  To accurately estimate locations and velocities of surrounding targets (cars)
is crucial for advanced driver assistance systems based on radar sensors. In
this paper we derive methods for fusing data from multiple radar sensors in
order to improve the accuracy and robustness of such estimates. First we pose
the target estimation problem as a multivariate multidimensional spectral
estimation problem. The problem is multivariate since each radar sensor gives
rise to a measurement channel. Then we investigate how the use of the
cross-spectra affects target estimates. We see that the use of the magnitude of
the cross-spectrum significantly improves the accuracy of the target estimates,
whereas an attempt to compensate the phase lag of the cross-spectrum only gives
marginal improvement. This paper may be viewed as a first step towards applying
high-resolution methods that builds on multidimensional multivariate spectral
estimation for sensor fusion.
