A fingerprint-based localization algorithm based on LSTM and data expansion method for sparse samples

The accuracy of WiFi fingerprint-based localization is related to the number of reference points, generally, to obtain better positioning accuracy, enough samples must be collected, which will inevitably lead to a huge sampling workload. Thus, it will be of great significance to design an algorithm using sparse samples to achieve positioning accuracy like that of dense samples. This paper proposes a WiFi fingerprint-based localization algorithm using Long Short-Term Memory Network (LSTM) with explainable feature and a sparse sample expansion algorithm (PGSE) based on Principal component analysis and Gaussian process regression for sparse samples. Specifically, in the case of limited number of collected reference points, principal component analysis is used to select the access point, and Gaussian process regression is used to model the reference point coordinates and the corresponding received signal strength values in the training sample set, to expand the signal data and construct a new fingerprint database. The effectiveness of the PGSE algorithm is verified by using the public dataset ’UJIIndoorLoc’. At the same time, the applicability of PGSE expansion algorithm to data with temporal information is verified in the fingerprint-based localization method. In addition, this paper also proposes a WiFi-RSSI indoor localization method based on Long Short-Term Memory Network. Lots of experiments are conducted in the actual scenes and the results are compared with several existing methods. The results indicate that the proposed method improves the precision of indoor localization on an average level compared to state-of-the-art methods.