Quantitative analysis of rodent behavior in naturalistic settings is crucial for neuroscience, yet traditional methods often lack precision or scalability. While ultra-wideband (UWB) sensor tracking provides centimeter-level localization, standard metrics like root mean square error (RMSE) fail to capture the probabilistic and sequential nature of behavior. We introduce a probabilistic, information-theoretic framework that leverages high-resolution UWB sensor trajectories to address this gap. By integrating Shannon entropy to quantify uncertainty, Bernoulli modeling to assess accuracy thresholds, and first-order Markov chains to characterize state dynamics, our approach derives interpretable behavioral markers directly linked to signal quality. Empirical evaluation in an open-field arena demonstrated robust tracking under line-of-sight (LoS; RMSE: 20Â mm) and non-line-of-sight (NLoS; RMSE: 35Â mm) conditions. Crucially, we show that physical-layer impairments propagate to behavioral metrics: NLoS conditions increased entropy from 1.15 to 1.78Â bits, reduced the probability of achieving sub-20Â mm accuracy from 63.2% to 27.7%, and decreased state persistence, indicating greater behavioral fragmentation. By treating UWB signals as a probabilistic information source, our computationally efficient framework establishes a methodological bridge between engineering performance and neuroscience, enabling scalable, reproducible, and low-bias behavioral quantification suitable for preclinical research in complex environments.