Multiple Target Tracking in Automotive FCM Radar by Multi-Bernoulli Filter with Elimination of Other Targets
Koichiro Suzuki, Chiharu Yamano, Norikazu Ikoma · 2018
To protect vulnerable road users, such as pedestrians, it is important to realize multi-target tracking in complex scenes. Due to the low signal-to-noise ratio (SNR) of pedestrian targets, the track-before-detect (TBD) approach seems to be effective. However, when an actual radar sensor is used, observation interference between targets, especially pedestrians and higher-SNR objects (such as roadside objects), may occur and lead to an incorrect tracking result. In this paper, we describe an algorithm for a multi-Bernoulli filter for TBD by eliminating targets from the original observation of an automotive fast chirp modulation (FCM) radar that is suited for complex scenes. With sequential Monte Carlo (SMC) implementation of the proposed algorithm, the approach is validated through the simulation of an urban road scene.