FMCW-MIMO Radar-Based Pedestrian Trajectory Tracking Under Low- Observable Environments

Pedestrian trajectory perception is always a challenging task for autonomous urban driving since the weak echoes from pedestrians may be masked by strong background clutters, for example, road infrastructures and other vehicles. As a consequence, this article presents a frequency-modulated continuous wave-multiple input multiple output (FMCW-MIMO) radar-based trajectory tracking method to realize continuous pedestrian detection and tracking under low-observable environments. More specifically, the keystone transform-constant false alarm ratio (KT-CFAR) is first proposed to maximize the output signal-to-interference plus noise ratio (SINR) depending on the time–space correlations of a sequence of consecutive echoes, for the sake of improving the pedestrian detection probability under low-observable conditions. Then, the multiple signal classification (MUSIC) algorithm is employed to provide a high azimuth resolution to improve the multitarget resolving ability. After that, a pedestrian trajectory tracking framework is constructed by means of Bayesian theory and sequential Monte Carlo (SMC) sampling. Finally, simulation and experimental results show that the proposed method can obtain high-precision pedestrian trajectory tracking under low SINR conditions compared with conventional Kalman filter (KF)-based tracking methods.