M2M Encountering: Collaborative Localization via Instant Inter-Particle Filter Data Fusion

Much research has addressed indoor localization by integrating portable/wearable sensing and communication technologies. While GPS has dominated the outdoor environments, indoor localization schemes have to consider different observations, such as radio-frequency signals, vision or motions data, and apply data fusion to combine various sensor inputs. In this paper, we observe that when two devices meet up, which we call machine-to-machine (M2M) encountering, they can collaboratively calibrate each other's potential locations via M2M communications. We apply this technique to particle filter (PF), a common fusion technique, and show how to take the M2M encountering opportunities, which may happen frequently in crowded areas, to allow user devices to collaboratively calibrate their locations. Hence, the PF technique, which normally fuses observations from individual devices, is extended to an inter-PF, cross-device domain. We validate our inter-PF solution by simulations as well as a prototype system with smartphones and ZigBee infrastructure deployed in an office building. It is verified that the inter-PF helps converge the positioning results more rapidly, and improves location quality. Also, the proposed encountering mechanism has potential to be applied to other localization algorithms to improve their accuracy.