  Algorithms for joint mutual interference mitigation and object parameter
estimation are a key enabler for automotive applications of frequency-modulated
continuous wave (FMCW) radar. The underlying signal model poses a challenge for
signal separation, since both the coherent radar echo and the non-coherent
interference influenced by individual multipath propagation channels must be
considered. In particular, under certain assumptions, the model is described as
a superposition of multipath channels weighted by parametric chirp envelopes in
the case of interference. In this paper, we introduce a method inspired by
sparse Bayesian learning (SBL) to detect and estimate radar object parameters
while also estimating and successively canceling the interference signal. An
augmented probabilistic model is employed that uses hierarchical Gamma-Gaussian
prior model for each multipath channel separately. Based on this model an
iterative inference algorithm is derived using the variational
expectation-maximization (EM) methodology. The algorithm is statistically
evaluated in terms of object parameter estimation accuracy and robustness,
indicating that it is fundamentally capable of achieving the Cramer-Rao lower
bound (CRLB) with respect to the accuracy of object estimates and it closely
follows the radar performance achieved when no interference is present.
