF 15.693 5 组合模型1+SCKF 10.975 5 组合模型2+SCKF 6.709 8 组合模型3+SCKF 6.148 6
Table 5. ARMSEs of position data in the six trajectories using three combined models with SCKF algorithm
View tableView in ArticleTable 5. ARMSEs of position data in the six trajectories using three combined models with SCKF algorithm
模型及搭配算法 ARMSE/m 轨迹1 轨迹2 轨迹3 轨迹4 轨迹5 轨迹6 组合模型1+SCKF 6.131 2 6.277 3 7.880 9 6.140 8 6.829 8 11.525 2 组合模型2+SCKF 6.130 1 5.947 2 7.542 7 5.736 1 5.395 2 7.290 2 组合模型3+SCKF 5.757 0 5.590 9 6.689 4 5.659 2 5.349 9 6.632 9
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Jiaxuan YANG, Baiguo CHEN, Lingqi MA. Ship trajectory tracking based on IMM-SCKF algorithm[J]. Chinese Journal of Ship Research, 2022, 17(4): 12
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Received: Dec. 3, 2021
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Published Online: Mar. 26, 2025
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