Cognitive Indoor Positioning and Tracking using Multipath Channel Information
This addresses indoor positioning challenges for applications requiring accuracy under infrastructural constraints, but appears incremental as it builds on existing multipath filtering concepts.
The paper tackles robust indoor positioning in harsh multipath environments by developing a system that adapts to surroundings and filters out irrelevant information, but no concrete results or numbers are provided.
This paper presents a robust and accurate positioning system that adapts its behavior to the surrounding environment, mimicking the capability of the visual brain to filtering out clutter and focusing attention on activity and relevant information. Especially in indoor environments, which are characterized by harsh multipath propagation, robust positioning is still hard to achieve 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.