Tracking a Mobile Beacon: A Purely Probabilistic Approach

Abstract

We construct a practical and real-time probabilistic framework for fine target tracking. The practicality comes from the application of the forward algorithm and the small parameter set used to build the hidden Markov model (HMM). A Bluetooth Low-Energy (BLE) beacon navigating in the environment publishes BLE packets which are captured by the stationary sensors. Fingerprints are formed by collecting received signal strength indicators (RSSI) of these packets, which are then processed into a high resolution emission matrix using a histogram combination technique. We convert the map of the area into a grid structure, the resolution of which is controlled by the grid cell size. The transition matrices are built by Gaussian blur masks parametrized by the size and diffusion extent. As the transition matrix is highly sparse, we make the exact inference tractable by adopting a sparse matrix representation and by intelligently controlling the mask size, diffusion factor and grid cell size. Filtering can then be directly performed by the forward algorithm given a series of real RSSI measurements along real trajectories. We measure the performance of the system by comparing the most likely positions at each step with the ground truth positions. We achieve promising results and evaluate the approach also by the runtime and memory usage.