A method used to detect anomaly and estimate the state of vehicle in driving was proposed.The kinematics model of the vehicle was constructed and nonholonomic constraint conditions were added,which refer to that once the vehicle encounters the faults that could not be controlled,the constraint conditions are violated.Estimation equations of the velocity errors of the vehicle were given out to estimate the velocity errors of side and forward.So the stability of the whole vehicle could be judged by the velocity errors of the vehicle.Conclusions were validated through the vehicle experiment.This method is based on GPS/INS integrated navigation system,and can provide foundation for fault detections in unmanned autonomous vehicles.
Mean shift算法在实际应用中,若目标部分被遮挡或有背景因素干扰,则跟踪精度会降低.鉴于此,将背景和目标本身分别进行加权,通过背景加权改善对目标特征的描述,对目标的不同部位赋予大小不等的权值,有效地提高了Bhattacharyya系数值.从原算法对目标模型的描述出发,将其加入到Mean shift算法的数学模型表达式中.通过算法改进前后的实验结果以及跟踪偏差和迭代次数的比较发现,跟踪效果得到了明显改善.
针对延迟容忍网络中存在的数据传输时延较高、摆渡节点间协作性不高,以及如何最优分配摆渡节点等问题,提出一种基于位置信息的摆渡节点延迟容忍网络路由算法(ferries routing mechanism based on location information for delay tolerant network,FRLI)。基于节点位置信息,定义基于位置信息的数据传输机制,通过划分摆渡节点隶属的区域,根据摆渡节点在网络中的初始分布状况,合理分配网络中摆渡节点分布,通过交换彼此区域内缓存的网络节点信息,获取有利于当前区域内数据传输的有效信息,提高区域内数据传输效率;基于节点区域信息,确认目的节点是否属于当前区域后,直接将数据投递至网关节点,渐次转发至目的节点所在区域,有效提高数据传输效率。仿真结果表明,与当前MURA算法、SIRA算法相比,该算法具有更低的数据传输时延与更高的传输效率。
