Deep Learning-Based Direction-of-Arrival Estimation in Automotive MIMO Radars under Multipath Propagation

Abstract

Introduction . Today, millimeter-wave radars are a key component of driver-assistance systems. However, in urban scenarios, the performance of conventional methods for estimating the direction of arrival of signals (DOA) is degraded by multipath effects, which can lead to ghost targets. This article describes an algorithm for classifying signals caused by single reflection and multipath propagation and estimating their DOA. Aim . To develop and investigate experimentally a method for estimating the angular coordinates of objects in MIMO radars in the presence of multipath propagation. Materials and methods . A two-stage signal processing algorithm was proposed. In the first stage, a convolutional neural network classifies the signal scenario as one of the following types: direct path, multipath, or multiple target. In the second stage, the direction of arrival is estimated by the standard MUSIC method and a modified MUSIC method based on reconstruction of the signal correlation matrix. Training and validation were performed on a hybrid dataset consisting of data generated using a signal propagation model and actual measurements obtained by a TI AWR1843 MIMO radar. Results . The proposed algorithm can be used to classify signal propagation scenarios with high accuracy and to estimate the angular coordinates of objects. The proposed method was tested on an open dataset. The results show that the classification results are consistent with the mathematical model of signal propagation. Conclusion . The proposed approach is effective for multipath detection. The proposed algorithm allows the robustness of MIMO radar scene estimation to be improved.