  We develop a new tensor model for slow-time multiple-input multiple output
(MIMO) radar and apply it for joint direction-of-departure (DOD) and
direction-of-arrival (DOA) estimation. This tensor model aims to exploit the
independence of phase modulation matrix and receive array in the received
signal for slow-time MIMO radar. Such tensor can be decomposed into two tensors
of different ranks, one of which has identical structure to that of the
conventional tensor model for MIMO radar, and the other contains all phase
modulation values used in the transmit array. We then develop a modification of
the alternating least squares algorithm to enable parallel factor decomposition
of tensors with extra constants. The Vandermonde structure of the transmit and
receive steering matrices (if both arrays are uniform and linear) is then
utilized to obtain angle estimates from factor matrices. The multi-linear
structure of the received signal is maintained to take advantage of
tensor-based angle estimation algorithms, while the shortage of samples in
Doppler domain for slow-time MIMO radar is mitigated. As a result, the joint
DOD and DOA estimation performance is improved as compared to existing angle
estimation techniques for slow-time MIMO radar. Simulation results verify the
effectiveness of the proposed method.
