Artificial Intelligence Controller of a Filter Wheel for Acquisition and Tracking in Congested Environments

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Trusted AI and Autonomy 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: Develop Artificial Intelligence (AI) controlled sensor filter wheel for autonomous recognition of congested conditions and applications of correct filters for best possible scene analysis. DESCRIPTION: Some multispectral low-earth-orbit smaller-satellite-platform space sensors require an operator observing system readouts to command changes in optical/infrared bandpass filter settings and other system parameters in real time, based on varying background conditions in field of view (FOV), in order to acquire and continuously track an object. The operation could potentially be performed more quickly and efficiently using AI to change: filter settings, viewing geometries, day and night sensor controls, solar condition controls, tangent heights, and clutter background scene settings to ensure minimal missed detections and maintain continuity of track. PHASE I: Design and develop innovative solutions, methods, algorithms and concepts to implement automation into sensor declutter controls. Declutter artificial intelligence and/or machine learning algorithm should be narrow in focus and verifiable in operation. The solutions should capture the key areas for new development, suggest appropriate methods and technologies to minimize the time intensive processes, and incorporate new technologies researched during the design and development. PHASE II: Complete a detailed prototype design incorporating government performance requirements. Coordinate with the Government during prototype design and development to ensure the delivered products will be relevant to an ongoing missile defense architecture, data types, and structures. PHASE III DUAL USE APPLICATIONS: Scale-up the capability from the prototype utilizing the new technologies developed in Phase II into a mature, full scale, fieldable capability. Work with missile defense integrators to integrate the technology into a missile defense system level test-bed and test in a relevant environment. REFERENCES: 1) Demirci, S., Ozdemir, C., Akdagli, A. and Yigit, E. (2008), Clutter reduction in synthetic aperture radar images with statistical modeling: An application to MSTAR data. Microw. Opt. Technol. Lett., 50: 1514-1520. https://doi.org/10.1002/mop.23413. 2) E. V. Carrera, F. Lara, M. Ortiz, A. Tinoco and R. Le n, "Target Detection using Radar Processors based on Machine Learning," 2020 IEEE ANDESCON, 2020, pp. 1-5, doi: 10.1109/ANDESCON50619.2020.9272173. 3) Tanvir Islam, Miguel A. Rico-Ramirez, Dawei Han, Prashant K. Srivastava, Artificial intelligence techniques for clutter identification with polarimetric radar signatures, Atmospheric Research, Volumes 109 110, 2012, Pages 95-113, ISSN 0169-8095, https://doi.org/10.1016/j.atmosres.2012.02.007. KEYWORDS: Sensor; Filter Wheel; Artificial Intelligence; Machine Learning; AI; ML; Declutter