Multi-sensor Doppler radar for machine tool collision detection

Machine damage due to tool collisions is a widespread issue in milling production. These collisions are typically caused by human errors. A solution for this problem is proposed based on a low-complexity 24 GHz continuous wave (CW) radar system. The developed monitoring system is able to detect moving objects by evaluating the Doppler shift. It combines incoherent information from several spatially distributed Doppler sensors and estimates the distance between an object and the sensors. The specially designed compact prototype contains up to five radar sensor modules and amplifiers yet fits into the limited available space. In this first approach we concentrate on the Doppler-based positioning of a single moving target. The recorded signals are preprocessed in order to remove noise and interference from the machinery hall. We conducted and processed system measurements with this prototype. The Doppler frequency estimation and the object position obtained after signal conditioning and processing with the developed algorithm were in good agreement with the reference coordinates provided by the machine's control unit

Extended Kalman Doppler tracking and model determination for multi-sensor short-range radar

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.<br><br> 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