Cognitive Indoor Positioning and Tracking using Multipath Channel Information
This thesis presents a robust and accurate positioning system that adapts its behavior to the surrounding environment like the visual brain, mimicking its capability of filtering out clutter and focusing attention on activity and relevant information. Especially in indoor environments, which are characterized by harsh multipath propagation, it is still elusive to achieve the needed level of accuracy robustly under the constraint of reasonable infrastructural needs. In such environments it is essential to separate relevant from irrelevant information and attain an appropriate uncertainty model for measurements that are used for positioning.
The thesis has the goal to approach this objective more closely by implementing the four basic principles for human cognition, namely the perception-action-cycel (PAC), memory, attention and intelligence, into the positioning systems. To encounter all these principles, the concepts of Multipath-assisted indoor navigation and tracking (MINT) are intertwined with the principles of cognitive dynamic systems (CDSs) that were developed by Simon Haykin and co-workers.
MINT exploits specular multipath components (MPCs) that can be associated to the local geometry using a known floor plan. In this way, MPCs can be seen as signals from additional virtual sources—so-called virtual anchors (VAs)—that are mirror-images of a physical anchor w.r.t. features of a floor plan. Hence additional position-related information is exploited that is contained in the radio signals. This position-related information is quantified based on the Cramer Rao lower bound (CRLB) of the position error for a geometry-based stochastic channel model (GSCM) to account for geometry dependent MPCs as well as for stochastically modeled diffuse/dense multipath (DM). It shows that the signal-to-interference-plus-noise-ratio SINR quantifies the amount of position-related information.
However, inaccuracies in the floor plan and the resulting uncertainties in the VAs, are not considered at this stage. Hence, probabilistic MINT is introduced in this thesis that has the aims (i) to remove the requirement of a precisely known a-priori floor plan and (ii) to cope with uncertainties in the environment representation. In probabilistic MINT the VAs are comprised in a geometry-based probabilistic environment model (GPEM). In a consecutive step, this algorithm is extended to a probabilistic multipath-assisted feature-based simultaneous localization and mapping (SLAM) approach that can operate without any prior knowledge of the floor plan.
The GSCM and GPEM represent the built-in memory of the developed cognitive positioning system. In contrast, the attention is executed by the algorithm itself by enabling separation between relevant and irrelevant information and focusing onto the memorized model parameters. Closing the PAC with transmit waveform adaptation based on a cognitive controller (CC) supports this separation process and also facilitates (i) the feature of gaining new position-related information from the surrounding environment and (ii) suppression of additional noise. The interplay of all these characteristics is the key facilitator of intelligent behavior of the cognitive positioning algorithm.
The fulltext of this thesis can be found here.