Bayesian CRLB for Blind Indoor Localization with Imperfect Receiver Synchronization

We examine the bounds on the error variance for radio-based indoor localization. In the considered dense multipath scenario, an asynchronously transmitting (blind) node sends a wideband signal which is captured by a fixed infrastructure comprising several multi-channel receiver anchors at known positions. The transmitter location can be inferred by calculations based on time difference of arrival and angle of arrival. This requires a certain degree of phase synchronization between the channels of the anchors and time synchronization between the anchors themselves. We present statistical models of the synchronization impairments and derive the resulting Bayesian information matrix. Evaluating the Bayesian Cramér-Rao lower bound (CRLB) on the localization error variance in regard to different aspects of synchronization quality provides insights into the specific requirements on the implementation of a suitable synchronized receiver infrastructure. In conjunction with associated simulations and real-world measurements, this has the potential to further facilitate cost-efficient localization of low-power nodes in the context of the internet of things.