摘要：Limited by the sampling capacity of the mobile
devices, many real-time indoor location systems have such problems as low
accuracy, large variance, and non-smooth movement of the estimated position. A
new positioning algorithm and a new processing method for sampled data are
proposed. Firstly, a positioning algorithm is designed based on the
cluster-based nearest neighbour or probability. Secondly, a weighted average
method with sliding window is used to process the sampled data as to overcome
the mobile devices’ weak capability of signal sampling. Experimental results
show that, for the general mobile devices, the accuracy of indoor position
estimation increases from 56.5% to 76.6% for a 2-meter precision, and from
77.4% to 90.9% for a 3-meter precision. Therefore, the proposed methods can
significantly and stably improve the positioning accuracy.
