TECH FOCUS AREAS: Artificial Intelligence/Machine Learning; General Warfighting Requirements (GWR) TECHNOLOGY AREAS: Air Platform; Battlespace OBJECTIVE: Develop hardware and/or software for radio frequency (RF)-based navigation aiding on weapon systems. Hardware should be appropriate for both mid-course and terminal sensing, software/algorithm development should be focused on mid-course navigation aiding. Hardware and algorithms should be appropriate for a service-based open architecture approach for both terminal and mid-course applications. DESCRIPTION: Weapon navigation in contested environments is a critical capability for the Air Force and DoD. Many weapon systems already employ radio frequency (RF) terminal seekers. It is desired to modify or replace these systems with multi-role sensing and processing capabilities. In addition to terminal functionality, these capabilities should also provide for navigation aiding in Global Positioning System (GPS)-denied or degraded scenarios. This effort seeks to develop or modify RF sensor payloads and/or RF signal processing capabilities in support of modular, service-based GPS-denied navigation capabilities. Hardware: Hardware solutions should target weapon appropriate size, weight, and power (SWaP), provide direct access to in-phase and quadrature components (in-phase and quadrature (IQ) data) and other raw data sources, all necessary sensor metadata, act in a GPS-agnostic manner, and be operable with All Source Positioning and Navigation (ASPN) 2.0 or higher compliant interface control documents (ICDs) wherever feasible. Sensor development proposals specifically intended for research and development efforts may be larger SWaP, but should have a clear path to a refined footprint. Specific SWaP goals and details on ASPN ICDs will be provided to perspective proposers in a FAQ document. Proposals where sensor operation requires an inertial navigation solution (INS) input are appropriate; however, if that navigation solution is not GPS disciplined, the sensor must continue to function and the expected degradation of sensor performance as a function of INS uncertainty should be noted. The ability to leverage the sensor for both mid-course and terminal may require a wide field of regard, this may be achieved though digital or mechanical steering, a multi-antenna configuration, or other mechanisms within SWaP constraints. Hardware which provides the broader system with increased flexibility, e.g., frequency band options, multiple receive channels, or other properties that expand possible down-stream algorithm development, will be favored. Hardware focused proposals should also include base-line signal processing capabilities. Algorithms/Software: Algorithm development should focus on RF signal processing capability aimed at providing explicit navigation feedback to an ASPN compliant navigator, i.e., the processed RF data should provide something akin to a direct measurement (and uncertainties) of position, velocity, or attitude or a bearing to known features in the environment. Other navigation relevant inputs are also acceptable, e.g., nonlinear feedback appropriate for particle filters, or other estimators/optimizers, that provide likelihoods with respect to position or other navigation states. Algorithms are intended to be real-time and appropriate for on-board processing on weapon system processors (no assumed down-link with external processing) and compatible with ASPN ICDs whenever feasible. Algorithms can assume access to high-rate inertial navigation system (INS) input, however, that input should not be assumed to be GPS disciplined. Systems which provide loosely coupled feedback are preferred, however tightly coupled systems, if ASPN compliant, are also acceptable when tight coupling significantly improves efficacy. Algorithm implementations may be black box in nature (the specific instantiation), however the algorithms itself must be detailed mathematically as part of the effort. Requirements and assumptions on input data should be explicit, and signal processing capabilities providing broader flexibility in input requirements (e.g., flexibility in band, look angles, etc.) will be favored. General: Hardware and software approaches that are agnostic to INS input errors, yet still produce relevant measurement inputs for a navigator, are highly desirable. Specifically, insensitivity to position error and heading error will be most advantageous. Velocities, roll, and pitch will be degraded, but still reasonably well known, however sensitivity to these states should be noted. The following references are an incomplete list example RF-based data products which could likely be leveraged as a navigation aid (with additional development) if they, or something highly similar could be produced without implicit or explicit reliance on GPS. Works of interest include, but are not limited to, multi-angle Synthetic Aperture Radar (SAR) imaging on a unified coordinate system (high-speed platform) [1], interferometric multimode SAR for high quality terrain mapping [2], SAR image retrieval from SAR databases [3], and SAR to EO image matching [4]. Additional, classic hardware and processing capabilities are also desirable. Real-time processing such as synthetic aperture radar image formation or other modalities in situations when GPS is available (or the INS solution is within a specified tolerance) would provide added value to a proposal but is considered secondary to non-GPS disciplined capabilities. PHASE I: A successful Phase I effort develops algorithms focusing on radio frequency (RF) signal processing capability aimed at providing explicit navigation feedback to an ASPN compliant navigator. The processed RF data should provide something akin to a direct measurement (and uncertainties) of position, velocity, or attitude. Alternately, it should provide a bearing to known features in the environment. Other navigation relevant inputs are also acceptable, e.g., nonlinear feedback appropriate for particle filters, or other estimators/optimizers, providing likelihoods with respect to position or other navigation states. Algorithms are intended to be real-time and appropriate for on-board processing on weapon system processors (no assumed down-link with external processing) and compatible with ASPN ICDs whenever feasible). PHASE II: A successful Phase II effort will constitute the development of a hardware system or implementation and testing of real-time signal processing. Hardware development efforts will produce prototype hardware systems appropriate for (surrogate unmanned aerial vehicle (UAV)) flight environments and demonstrate data acquisition (and potentially signal processing) on AFRL-lead UAV flights. Algorithm developers will be provided relevant government furnished equipment (GFE) data, but may propose to use their own RF data, and will develop real-time code for signal processing which provides and navigation relevant output appropriate for an ASPN compliant navigator. PHASE III DUAL USE APPLICATIONS: Phase III will consist of transitioning sensor hardware and software to an operationally approved ASPN compliant navigation system on an operational UAV or weapon system. NOTES: 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 proposed tasks intended for accomplishment by the FN(s) in accordance with the Announcement and within the AF Component-specific instructions. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. Please direct questions to the Air Force SBIR/STTR Help Desk: usaf.team@afsbirsttr.us REFERENCES: [1] Chang, Wensheng et al. A Novel Multi-Angle SAR Imaging System and Method Based on an Ultrahigh Speed Platform. Sensors (Basel, Switzerland) vol. 19,7 1701. 10 Apr. 2019, doi:10.3390/s19071701. [2] H. Yang, C. Chen, S. Chen, F. Xi and Z. Liu, "Interferometric Phase Retrieval for Multimode InSAR via Sparse Recovery," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 1, pp. 333-347, Jan. 2021, doi: 10.1109/TGRS.2020.2994197. [3] L. Jiao, X. Tang, B. Hou and S. Wang, "SAR Images Retrieval Based on Semantic Classification and Region-Based Similarity Measure for Earth Observation," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 8, pp. 3876-3891, Aug. 2015, doi: 10.1109/JSTARS.2015.2429137. [4] Rostami M, Kolouri S, Eaton E, Kim K. Deep Transfer Learning for Few-Shot SAR Image Classification. Remote Sensing. 2019; 11(11):1374. https://doi.org/10.3390/rs11111374 KEYWORDS: SAR; RF; Navigation; GPS-denied; GPS-Degraded; Open Architecture; ASPN; Modular
