The received signal strength in the traditional fingerprint positioning algorithm fluctuates greatly in the com-plex indoors environment,which generates unreliable fingerprint information and results in insufficient positioning accuracy.We propose an ultra-wideband(UWB)fingerprint positioning method based on deep belief network(DBN)combined with extreme learning machine(ELM)using the range value as fingerprint information.Firstly,the multiple stacked restricted Boltzmann machines are used at the bottom of DBN to do unsupervised learning on the input data to extract deep features,and the ELM is used at the top layer to do supervised learning on the input data and location labels.In the offline fingerprint databse stage,a UWB fingerprint database expansion method based on Gaussian process regression is proposed to optimize the fingerprint acquisition process and reduce the cost of manual surveying.The experimental results show that the algo-rithm can achieve centimeter-level positioning accuracy in both line of sight(LOS)and non line of sight(NLOS)environ-ments.
ultra-wideband positioningdeep belief networkextreme learning machineGaussian process regression
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