In recent years, the need for error characterization, fault detection, and exclusion of navigation sensors has increased. Environments where GNSS performance is degraded or denied require the knowledge and application of alternative sensors such as cameras, magnetometers, and small ranging radios operating outside the GNSS band. Currently, vehicles such as unmanned aerial systems (UAS) may operate with a suite of these ’alt-nav’ sensors performing measurements across multiple domains. However, the addition of such sensors has increased the likelihood of having a mismodeled and/or faulty sensor, affecting the accuracy and performance of the overall navigation solution. Unlike traditional two-sensor systems such as GPS-Inertial integration, systems involving three or more sensors present the problem of ambiguity as to which sensor is adversely affecting the navigation solution. While extensive research into fault detection and exclusion has been conducted for standalone sensors such as ARAIM for GNSS systems, or Kalman filtering used in integrated two-sensor GNSS-Inertial systems, robustness and error resiliency for multi-sensor (i.e. three or more) systems remains largely unresolved. This problem presents the need for a robust framework that can maintain navigation integrity despite the additional sensor modalities. One proposed framework to solve the multi-sensor resiliency problem is known as the Autonomous and Resilient Management of All-Source Sensors for Navigation (ARMAS) and its associated fault detection algorithm, SAARM (Sensor Agnostic All-Source Residual Monitoring). This work provides insight into the performance of these algorithms with real GNSS data. The original work evaluated the performance on only simulated data.