无源RFID系统中多目标识别的估计算法

研究被动射频识别（RFID）系统中多目标识别环境下被识别目标数量的估计算法和最大吞吐率的获取方法。通过对时隙ALOHA方法的分析，建立了读写器和标签通信的二项式分布模型，得到了获取最大吞吐率的条件：在已知被识别目标数量的前提下，使时隙数与目标数量相等。提出一种被识别目标数量的估计算法——二次式分布估计（BDE）算法。BDE算法估计误差抖动小，平均误差为2.1%。仿真结果表明BED算法在识别目标数量少时，能将识别时间缩短至50%，当识别目标增加时，识别时间呈线性增长；系统吞吐率接近理论最大值，达到34%；空白时隙和碰撞时隙占时控制在16%附近。同时，识别时间成分分析指出：优化读写器命令、提高读写器传输数据率能够进一步缩短识别时间。

Estimation algorithm of identifying multi-tag in passive RFID system

This paper investigated how to shorten identifying time of multiple objects and gain maximum throughput between reader and tags in passive RFID system. We presented a combination model of the communication mechanism between reader and tags, and used this model to deduce an algorithm to estimate tag number: Binomial Distributing Estimation(BDE) algorithm. With the help of BDE algorithm, we then selected optimal slot number in slotted ALOHA protocol. Simulation results are as the following: First, identifying time of BDE algorithm is satisfactorily shortened when identifying tag number is one, two and three. Identifying time increases linearly with tag number. Second, throughput is about 34%, which is close to academic maximum. Third, analysis of identifying time ingredients indicates that optimizing reader commands and speeding up reader data rate can further shorten identifying time.