Integrated sensing and communications (ISAC) is poised to be a native technology for the forthcoming Sixth Generation (6G) era, with an emphasis on its potential to enhance communications performance through the integration of sensing information, i.e., sensing-assisted communications (SAC). Nevertheless, existing research on SAC has predominantly confined its focus to scenarios characterized by minimal clutter and obstructions, largely neglecting indoor environments, particularly those in industrial settings, where propagation channels involve high clutter density. To address this research gap, background subtraction is proposed on the monostatic sensing echoes, which effectively addresses clutter removal and facilitates detection and tracking of user equipments (UEs) in cluttered indoor environments with SAC. A realistic evaluation of the introduced SAC strategy is provided, using ray tracing (RT) data with the scenario layout following Third Generation Partnership Project (3GPP) indoor factory (InF) channel models. Simulation results show that the proposed approach enables precise predictive beamforming largely unaffected by clutter echoes, leading to significant improvements in effective data rate over the existing SAC benchmarks and exhibiting performance very close to the ideal case where perfect knowledge of UE location is available.