基于标签分组的DFSA抽样训练规则防冲突算法

DFSA sampling training rules anti-collision algorithm based on tags grouped

主要对标签防冲突的问题进行研究,阐述了已有的分组算法原理,并分析其仍存在的可改进的地方,将标签分布特点与数据结构原型进行有机结合,提出相应的改进方案。引入了抽样和训练规则的概念,经过对分组过程的研究,找出帧长调整的规律性变化,提出了基于标签分组的DFSA抽样训练规则防冲突算法。通过实验仿真,充分体现了该方法在保证现有识别率的情况下,能够有效减少系统功耗,降低识别过程中的计算复杂度,缩短识别时间。

This paper mainly studied the problem of tag collision. Firstly, it analyzed and associated the existing grouped al- gorithm principle with its available developing problems with the research of the relationship of the characteristic of label distri- bution and related data structure in order to put forward the corresponding improvement scheme. Through the study of the process of grouping, it proposed the DFSA sampling training rules anti-collision algorithm based on tags grouped. At last, it verified the available of this method by the simulation results. The experiment shows that this algorithm not only does not affect the success rate of recognition, but also can improve the system recognition time, reduce the system computational complexity and system power consumption.