Precise Tracking of Things via Hybrid 3-D Fingerprint Database and Kernel Method Particle Filter
Precise Tracking of Things (PToT) using RF signals has posed a serious challenge in an indoor environment. The precision localization information is an enabler for better coordinated-tasks and is essential for a successful launch of many emerging applications. PToT relies on two principal components, a novel navigation (tracking) algorithm, and a hybrid 3D fingerprint database.
In this dissertation, we begin by using the two widely known ranging techniques, Time Of Arrival (TOA) associated with Ultra-wideband (UWB) and Received Signal Strength (RSS) with WiFi signals. First, we use the theoretical models derived from empirical measurement of TOA and RSS to analyze the performance of hybrid (WiFi & UWB) cooperative localization accuracy in a multi-robot operation in a typical office environment.
To measure the performance of this hybrid localization, we derive a mathematical formulation for the Cramer-Rao-Lower-Bound (CRLB). The hybrid method shows more accuracy over WiFi-only approach. In achieving more precision, we extend our work. Second, we introduce a novel approach, a Kernel Method Particle Filter (KMPF) for tracking and predicting the position by accessing the information created by hybrid 3D fingerprint database.
We derive the mathematical and statistical framework for the Particle Filter based on the statistical assumptions about the behavior of channel models. We also describe the formation of one of the necessary PToT component, a 3D fingerprint database. We compare the performance of the KMPF against the CRLB using WiFi signal channel models.