Abstract: Fingerprint localization is a simple and efficient wireless localization technology. It is free from interference caused by multipath effect and reflection of wireless signals, and achieves high accuracy. However, fingerprint localization technology requires a large offline fingerprint database for better accuracy. With the expansion of fingerprint database scale, traditional fingerprint localization algorithms have been difficult to meet the real-time requirements of large data applications. Therefore, combining fingerprint localization algorithm and in-memory computing engine Spark, a fingerprint localization data processing method based on Spark is designed and implemented. In the Map phase, the K neighbors of query point are found respectively. In the Reduce stage, the K neighbors of each partition are reduced to the global K nearest neighbors. Finally, the result coordinates are found by weighted mean. Cluster experiments show that the Spark based fingerprint localization data processing method has a good speedup in certain parallelism, and has the capability of real-time localization processing in large-scale fingerprint database.

Key words: wireless localization technology, fingerprint localization, Spark computing engine, weighted KNN, distributed computation

摘要： 指纹定位技术是一种简单高效的无线定位技术，它不受无线信号多径效应和反射造成的干扰，具有较好的定位精度。然而指纹定位技术需要建立庞大的离线指纹数据库，随着指纹数据库规模的扩大，传统的指纹定位算法已经难以满足大数据应用中实时性的需求。结合指纹定位算法的特点和Spark计算引擎基于内存计算的优势，设计并实现了基于Spark的指纹定位数据处理方法。在Map阶段分别找到查询点在每个分区内的[K]近邻，在Reduce阶段规约各分区[K]近邻获得全局[K]近邻，最后通过加权求值获得最终的定位坐标。集群实验表明，基于Spark的指纹定位数据处理方法在一定并行度下有较好的加速比，在大规模指纹数据库下有实时定位处理的能力。

关键词: 无线定位技术, 指纹定位, Spark计算引擎, 加权KNN, 分布式计算