Evolutionary strategy for elimination of accumulated errors in positioning system based on particle filter

The particle filter algorithm is a commonly used method for a localization based on magnetic measurement, however, it has a fatal flaw, called the existence of accumulated errors, leading to the failure of localization. According to the mutation step controlled by fitness, an adaptive evolution strategy is proposed in the particle filter algorithm to improve the searching efficiency and the precision, thus enhanceing the variety of re-sampled particles. Then, the optimization of selecting particles is realized based on the particle weight. To increase the positioning accuracy and overcome the effects on accumulated errors, a geomagnetic matching algorithm is periodically called after the target moving some steps. In the geomagnetic matching, the use of pre-matching prior to exactly matching process can reduce the convergence time. The simulation by C++ on an Android smartphone, the test in an indoor environment and further simulation based on real-world measurements show that the algorithm can effectively improve the filter performance and the positioning accuracy.