  We present a low-complexity widely separated multiple-input-multiple-output
(WS-MIMO) radar that samples the signals at each of its multiple receivers at
reduced rates. We process the low-rate samples of all transmit-receive chains
at each receiver as data matrices. We demonstrate that each of these matrices
is low rank as long as the target moves slowly within a coherent processing
interval. We leverage matrix completion (MC) to recover the missing samples of
each receiver signal matrix at the common fusion center. Subsequently, we
estimate the targets' positions and Doppler velocities via the maximum
likelihood method. Our MC-WS-MIMO approach recovers missing samples and
thereafter target parameters at reduced rates without discretization. Our
analysis using ambiguity functions shows that antenna geometry affects the
performance of MC-WS-MIMO. Numerical experiments demonstrate reasonably
accurate target localization at SNR of 20 dB and sampling rate reduction to
20%.
