Application of Indoor Positioning Based on Kpca and Bp Neural Network Algorithm

Abstract

As the location method of indoor fingerprint position needs to be conducted in a region with signal coverage, the signal intensity's information of all Access Points has to be collected to build a fingerprint database. And not all information in the database will be of positive service to the positional accuracy. On account of increasing data dimensions in the database, rising complexity of algorithm and the climbing number of required experimental samples, a dimension disaster could be arisen. Based on KPCA algorithmic method, in this paper, positioning performance will be improved even under noisy circumstance by preprocessing the position fingerprint data and some data space occupied by positioning system will be saved through decreasing the data dimensions and reduced information of redundancy.