Radio-based localization systems conventionally require stationary reference points (e.g., anchors) with precisely surveyed positions, making deployment time-consuming and costly. This article presents an empirical evaluation of collaborative self-calibration for ultra-wideband (UWB) networks, extending a Bayesian approach based on grid-based uncertainty propagation. The enhanced algorithm reduces measurement availability requirements while maintaining positioning accuracy through probabilistic state estimation. We validate the approach using real-world data from controlled indoor experiments with 12 nodes in a static environment. Experimental evaluation yields 0.28 m mean ranging error under line-of-sight conditions and 1.11 m overall ranging error across mixed propagation scenarios. Results confirm the algorithm's resilience to measurement noise and partial connectivity scenarios typical in industrial deployments. We evaluate algorithm robustness under nonline-of-sight-contaminated initialization, showing graceful accuracy degradation (median error 0.62-0.99 m) compared to closed-form methods that exhibit substantial performance collapse (median error up to 2.43 m). The findings contribute to automated UWB network initialization for indoor positioning applications, reducing infrastructure dependence compared to manual anchor calibration procedures.