Abstract
Deep-learning classifiers can effectively improve the accuracy of fingerprint-based indoor positioning. During fingerprint database construction, all received signal strength indicators from each access point are combined without any distinction. Therefore, the database is created and utilised for deep-learning models. Meanwhile, side information regarding specific conditions may help characterise the data features for the deep-learning classifier and improve the accuracy of indoor positioning. Herein, a side-information-aided preprocessing scheme for deep-learning classifiers is proposed in a dynamic environment, where several groups of different databases are constructed for training multiple classifiers. Therefore, appropriate databases can be employed to effectively improve positioning accuracies. Specifically, two kinds of side information, namely time (morning/afternoon) and direction (forward/backward), are considered when collecting the received signal strength indicator. Simulations and experiments are performed with the deep-learning classifier trained on four different databases. Moreover, these are compared with conventional results from the combined database. The results show that the side-information-aided preprocessing scheme allows better success probability than the conventional method. With two margins, the proposed scheme has 6.55% and 5.8% improved performances for simulations and experiments compared to the conventional scheme. Additionally, the proposed scheme, with time as the side information, obtains a higher success probability when the positioning accuracy requirement is loose with larger margin. With direction as the side information, the proposed scheme shows better performance for high positioning precision requirements. Thus, side information such as time or direction is advantageous for preprocessing data in deep-learning classifiers for fingerprint-based indoor positioning.
Highlights
With the dual development of wireless communications and smart mobile devices, location-based services (LBSs) have developed rapidly and shown broad market prospects [1]
When using the trial database as the input, the success probability is the probability of correct position prediction with an error margin of two reference point (RP)
The 0 margin indicates that the decision obtained at a certain RP position is entirely correct; the 1 margin means that the positioning decision result is within the range of (RP−1, RP+1), and the 2 margin indicates that the positioning decision value is within the range of (RP−2, RP+2)
Summary
With the dual development of wireless communications and smart mobile devices, location-based services (LBSs) have developed rapidly and shown broad market prospects [1]. In [15,16], where RSSI values were collected at different times (morning/afternoon) and directions (forward and reverse), it was shown that building a large-scale database may not always be suitable for deep-learning classifiers in dynamic environments. The side information was used to segment and construct the multiple databases for training classifiers to evaluate the performance of the system This reduces the impact of dynamic RSSI values from the same APs for feature learning and improves the positioning accuracies. We observed that the fingerprints measured at different times and directions were different, so we proposed a preprocessing method to classify the database based on the side information and in combination with the data augmentation method to effectively improve the positioning success rates.
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