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This concept presents a new statistical likelihood function and Bayesian analysis update for non-standard measurement types that rely on associations between observed and cataloged features. These measurement types inherently contain non-standard errors that standard techniques, such as the Kalman filter, make no effort to model, and this mismodeling can lead to filter instability and degraded performance. Vision-based navigation methods utilizing the Kalman filter involve a preprocessing step to identify features within an image by referencing a known catalog. However, errors in this pre-processing can cause navigation failures. AFP offers a new approach, processing points generated by features themselves without requiring identification. Points such as range or bearing are directly processed by AFP. Operating on finite set statistics principles, AFP treats data as sets rather than individual features. This enables simultaneous tracking of multiple targets without feature labeling. Unlike the sequential processing of the Kalman filter, AFP processes updates in parallel, independently scoring each output based on rigorous mathematical functions. This parallel processing ensures robust navigation updates in dynamic environments, and without requiring an identification algorithm upstream of the filter. Computational simulations conducted at Johnson Space Center demonstrate that AFP's performance matches or exceeds that of the ideal Kalman filter, even under non-ideal conditions. Anonymous Feature Processing for Enhanced Navigation is at a technology readiness level (TRL) 4 (component and/or breadboard validation in laboratory environment) and is now available for patent licensing. Please note that NASA does not manufacture products itself for commercial sale.