摘要
在智能交通系统应用中,常常需要利用多种传感器对不同的交通目标进行数据采集,经常遇到时间不同步、数据率不一致的问题。采用BP神经网络算法,提出了一种基于多传感器多目标数据的时间对准方法,较好地解决了多目标跟踪的基础问题,提高了数据融合效率,并通过仿真实验及与传统时间对准方法的比较给出了该方法的优点。
In the application of Intelligent Transportation System（ITS）,multiple sensors are often used to collect data from different traffic targets.Problems exist because times are not synchronized and data rates are not inconsistent.This paper uses BP neural network algorithm to present a new method of data time registration,which not only solves the basic problem in multi-sensor multi-target tracking but also improves the efficiency of data fusion.Simulation and comparisons with traditional methods have shown advantages of this method.
出处
《交通信息与安全》
2011年第1期121-123,共3页
Journal of Transport Information and Safety
基金
交通运输部科技项目(批准号:2008-319-814-060)资助
关键词
BP神经网络
多传感器
时间对准
仿真
BP neural network
multi-sensor
time alignment
simulation
