基于自适应加权平均的车速检测研究

Speed Detection Based on the Adaptive Weighted Average Method

随着智能交通的发展,海量的交通状态实时数据成为智能交通的研究对象,利用低损耗获取高实时性的交通流状态成为实际应用研究中的重点.本文引入一种自适应加权平均的车速检测方法,与以往的方法相比,自适应加权平均方法更加简单,并且容易实现;虽然其误差比最小均方差加权平均算法略大,但是这种方法不必以存储海量的交通信息数据为代价,解决了交通信息采集过程中的关键——实时性不高、存储容量大等实际问题.通过MATLAB仿真结果可以看到,此方法大大节省了数据存储空间和数据处理的时间,适合在实际工程应用中广泛推广.

With the development of intelligent transportation systems （ ITS）, the magnanimity real-time traffic data become a major study objectivities, and the key is how to get high quality real-time traffic information with low loss. An adaptive weighted average method for detecting speed is introduced in this paper. Compared with traditional approaches, this method is easier to implement, and the error is slightly larger than the minimum mean square error weighted average. This method does not need large storage space for traffic information, which can resolve the problem of real-time in the course of measuring the traffic parameters. The result of the MATLAB simulation shows that this method saves not only hardware but time. it matches well with the actual demand.