Combining Particle Filtering with Cricket System for Indoor Localization and Tracking Services

We describe the design, implementation and performance evaluation of a wireless sensor network based localization and tracking system, which can provide localization services in many of the pervasive computing applications. Our system uses non-linear Bayesian filtering to obtain accurate tracking results from noisy localization data. This allows high-accuracy tracking of non-linear motion even in the presence of non- Gaussian measurement noise, which is a significant improvement in generality compared to many state-of-the-art sensor network localization systems. Our prototype uses a combination of radio and ultrasound signals to obtain distance estimates necessary for the filtering process, although in principle any distance estimation technique can be used. We also describe improved outlier detection and post-calibration methods for enhancing the quality of the distance estimates obtained compared to earlier systems.