Research on desensitized optimal filtering techniques and a navigation and sensor fusion tool kit using advanced filtering techniques is proposed. Research focuses on reducing the sensitivity of Kalman filters with respect to model parameter uncertainties using a robust trajectory optimization approach called Desensitized Optimal Control, developed by the proposing company. The proposed tool kit implements the research results as well as recent advances in robust and/or adaptive generalized Kalman and Sigma-Point filters for non-Gaussian problems with uncertain error statistics. The proposed research and development brings new filtering and sensor fusion techniques to NASA and industry in a convenient package which can be used as a stand-alone toolbox, either for ground support or for onboard applications. Its modular structure enables it to be readily integrated with other tools, and thus enhances the existing fleet of applications. The desensitized optimal filtering research and the feasibility study on components of the proposed tool kit will be carried out concurrently. The tool kit is a generic stand-alone application, and has a modularized structure which facilitates easy integration with existing tools. A suite of sensor models and noise distributions as well as Monte-Carlo analysis capability are included to enable statistical performance evaluations.