Performance Bounds For Displaced Sensor Automotive Radar Imaging

In automotive radar imaging, displaced sensors offer improvement in localization accuracy by jointly processing the data acquired from multiple radar units, each of which may have limited individual resources. In this paper, we derive performance bounds on the estimation error of target parameters processed by displaced sensors that correspond to several independent radars mounted at different locations on the same vehicle. Unlike previous studies, we do not assume a very accurate time synchronization among the sensors. Instead, we consider only frame-level time alignment which is more common and practical in modern automotive sensor networks. We first develop a displaced multiple-input multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) radar signal model under coarse synchronization and then propose processing models relevant to modern automotive radars such as point-cloud-based fusion and raw signal imaging. Contrary to earlier works based on deterministic Cramér-Rao lower bound, our displaced sensors framework is Bayesian. Numerical experiments with our proposed non-coherent processing of displaced MIMO FMCW radars show an order of performance improvement in position estimation over the conventional point-cloud fusion.