IT IS PROPOSED TO DEVELOP DESENSITIZED OPTIMAL FILTERING TECHNIQUES AND TO IMPLEMENT THESE ALGORITHMS IN A…

Public award description

IT IS PROPOSED TO DEVELOP DESENSITIZED OPTIMAL FILTERING TECHNIQUES AND TO IMPLEMENT THESE ALGORITHMS IN A NAVIGATION AND SENSOR FUSION TOOL KIT. THESE PROPOSED DESENSITIZED OPTIMAL FILTERING TECHNIQUES INCLUDE RECENT ADVANCES IN ROBUST AND/OR ADAPTIVE GENERALIZED KALMAN AND SIGMA-POINT FILTERS FOR NON-GAUSSIAN PROBLEMS WITH UNCERTAIN ERROR STATISTICS, AS WELL AS A PROPOSED NEW TECHNIQUE TO DESENSITIZE THE KALMAN FILTER WITH RESPECT TO PARAMETER UNCERTAINTIES USING A ROBUST TRAJECTORY OPTIMIZATION APPROACH CALLED DESENSITIZED OPTIMAL CONTROL. THESE TECHNIQUES WILL BE IMPLEMENTED IN A RELATIVELY GENERIC ENVIRONMENT WHICH ENABLES THE USER TO IMPORT DYNAMICS AND MEASUREMENT MODELS NECESSARY TO APPLY THESE FILTERING TECHNIQUES TO A PARTICULAR NAVIGATION AND SENSOR FUSION PROBLEM. A VARIETY OF SENSOR MODELS AND NOISE DISTRIBUTIONS WILL BE AVAILABLE FOR THE USER TO SELECT, AND MONTE-CARLO ANALYSIS CAPABILITY WILL BE BUILT INTO THE TOOL KIT TO ENABLE STATISTICAL PERFORMANCE EVALUATIONS. THE TOOL KIT WILL ALSO HAVE A MODULARIZED STRUCTURE SO THAT THE MODULES CAN BE READILY INTEGRATED WITH OTHER APPLICATIONS.