Abstract
Abstract. A tracking solution for collision avoidance in industrial machine tools based on short-range millimeter-wave radar Doppler observations is presented. At the core of the tracking algorithm there is an Extended Kalman Filter (EKF) that provides dynamic estimation and localization in real-time. The underlying sensor platform consists of several homodyne continuous wave (CW) radar modules. Based on In-phase-Quadrature (IQ) processing and down-conversion, they provide only Doppler shift information about the observed target. Localization with Doppler shift estimates is a nonlinear problem that needs to be linearized before the linear KF can be applied. The accuracy of state estimation depends highly on the introduced linearization errors, the initialization and the models that represent the true physics as well as the stochastic properties. The important issue of filter consistency is addressed and an initialization procedure based on data fitting and maximum likelihood estimation is suggested. Models for both, measurement and process noise are developed. Tracking results from typical three-dimensional courses of movement at short distances in front of a multi-sensor radar platform are presented.
Highlights
The fusion of electronics and mechanics is an ongoing process, which benefits from by downscaling and reduced production costs for various kinds of sensor technologies
A tracking solution for collision avoidance in industrial machine tools based on short-range millimeter-wave radar Doppler observations is presented
We introduce the adapted Extended Kalman Filter (EKF) algorithm, process and noise models, and give the initialization procedure based on maximum likelihood estimation
Summary
The fusion of electronics and mechanics is an ongoing process, which benefits from by downscaling and reduced production costs for various kinds of sensor technologies. An even more sophisticated task is the prediction of future machine states and of temporary and instantaneous production steps, such as processes in milling machines This leads to the notion of collision avoidance in automated machine tools in order to prevent damages and downtimes, which cause high maintenance and material costs (Wächter et al, 2014). A new signal processing stage for nonlinear target tracking based on Doppler shift estimates is introduced. It is essentially based on an Extended Kalman Filter (EKF). Mittermaier et al.: Extended Kalman Doppler tracking and model determination ing derived process and noise models, as well as an extensive study on appropriate filter initialization.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
