Predictive Error Correction Algorithm for Hypersonic Applications

OUSD (R&E) MODERNIZATION PRIORITY: Hypersonics TECHNOLOGY AREA(S): Air Platform The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: Determine best method to implement predictive algorithm for instantaneous error analysis and processing for hypersonic navigation applications. DESCRIPTION: Hypersonic vehicles can have large inertial measurement errors due to their flight patterns and speeds. The need for rapid error correction or even predictive methods to apply adjustments in anticipation of accumulated errors is necessary to ensure flight accuracy. Use of Kalman filters is a common state of the art approach. This topic seeks software improvements beyond state of the art that can be applied to existing IMUs. Proposed solutions could focus on new algorithms, improved Kalman filter, applications of machine leaning, or artificial intelligence to hypersonic navigation that would allow for more precise use of hypersonic applications in defense schemas. Solution should be a stand-alone algorithm or solution that can be incorporated into future missile defense hardware. Solutions should increase error correction estimates by greater than 25%. Solutions should also maintain accuracy for at least 200s without GPS input at 200Hz sampling rate or higher. PHASE I: Design and develop innovative solutions, methods, and concepts to correct or mitigate current and anticipated error in hypersonic inertial measurement units in real time. The solutions should capture the key areas for new development, suggest appropriate methods and algorithms to minimize the time intensive processes, and incorporate new technologies researched during the design and development. PHASE II: Complete/refine a detailed algorithm incorporating Government performance requirements and current leading edge methods. Coordinate with the Government during design and development to ensure that the delivered products will be relevant to an ongoing missile defense architecture and data types and structures. PHASE III DUAL USE APPLICATIONS: Adapt the capability from the prototype utilizing the new technologies and/or algorithms developed in Phase II into a mature, full scale, fieldable capability. Work with missile defense integrators to integrate the advancement into a missile defense system level test-bed and test in a relevant environment. REFERENCES: Dutta, P. and R. Bhattacharya. Nonlinear Estimation of Hypersonic State Trajectories in Bayesian Framework with Polynomial Chaos. Journal of Guidance Control and Dynamics 33 (2010): 1765-1778. Sun, Tao & Xin, Ming. (2014). (Hypersonic Entry Vehicle State Estimation Using High-degree Cubature Kalman Filter). AIAA AVIATION 2014 -AIAA Atmospheric Flight Mechanics Conference. 10.2514/6.2014-2383. G. Hu, L. Ni, B. Gao, X. Zhu, W. Wang and Y. Zhong, "Model Predictive Based Unscented Kalman Filter for Hypersonic Vehicle Navigation With INS/GNSS Integration," in IEEE Access, vol. 8, pp. 4814-4823, 2020, doi: 10.1109/ACCESS.2019.2962832. C. Shen et al., "Seamless GPS/Inertial Navigation System Based on Self-Learning Square-Root Cubature Kalman Filter," in IEEE Transactions on Industrial Electronics, vol. 68, no. 1, pp. 499-508, Jan. 2021, doi: 10.1109/TIE.2020.2967671. KEYWORDS: Kalman, filter, IMU, inertial, artificial intelligence, machine learning, algorithm, navigation error, navigation, IMU