【作者】 林岚；

【导师】 邱晓红；

【作者基本信息】 江西师范大学 ， 计算机应用技术， 2005， 硕士

【摘要】 数据融合是对多源信息进行处理的一门综合性学科。目标跟踪是数据融合的一个重要方面。传统的多目标跟踪技术存在快速响应与提高精度之间的矛盾,寻求更好的解决方法一直是专家们不断研究探讨的。 20世纪80年代以来,神经网络技术的再次兴起,为多目标跟踪研究注入了新的活力。本文通过分析神经网络的功能、特点,将其与目标跟踪技术相结合,首先提出一种基于kohonen网络的多目标跟踪算法。此算法将位置测量信息作为网络输入信息,结合卡尔曼滤波等方法对目标实施跟踪。仿真表明,在一定条件下此算法跟踪较准确,但由于网络自身的局限性,对跟踪的实时性有一定影响。针对这一问题,本文引入了模糊kohonen聚类(FKCN)算法,将其与kohonen算法进行对比分析,提出改进算法。仿真结果表明,基于FKCN的改进算法与基于kohonen网络的跟踪算法相比,大大加快了网络的收敛速度,提高了跟踪的实时性。本文还将FKCN算法与“先融合后滤波”的思想相结合,提出了一种多传感器测量信息融合算法。仿真实验证明了此跟踪融合算法具有较好的跟踪效果,为多目标跟踪数据融合研究提供了一条新途径。最后,针对仿真实验中存在的强机动目标跟踪不太精确的问题,对基于BP网络的自适应跟踪算法的进行了探讨,并提出了几点改进方案。更多还原

【Abstract】 Content: Data fusion is a comprehensive subject of studying the processing of multi-source information. Target tracking is an important part of data fusion. However, there is a contradiction between increasing rapid response and improving the tracking precision in traditional multi-target tracking technology. In order to get better solution, many experts have been studying in this area.Since the 1980s, with the revival of neural networks, the multi-target tracking technology has greatly developed. At the beginning of this paper, properties, the structure of the neural network are analyzed, and an algorithm for multi-target tracking based on kohonen neural network is proposed, which integrates the neural network and the multi-target tracking technology. This algorithm uses position information as the input of network, and it is applied to multi-target tracking by means of the kalman filter algorithm and other methods. Simulation results show that the algorithm can realize precise tracking in certain condition. However, because of the limitation of the kohonen network itself, it may not be able to meet the real-time requirement. To solve the problem, the fuzzy kohonen clustering network (FKCN ) is introduced here and compared with kohonen network, accordingly, an improvement tracking algorithm is proposed. Simulation results show that the improvement algorithm based on FKCN can enhance the network convergence rate and satisfy the request of real-time. Combining FKCN algorithm with the idea of" filter after fusion", a multi-sensor fusion algorithm is proposed. Simulation results also show that this algorithm works well in multiple targets condition and it provides a new way of the data fusion for multi-target tracking field. Finally, in view of existing problem in simulation experiments, the self-adaptation tracking algorithm based on BP neural network is studied, and its improvement schemes are proposed.更多还原