  With the increase in the number of active satellites and space debris in
orbit, the problem of initial orbit determination (IOD) becomes increasingly
important, demanding a high accuracy. Over the years, different approaches have
been presented such as filtering methods (for example, Extended Kalman Filter),
differential algebra or solving Lambert's problem. In this work, we consider a
setting of three monostatic radars, where all available measurements are taken
approximately at the same instant. This follows a similar setting as
trilateration, a state-of-the-art approach, where each radar is able to obtain
a single measurement of range and range-rate. Differently, and due to advances
in Multiple-Input Multiple-Output (MIMO) radars, we assume that each location
is able to obtain a larger set of range, angle and Doppler shift measurements.
Thus, our method can be understood as an extension of trilateration leveraging
more recent technology and incorporating additional data. We formulate the
problem as a Maximum Likelihood Estimator (MLE), which for some number of
observations is asymptotically unbiased and asymptotically efficient. Through
numerical experiments, we demonstrate that our method attains the same accuracy
as the trilateration method for the same number of measurements and offers an
alternative and generalization, returning a more accurate estimation of the
satellite's state vector, as the number of available measurements increases.
