  Displaced automotive sensor imaging exploits joint processing of the data
acquired from multiple radar units, each of which may have limited individual
resources, to enhance the localization accuracy. Prior works either consider
perfect synchronization among the sensors, employ single antenna radars, entail
high processing cost, or lack performance analyses. Contrary to these works, we
develop a displaced multiple-input multiple-output (MIMO) frequency-modulated
continuous-wave (FMCW) radar signal model under coarse synchronization with
only frame-level alignment. We derive Bayesian performance bounds for the
common automotive radar processing modes such as point-cloud-based fusion as
well as raw-signal-based non-coherent and coherent imaging. For the
non-coherent mode, which offers a compromise between low computational load and
improved localization, we exploit the block sparsity of range profiles for
signal reconstruction to avoid direct computational imaging with massive data.
For the high-resolution coherent imaging, we develop a method that
automatically estimates the synchronization error and performs displaced radar
imaging by exploiting sparsity-driven recovery models. Our extensive numerical
experiments demonstrate these advantages. Our proposed non-coherent processing
of displaced MIMO FMCW radars improves position estimation by an order over the
conventional point-cloud fusion.
