We propose a wireless localization algorithm based on complex-domain super multidimensional scaling (CD-SMDS) augmented with a self-localization (SL) component, whereby each target tracks its own motion by incorporating bearing in- formation, obtained e.g., from integrated inertial sensors. The proposed method improves localization accuracy by simultaneously using the time series information of distance and angle associated to the SL information in order to construct the SMDS rank-one edge kernel matrix, maximizing the noise reduction effect of the low-rank truncation via singular value decomposition (SVD). The efficacy of the proposed method over the original CD-SMDS is confirmed via software simulations, and compared with an SL-aware Cram´er-Rao lower bound (CRLB).