通过对临近空间高超声速飞行器的受力分析,建立了其周期性跳跃运动的数学模型,仿真分析了其运动特性;提出了一种加速度均匀变化的运动模型(constant differential of acceleration,CDA),并与CV模型、CA模型交互,使用引入强跟踪滤波器的...通过对临近空间高超声速飞行器的受力分析,建立了其周期性跳跃运动的数学模型,仿真分析了其运动特性;提出了一种加速度均匀变化的运动模型(constant differential of acceleration,CDA),并与CV模型、CA模型交互,使用引入强跟踪滤波器的交互式多模型算法对周期性跳跃运动进行跟踪研究。结果表明,该跟踪算法比经典IMM算法有更好的跟踪精度。展开更多
Aim To develop a practical target tracking algorithm for different motion modes. Methods After creation of the new model, it was implemented by computer simulation to prove its performance and compared with the of...Aim To develop a practical target tracking algorithm for different motion modes. Methods After creation of the new model, it was implemented by computer simulation to prove its performance and compared with the often-used current statistical model. Results The simulation results show that the new IMM (interactive multiple model) have low tracking error in both maneuVering segment and non^Inaneuwi segment while the current statistical model bas muCh higher tracking error in non-maneuvering segment. Conclusion In the point of trackintaccuracy, the new IMM method is much better than the current acceleration method. It can develop into a practical target hacking method.展开更多
According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm ...According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm based on fuzzy logic inference （FIMM） is proposed. Maneuvering patterns of the target are represented by model sets, including the constant velocity model （CA）, the Singer mode~, and the nearly constant speed horizontal-turn model （HT） in FIMM technology. The simulation results show that compared to conventional IMM, the reliability and real-time performance of underwater target tracking can be improved by FIMM algorithm.展开更多
A typical Markov network for modeling the interaction among targets can handle the error merge problem,but it suffers from the labeling problem due to the blind competition among collaborative trackers. In this paper,...A typical Markov network for modeling the interaction among targets can handle the error merge problem,but it suffers from the labeling problem due to the blind competition among collaborative trackers. In this paper,we propose a motion constraint Markov network model for multi-target tracking. By augmenting the typical Markov network with an ad hoc Markov chain which carries motion constraint prior,this proposed model can overcome the blind competition and direct the label to the corresponding target even in the case of severe occlusion. In addition,the motion constraint prior is formu-lated as a local potential function and can be easily incorporated in the joint distribution representation of the novel model. Experimental results demonstrate that our model is superior to other methods in solving the error merge and labeling problems simultaneously and efficiently.展开更多
In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Int...In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.展开更多
文摘通过对临近空间高超声速飞行器的受力分析,建立了其周期性跳跃运动的数学模型,仿真分析了其运动特性;提出了一种加速度均匀变化的运动模型(constant differential of acceleration,CDA),并与CV模型、CA模型交互,使用引入强跟踪滤波器的交互式多模型算法对周期性跳跃运动进行跟踪研究。结果表明,该跟踪算法比经典IMM算法有更好的跟踪精度。
文摘Aim To develop a practical target tracking algorithm for different motion modes. Methods After creation of the new model, it was implemented by computer simulation to prove its performance and compared with the often-used current statistical model. Results The simulation results show that the new IMM (interactive multiple model) have low tracking error in both maneuVering segment and non^Inaneuwi segment while the current statistical model bas muCh higher tracking error in non-maneuvering segment. Conclusion In the point of trackintaccuracy, the new IMM method is much better than the current acceleration method. It can develop into a practical target hacking method.
基金Supported by the National Natural Science Foundation of China （No.40067116）, the Research Development Foundation of Dalian Naval Academy （No.K200821）.
文摘According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm based on fuzzy logic inference （FIMM） is proposed. Maneuvering patterns of the target are represented by model sets, including the constant velocity model （CA）, the Singer mode~, and the nearly constant speed horizontal-turn model （HT） in FIMM technology. The simulation results show that compared to conventional IMM, the reliability and real-time performance of underwater target tracking can be improved by FIMM algorithm.
文摘A typical Markov network for modeling the interaction among targets can handle the error merge problem,but it suffers from the labeling problem due to the blind competition among collaborative trackers. In this paper,we propose a motion constraint Markov network model for multi-target tracking. By augmenting the typical Markov network with an ad hoc Markov chain which carries motion constraint prior,this proposed model can overcome the blind competition and direct the label to the corresponding target even in the case of severe occlusion. In addition,the motion constraint prior is formu-lated as a local potential function and can be easily incorporated in the joint distribution representation of the novel model. Experimental results demonstrate that our model is superior to other methods in solving the error merge and labeling problems simultaneously and efficiently.
基金Supported by the National Natural Science Foundation of China (No. 60772154)the President Foundation of Graduate University of Chinese Academy of Sciences (No. 085102GN00)
文摘In multi-target tracking,Multiple Hypothesis Tracking (MHT) can effectively solve the data association problem. However,traditional MHT can not make full use of motion information. In this work,we combine MHT with Interactive Multiple Model (IMM) estimator and feature fusion. New algorithm greatly improves the tracking performance due to the fact that IMM estimator provides better estimation and feature information enhances the accuracy of data association. The new algorithm is tested by tracking tropical fish in fish container. Experimental result shows that this algorithm can significantly reduce tracking lost rate and restrain the noises with higher computational effectiveness when compares with traditional MHT.
