Ultra-wideband position tracking on an assembly line

This works considers the problem of tracking objects on an assembly line using an ultra-wideband (UWB) positioning system. Assembly line tracking can be accomplished using touch sensors that physically detect when an object reaches a given location. Such tracking requires sensors placed throughout the entire assembly line, and only provides readings at the sensor locations. In contrast, UWB position tracking utilizes a set of sensors surrounding the whole area, enabling continuous position tracking with less infrastructure. Similar tracking can be accomplished using radio frequency identication (RFID) sensing, but this only provides readings when the parts are near RFID readers. The advantage of UWB position tracking is that it can provide sensor readings continuously throughout the entire tracking area. However, UWB position estimates are noisy, typically having an accuracy of 30-100 cm in a room-to-building sized area. This accuracy is sucient for monitoring which part of an assembly line a part is currently traversing, but is not accurate enough to enable precise tooling or positioning. In this work, we are using a map of an assembly line to constrain the motion tracking. This is similar to how a road map can be used to constrain position tracking for a GPS sensor. The idea is that the raw sensor measurements are constrained by the a priori known map of motion along the assembly line. We use these constraints and design a particle filter to improve position tracking accuracy

Filtering Impulses in Dynamic Noise in the Presence of Large Measurement Noise

This work considers the problem of filtering a system in which the dynamic noise occasionally has an impulse value that is an order of magnitude or more larger than its typical expected distribu-tion. This is particularly challenging when the ratio of measurement noise to typical dynamic noise is large enough that the impulse dynamic noise cannot be easily distinguished from a large random occurrence of measurement noise. A new filter model is proposed using a multiple model approach in which one of the models is an impulse. The implementation of the model is demonstrated in a Kalman filter framework. Simulation results show the improvement of the new filter over existing methods across a range of measurement, typical, and impulse dynamic noises. The filter is then ap-plied to three different problems: 2D human motion tracking using ultra-wideband (UWB) position measurements, power system state estimation on a coupled bus, and handling outlier measurement noise in UWB tracking. In each case the new filter demonstrates a 2-4% improvement over existing state-of-the-art techniques