Hybrid Inference for Sensor Network Localization Using a Mobile Robot

In this paper, we consider a hybrid solution to the sensor network position inference problem, which combines a real-time filtering system with information from a more expensive, global inference procedure to improve accuracy and prevent divergence. Many online solutions for this problem make use of simplifying assumptions, such as Gaussian noise models and linear system behaviour and also adopt a filtering strategy which may not use available information optimally. These assumptions allow near real-time inference, while also limiting accuracy and introducing the potential for ill-conditioning and divergence. We consider augmenting a particular realtime estimation method, the extended Kalman filter (EKF), with a more complex, but more highly accurate, inference technique based on Markov Chain Monte Carlo (MCMC) methodology. Conventional MCMC techniques applied to this problem can entail significant and time consuming computation to achieve convergence. To address this, we propose ...

• Type: Conference
• Year: 2007
• Where: AAAI
• Authors: Dimitri Marinakis, David Meger, Ioannis M. Rekleitis, Gregory Dudek
• Keywords: AAAI 2007, Inference, Intelligent Agents, Position Inference Problem, Sensor Network