Neural Network based UWB Geo-Regioning
Ultra-Wideband (UWB) Geo-RegioningThis positioning technique follows a new approach exploiting the Ultra-Wideband (UWB) nature of the channel to achieve a rough localization of UWB transceivers in rich multipath environment. We suppose that the channel impulse response (CIR) of a transmitter/receiver (TX/RX) pair is almost unique, given by the many resolvable multipath components that result from the individual geographical constellation/position of RX and TX. At a certain RX the CIR received from any TX is like a signature of the TX position. If two TXs have a very similar signature they are very close together with a high probability. Although it has been shown, that the spatial correlation of the signatures keenly decreases within about 10 cm, we could show that there remains enough information to decide, whether two signatures belong to the same geographical area/region or not. We refer to this approach as "geo-regioning". We assume that a region can have a size of several dm3 up to several m3. In data aided geo-regioning the positions of some specific reference nodes in the network are known. This information is used to derive from the regioning process the position location information of all received signals. This facilitates a variety of location aware services and protocols in dense ad-hoc networks. Blind geo-regioning does not use a priori location information. It has many interesting applications such as (i) data fusion in dense sensor networks and (ii) routing in hierarchical sensor networks, where the clusterheads may base the routing selection on the region information.
Neural NetworksNeural networks are composed of simple elements operating in parallel. These elements are inspired by biological nervous systems. As in nature, the network function is determined largely by the connections between elements. You can train a neural network to perform a particular function by adjusting the values of the connections (weights) between elements. Commonly neural networks are adjusted, or trained, so that a particular input leads to a specific target output. The network is adjusted, based on a comparison of the output and the target, until the network output matches the target. Typically many such input/target pairs are needed to train a network.
- The student will get familiar with the existing work on UWB Geo-Regioning and UWB channel modeling. The first goal is to identify channel parameters, which efficiently describe the UWB channel. This is documented in channel modeling literature.
- The identified channel parameters should be estimated from existing UWB channel measurements.
- For the application of neural networks, data preprocessing is essential. The student should identify, which data preprocessing steps are necessary and beneficial.
- The Matlab Neural Network Toolbox is used to design, train, and evaluate a neural network, which performs UWB Geo-Regioning.
- For performance analysis a set of measured channel impulse responses is provided. By means of these data simulations are performed to compare the performance of the neural network approach to existing algorithms and to characterize important algorithm parameters.
The figures show measured power delay profiles of channel impulse responses originating from two different regions.
|Subject area||Ultra Wideband, Positioning, Neural Networks|
|Type of work||40% Theory, 30% Simulation, 30% Software|
|Supervisor||Dr. Christoph Steiner|
|Professor||Prof. Dr. Armin Wittneben|