Large-Scale Deep Learning Framework on FPGA for Fingerprint-Based Indoor Localization

Abstract

Localization Based Service (LBS) has become as one of the most important applications in modern daily life. Positioning technologies for outdoor environments are relatively mature because of the wide coverage of satellite navigating systems such as the Global Positioning System (GPS). In contrast, indoor localization remains a great challenge due to the fluctuated radio propagation environment. In addition to the high requirement of accuracy, an indoor localization system should also be low cost, low power consumption, and ubiquitous availability in mobile devices. To this end, fingerprint-based indoor localization schemes have served as an effective methodology to satisfy those requirements and have attracted more and more research attentions. In this paper, we present a scalable Deep Neural Network (DNN) architecture with Denoising Auto-encoder for Fingerprint-based Indoor Localization (called 'SDNNLoc') based-on FPGA implementation. First, a scalable stacked denoising auto-encoder is introduced to extract features from the fingerprint database for robustness and accuracy. Then, a generic parameterized DNN accelerator generating & optimization framework is proposed for FPGA implementation. In addition, we also demonstrate a WiFi-based fingerprinting indoor localization system for a crowdsensed university campus scenario. The experimental results show that the proposed DNN framework and its FPGA implementation are feasible for efficient and accurate indoor localization with good performance and high scalability.