Precise Tracking of Things via Hybrid 3-D Fingerprint Database and Kernel Method Particle Filter

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Abstract
Precise tracking of things (PToT) using RF signals has posed a serious challenge in indoor environment. However, the need for more precision in tracking people and inventories in indoor environment has been gaining traction. Furthermore, there are demands for location-based services, such as micro-fencing for targeted advertisement. Precision is key for successful launch of the aforementioned applications. PToT relies on two main components, a novel hybrid 3-D fingerprint database and precise navigation (tracking). In this paper, we address the benefit of our novel approach in creating 3-D fingerprint database in concert with kernel method particle filter (KMPF) to achieve precise positioning and tracking. The hybrid 3-D fingerprint digital map is created from Wi-Fi and Bluetooth received signal strength as well as the fusion of optical sensor 3-D coordinate and magnetic sensor rotational attributes, hence much improved accuracy. This is the first integral component for PToT. The KMPF will track and predict the position utilizing the map information created by the high-resolution signature database. The parameter estimation for cooperative and non-cooperative modes of operations is compared with lower bound on the variance using Cramér–Rao lower bound.

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