A compression and reconstruction algorithm of indoor WLAN positioning Radiomap data based on neural network

This paper aims at the problem of large amounts of realtime data transmission in indoor WLAN positioning. We propose a method that we use the BP network to compress at the sending end, and then save the weight. The method reconstructs data in the terminal, which greatly reduces the amount of data in real-time transmission to ensure the fast and effective of real-time positioning. The used simulation environment is in the 27 AP in the floor for data acquisition and real-time simulation application. The applied algorithm is a universal KNN algorithm. The core data compression method is the improved BP neural network. Finally, focusing on the data in the input end of BP network, the paper makes the real-time Radiomap be normalized and presets the simulation parameters. The simulation results show that in the case of choosing appropriate parameters to simulate, compared to the RAR compression, it can compress the whole Radiomap into 1/2 or even 1/3 of data size, it can maintain high stability in a certain degree of compression, and it can guarantee that the reconstructed and compressed data have lossy compression within the allowable range of positioning accuracy.