A Graph-Based Track-Before-Detect Algorithm for Automotive Radar Target Detection

Abstract

Among various types of sensors, millimeter-wave radar is widely used, especially in the field of intelligent transportation, but often limited by low signal-to-noise ratio at the same time. As a result, sensor fusion is currently the most common way for target detection. In contrast to this, we focus on the problem of single radar target detection. Considering that automotive radar usually contains a plot generation module (which provides a limited number of candidate targets), the track-before-detect method can meet real-time requirements in ADAS. Successive track cancellation is one of the effective strategies for enhancing the performance of track-before-detect algorithms. We are inspired by this and derive a graph-based radar target detection algorithm. Since the data to be processed between frames have structural similarity, we can measure the correlation between plots better without a large amount of calculation by taking advantage of the dynamically maintained graph. Furthermore, we define a simple state transition process of each plot, which enhances robustness for temporary target signal loss. We verify the effectiveness of the proposed algorithm with both the simulation experiment and real data test in the last part of the paper.