基于粗精二次估计的RFID标签数目估算方法

为了解决航空物联网信息采集领域RFID标签估算方法存在的估算精度和运算量之间的矛盾，以及标签读取过程随机性所导致的估算方法性能不稳定的问题，结合粗估计的快速、精估计的准确和二次估计算法性能的稳定性，提出一种基于粗精二次估计的RFID标签数目估算方法。首先，对帧时隙ALOHA算法标签读取过程进行建模，分析得出碰撞时隙中的平均标签数目和碰撞时隙所占比例之间的数学模型；然后，基于上述数学模型进行标签数目粗估计，评估粗估计值是否需要进行二次精估计。在二次精估计中，将粗估计值作为先验知识，采用基于先验知识的最大后验概率（MAP）估计算法提高估算准确度，相比原始后验概率估计算法的搜索范围可减少90%。仿真实验表明，基于粗精估计的RFID标签数目估算平均误差为3.8%，估算方法性能稳定性显著提高，运算量大幅下降，可有效地应用于航空物联网信息采集过程。

Estimation method for RFID tags based on rough and fine double estimation

To solve the contradiction between the estimation accuracy and the calculation amount of the RFID tag estimation method, and the instability of the estimation method performance caused by the randomness of the tag reading process in the field of aviation logistics networking information gathering. Based on the idea of complementary advantages, a method for estimating the number of RFID tags based on rough and fine estimation was proposed. By modeling and analyzing the tag reading process of framed ALOHA algorithm, the mathematical model between the average number of tags in the collision slot and the proportion of the collision slot was established. Rough number estimation based on the model was made, and then, according to the value of rough estimation, the reliability of rough estimation was evaluated. The Maximum A Posteriori (MAP) estimation algorithm based on the value of rough estimation as priori knowledge was used to improve the estimation accuracy. Compared to the original maximum posteriori probability estimation algorithm, the search range can be reduced up to 90%. The simulation results show that, the average error of the RFID tag number estimation based on rough and fine estimation is 3.8%, the stability of the estimation method is significantly improved, and the computational complexity is greatly reduced. The proposed algorithm can be effectively applied to the information collection process aviation logistics networking.