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Accurate localization and perception are pivotal for enhancing the safety andreliability of vehicles. However, current localization methods suffer fromreduced accuracy when the line-of-sight (LOS) path is obstructed, or acombination of reflections and scatterings is present. In this paper, wepresent an integrated localization and sensing method that delivers superiorperformance in complex environments while being computationally efficient. Ourmethod uniformly leverages various types of multipath components (MPCs) throughthe lens of random finite sets (RFSs), encompassing reflections, scatterings,and their combinations. This advancement eliminates the need for the multipathidentification step and streamlines the filtering process by removing thenecessity for distinct filters for different multipath types, a requirementthat was critical in previous research. The simulation results demonstrate thesuperior performance of our method in both robustness and effectiveness,particularly in complex environments where the LOS MPC is obscured and insituations involving clutter and missed detection of MPC measurements.

Exploiting Multipath Information for Integrated Localization and Sensing via PHD Filtering