In this paper, we investigate a non-lineof-sight (NLOS) sensing problem at terahertz frequencies. To be able to observe the targets shadowed by a blockage, we propose a method using reconfigurable intelligent surfaces (RIS). We employ a bistatic radar system and scan the obstructed area with RIS using hierarchical codebooks (HCB). Moreover, we propose an iterative maximum likelihood estimation (MLE) scheme to yield the optimum sensing accuracy, converging to Cramer-Rao lower bound (CRLB). We take band-specific effects such as diffraction and beam squint into account and show that these effects are relevant factors affecting localization performance in RIS-employed radar setups. The results show that under NLOS conditions, the system can still localize all the targets with very good accuracy using the RIS. The initial estimates obtained by the HCBs can provide centimeter-level accuracy, and when the optimal performance is needed, at the cost of a few extra transmissions, the proposed iterative MLE method improves the accuracy to sub-millimeter accuracy, yielding the position error bound.