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Description: 机动目标的跟踪问题一直是人们研究的重点，实现机动目标精确跟踪，首要解决的问题就是使所建立的目标运动模型与实际的目标运动模型匹配。目前常用的有多模型(MM)，交互式多模型(IMM)，切换模型等。多模型方法就是对一组具有不同机动模型分别进行Kalman滤波，最终的参数估计是各滤波器估计值的加权和；在多模型基础上，Shalom提出了交互式多模型方法，这一方法对无序目标的机动检测，显示了更好的鲁棒性和跟踪的稳定性；切换模型则是分别建立机动和非机动运动模型，利用机动检测实现在这两个模型之间的切换。一般来说，交互式多模型的跟踪性能较好。-mobile target tracking problem has been a focus of research and achieve precise maneuvering target tracking, the most pressing issue is to enable the establishment of the target model and the actual movement of the target model matching. At present, more commonly used model (MM), interactive multi-model (IMM), switching model. Multi-model approach is a group of different mobile models were Kalman filtering, The final parameter is the estimated value of the filter is estimated weighted; in the multi-model basis, Shalom made the interactive multi-model approach that the objectives of the motor disorder detection, better show the robustness and stability of the track; switching model is set up motorized and non-motorized sports model, the use of mobile testing to achieve in this model between
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Size: 207760 |
Author: zhangfei |
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Description: 机动目标的跟踪问题一直是人们研究的重点，实现机动目标精确跟踪，首要解决的问题就是使所建立的目标运动模型与实际的目标运动模型匹配。目前常用的有多模型(MM)，交互式多模型(IMM)，切换模型等。多模型方法就是对一组具有不同机动模型分别进行Kalman滤波，最终的参数估计是各滤波器估计值的加权和；在多模型基础上，Shalom提出了交互式多模型方法，这一方法对无序目标的机动检测，显示了更好的鲁棒性和跟踪的稳定性；切换模型则是分别建立机动和非机动运动模型，利用机动检测实现在这两个模型之间的切换。一般来说，交互式多模型的跟踪性能较好。-mobile target tracking problem has been a focus of research and achieve precise maneuvering target tracking, the most pressing issue is to enable the establishment of the target model and the actual movement of the target model matching. At present, more commonly used model (MM), interactive multi-model (IMM), switching model. Multi-model approach is a group of different mobile models were Kalman filtering, The final parameter is the estimated value of the filter is estimated weighted; in the multi-model basis, Shalom made the interactive multi-model approach that the objectives of the motor disorder detection, better show the robustness and stability of the track; switching model is set up motorized and non-motorized sports model, the use of mobile testing to achieve in this model between
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Size: 207872 |
Author: zhangyun |
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Description: Yaakov Bar-Shalom, X.-Rong Li,Thiagalingam Kirubarajan - Estimation with Applications to Tracking and Navigation - DynaEst toolbox -Yaakov Bar-Shalom, X.-Rong Li,Thiagalingam Kirubarajan- Estimation with Applications to Tracking and Navigation- DynaEst toolbox
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Size: 641024 |
Author: Dang Nhat Anh |
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Description: 这是数模比赛里面的多雷达多目标数据融合代码，希望对大家有用-This is the Multi-radar data fusion multi-target code of Mathematical Model Competition,Hope to be useful~
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Size: 24576 |
Author: rock |
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Description: 信息融合 使用两个传感器进行航迹融合 Bar-Shalom-Campo融合算法-ｆｕｓｉｏｎ Bar-Shalom-Campo
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Size: 1024 |
Author: caoxiang |
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Description: Tutorial Workshop - April 2004
Multitarget Tracking and Multisensor Fusion
Yaakov Bar-Shalom, Distinguished IEEE AESS
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Size: 1215488 |
Author: jamel |
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Description: Bar-Shalom-Campo融合算法- Bar-Shalom-Campo data fusion algorithm
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Size: 2048 |
Author: 赵东 |
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