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Cognitive Indoor Positioning and Tracking using Multipath Channel Information

Tue, Mar 01, 2016

During my PhD studies I have introduced and discussed a positioning and tracking system for harsh indoor environments that is aware of its surrounding environment and further is able to act optimally on its environment, i.e. it controls the measurement information-return. The Figure illustrates the schematics of the cognitive positioning/tracking system. The physical main blocks are the cognitive perceptor (CP) and cognitive controller (CC) with built-in memories for the perceived environmental state and the (reciprocally) taken control-actions on the environment. Both are linked via feedback and feedforward information, thus the controller is able to choose new actions based on the perceptor’s Bayesian state information. The perception-action-cycle (PAC) incorporates the sensed environment into the closed loop with the CP and CC.

The resulting cognitive multipath-assisted simultaneous localization and mapping algorithm has the following detailed characteristics:

– Robust online learning of the geometric-probabilistic environment model (GPEM)

–Robustness against outliers in the measurements and false data associations facilitated by probabilistic modeling of the VAs

–Local adaptation to the channel characteristics enabled by online learning of the parameters of the geometric-stochastic channel model (GSCM)

–Based on the perceptive attention, the controllers’ attention is focused on relevant channel and environment features enabled by cognitive control (CC) of the transmitted waveform

All concepts—GPEM, GSCM and CC—intertwined facilitate the desired level of robustness and accuracy of the positioning and tracking system in harsh indoor environments.  

More information can be found in my thesis!

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