  This study investigates the problem of angle-based localization of multiple
targets using a multistatic OFDM radar. Although the maximum likelihood (ML)
approach can be employed to merge data from different radar pairs, this method
requires a high complexity multi-dimensional search process. The multiple
signal classification (MUSIC) algorithm simplifies the complexity to a
two-dimensional search, but no framework is derived for combining MUSIC
pseudo-spectrums in a multistatic configuration. This paper exploits the
relationship between MUSIC and ML estimators to approximate the
multidimensional ML parameter estimation with a weighted combination of MUSIC
pseudo-spectrum. This enables the computation of a likelihood map on which a
peak selection is applied for target detection. In addition to reducing the
computational complexity, the proposed method relies only on transmitting the
estimated channel covariance matrices of each radar pair to the central
processor. A numerical analysis is conducted to assess the benefits of the
proposed fusion.
