Location Fingerprinting for Ultra-Wideband Systems

The Key to Efficient and Robust Localization

This project introduces and studies a novel position location concept, which provides accurate position estimates in dense multipath and non-line-of-sight propagation environments. The main idea is to apply the location fingerprinting paradigm of position location to channel impulse responses with ultra-wide bandwidth. The large bandwidth enables a fine temporal resolution of the multipath propagation channel, which in turn acts as a unique location fingerprint of the positions of transmitter and receiver.

In the first part of this project, a location fingerprinting framework is developed from a communication theoretic perspective. The position location problem is formulated as hypothesis testing problem, such that fundamental methods from statistical detection theory can be ap- plied. Location fingerprints are modeled by parameterized probability density functions. Different hypotheses are distinguished by these parameters, which have to be estimated during a training phase. This framework generalizes a wide class of location fingerprinting approaches and enables the systematic derivation of optimal classification rules and theoretical performance analysis.

In the second part, location fingerprinting with two specific ultra-wideband receiver struc- tures is studied in detail. The first receiver is able to perform channel estimation. The corresponding location fingerprints are chosen as Nyquist sampled versions of the estimated channel impulse responses. The second receiver is a low complexity generalized energy detection receiver, where the energy samples at the output of the analog front-end serve as location fingerprints. In order to derive optimal classification rules, it is necessary to establish a stochastic description of the location fingerprints. This stochastic modeling is performed on the basis of measured data and a model selection criterion. The position location perfor- mance of both receiver structures is analyzed theoretically and experimentally with measured data. It is shown that decimeter accuracy is achievable with both receiver structures in dense multipath and non-line-of-sigh propagation environments. However, these performance in- vestigations reveal also a major shortcoming of the proposed method: A large amount of training data is required, in order to achieve high position location accuracy.

This issue is addressed in the third part of the project, where two promising techniques are proposed, in order to increase the efficiency of the training phase. At first, the position location problem is reformulated, such that the training phase can be combined with the localization phase in an iterative manner. Results from the localization phase are used as additional training data. Based on experimental performance results it is shown that the required amount of training data can be significantly reduced. The second technique is even more promising. Only very few measured channel impulse responses - theoretically only three per hypothesis for two-dimensional localization - are required during the training phase for parameter estimation. This efficient training phase is based on a geometrical channel model and exploits a priori knowledge about the geometry of the propagation environment. Experimental performance evaluation shows the high potential of this approach to achieve affordable complexity of the training phase.