In order to better understand the issues associated with Full Motion Video (FMV) geopositioning and to develop corresponding strategies and algorithms, an integrated test bed is required. It is used to evaluate the performance of various candidate algorithms associated with registration of the video frames and subsequent geopositioning using the registered frames. Major issues include reliable error propagation or predicted solution accuracy, optimal vs. suboptimal vs. divergent solutions, robust processing in the presence of poor or non-existent a priori estimates of sensor metadata, difficulty in the measurement of tie points between adjacent frames, poor imaging geometry including small field-of-view and little vertical relief, and no control (points). The test bed modules must be integrated with appropriate data flows between them. The test bed must also ingest/generate real and simulated data and support evaluation of corresponding performance based on module-internal metrics as well as comparisons to real or simulated “ground truth”. Selection of the appropriate modules and algorithms must be both operator specifiable and specifiable as automatic. An FMV test bed has been developed and continues to be improved with the above characteristics. The paper describes its overall design as well as key underlying algorithms, including a recent update to “A matrix” generation, which allows for the computation of arbitrary inter-frame error cross-covariance matrices associated with Kalman filter (KF) registration in the presence of dynamic state vector definition, necessary for rigorous error propagation when the contents/definition of the KF state vector changes due to added/dropped tie points. Performance of a tested scenario is also presented.