Abstract
Indoor positioning technologies are of great use in GPS-denied areas. They can be partitioned into two types of systems—infrastructure-free based and infrastructure-dependent based. WiFi based indoor positioning system is somewhere between the infrastructure-free and infrastructure-dependent systems. The reason is that in WiFi based systems, Access Points (APs) as pre-installed infrastructures are necessary. However, the APs do not need to be specially installed, because WiFi APs are already widely deployed in many indoor areas, for example, offices, malls and airports. This feature makes WiFi based indoor positioning suitable for many practical applications. In this paper, a seq2seq model based, deep learning method is proposed for WiFi based fingerprinting. The model can learn from different length of training sequences, and thus can exploit the context information for positioning. The context information denotes the information contained in the sequence, which can help finding the correspondences between RSS fingerprints and the coordinate positions. A simple example piece of context information is human walking routine (such as no sharp turns). The proposed method shows an improvement with an open source dataset, when compared against deep learning based counterpart methods.
Highlights
Indoor positioning denotes the problem of positioning during Global Positioning System (GPS)outages
The proposed method is tested adopting an open sourced dataset, the results show that the proposed method can improve the positioning accuracy compared with its deep learning based counterpart methods
We can see that compared with PLGD method, Stacked Denoising Auto-encoder (SDA) method and the f-Recurrent Neural Network (RNN) method, our method has increased the accuracy by 1.32 m, 1.27 m and 0.79 m in Building One, and 0.60 m, 0.51 m and 0.25 m in Building Two respectively
Summary
Indoor positioning denotes the problem of positioning during Global Positioning System (GPS). In References [13,14], a Recurrent Neural Network (RNN) is adopted for the training model between the RSS fingerprints and the coordinate positions These methods have the same overall structure but differs in detailed implementation. The seq2seq model has been previously studied to solve NLP translation tasks, so it shows potential to tackle WiFi fingerprinting with the aid of context information as shown in the following examples. Example One: In WiFi based positioning, different coordinate positions can correspond to similar RSS fingerprints due to noises in certain cases. Example Two: Some fierce change of adjacent RSS fingerprints (or special changes in RSS sequences) normally corresponds to certain coordinate positions This can be considered as certain patterns hided in the context information, which can be learned from the sequences. The remaining of the paper is organized as follows—Section 2 reviews related works, Section 3 describes the proposed method, Section 4 shows the experimentation and Section 5 presents the conclusion and future work
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