Relative RADAR cross section based feature identification with millimeter wave RADAR for outdoor SLAM

Millimeter wave RADARs are more robust than most other sensors used in outdoor autonomous navigation in that their performance is less affected by dust, fog, moderate rain or snow and ambient lighting conditions. Millimeter wave (MMW) RADAR differs from other range sensors as it can provide complete power returns for many points down range. Im addition, MMW RADAR has a comparatively long range which can enable a vehicle to localize efficiently when there are only a few features in the environment. A method for estimating the relative RADAR cross section of objects is explained. This is useful in SLAM as we can predict the relative RCS of objects based on predicted observations which will allow feature discrimination so that features can be identified by parameters other than their coordinates. A new augmented state vector for an extended Kalman filter is introduced which includes the relative RADAR cross sections of features, and the RADAR constants and losses along with the usual vehicle pose and feature locations. An estimate of the received noise when a target is present and in target absence has been carried out for accurately predicting the RADAR power-range spectra. Finally a SLAM formulation using the proposed methods is shown. This work is a step towards robust outdoor SLAM with MMW RADAR based continuous power spectra.