Ghost Target Suppression Using Deep Neural Network in Radar-Based Indoor Environment Mapping
Taewon Jeong, Seongwook Lee
Abstract
In this paper, we propose a method to remove ghost targets that occur in radar-based indoor environment mapping. In this work, the ghost target refers to an object that does not physically exist in actual space, but is detected by multiple reflection paths of a radar signal. We first acquire target detection results in an indoor hallway environment using a millimeter-wave band frequency-modulated continuous wave radar sensor. In the detection results, the estimated distance, velocity, and angle values of real and ghost targets have different distributions, which can be used as criteria to classify both types of targets. Therefore, we design a deep neural network (DNN)-based classifier using the target features as input vectors. Through the proposed DNN-based classifier, the ghost targets can be identified and removed from the detection results. We verify the performance of the proposed ghost target suppression method on two different types of radar sensor data sets: 1) radar sensor data acquired from the hallway environment used to train the DNN and 2) radar sensor data acquired from new hallway environments not used for training the DNN. Our proposed method can remove up to 87.36% of ghost targets on average in three different indoor environments.