Parallel Area Navigation Enhanced Information Extraction Algorithm Based on Massive Historical GNSS Data and MapReduce

Abstract

Navigation enhanced information (NEI) is very important to improve the positioning and navigation performance of GNSS receivers, and the current available NEI mainly come from static station observation data, which don't has enough credibility and area attribute. In this paper, an parallel area navigation enhanced information (ANEI) extraction algorithm based on massive historical GNSS data and MapReduce is proposed, to get the ANEI which is much more effective than traditional NEI, and the high efficiency of the algorithm is guaranteed by using the Hadoop parallel programming model MapReduce. The principle of the algorithm is as follows: a large number of historical GNSS Intermediate Frequency data is divided into blocks to be acquired and tracked parallel to get massive multi satellite system pseudorange and navigation message (MPD), then, the massive MPD are weighted and fused parallel by using variance component estimation to get the NEI which will be corresponded to the corresponding location coordinate, completing the parallel extraction of ANEI. Experimental results show that the positioning error of GNSS receivers is reduced about 18.24% by using ANEI instead of traditional NEI, and the execution time of the algorithm is reduced about 46.72% by using MapReduce, so the algorithm proposed in this paper is more reliable and effective.