Self-Supervised Deep State Space Model for Enhanced Indoor Tracking

Accurate indoor tracking is a critical component of modern location-based services, fundamentally transforming the way we interact with indoor environments. Traditional state space model (SSM) based tracking often struggles in complex environments due to its reliance on fixed and oversimplified transition and observation functions. In this paper, we propose a novel deep state space model (DSSM) approach for indoor tracking that overcomes these limitations by leveraging trainable neural networks (NNs) in place of fixed transition and observation functions. The proposed DSSM retains the structured representation of SSMs while improving the ability to effectively capture the complex dynamics of both target movements and measurement errors. The proposed model incorporates physics constraints to enable self-supervised learning, eliminating the need for labeled data during training. We evaluate our framework using real-world time of flight (ToF) measurements, demonstrating its superior tracking accuracy compared to conventional methods.