The Pentagon’s Defense Innovation Unit (DIU) is looking for commercial companies to provide input on improving the Department of Defense’s (DoD) awareness of intentional disruptions of global navigation satellite systems (GNSS), including GPS. They want the solutions to leverage machine-driven analytics and datasets derived from publicly/commercially available information (PAI/CAI) to provide a situational awareness capability for intentional global navigation satellite system (GNSS) disruptions.
This solicitation is particularly focused on persistent, large-area coverage of falsified GNSS emitters that result in localized spoofing phenomenology. The GNSS, to include the Global Positioning System (GPS) satellite network, has proven indispensable to position, navigation, and timing (PNT) capabilities in both DoD as well as the commercial domain. The entire world is dependent on GNSS or GNSS-based systems, yet the GPS architecture and its users are vulnerable to denial and manipulation by adversarial actors. To date, intentional manipulation of GNSS operations have enabled nefarious activities, to include narcotics trafficking, unapproved operation of autonomous vehicles, illegal fishing, and sea-borne piracy.
Additionally, nation-state use of GNSS jamming or spoofing systems may extend beyond the area of conflict, causing deleterious effects on civilian populations. Such activities degrade or deny critical geolocation capabilities and further introduce hazards to safety-of-life-navigation, critical infrastructure, and emergency response services.
Desired Product Capabilities
Competitive proposals will address as many of the following capabilities as possible. Note that the Defense Innovation Unit (DIU) is open to considering solutions that cover a subset or the entirety of interest areas listed below.
- Commercially available data sources: Collect, provide, and/or ingest various types of relevant data necessary for subsequent persistent GNSS interference analysis. Data types include, but are not limited to: automatic identification system (AIS), automatic dependent surveillance-broadcast (ADS-B), cellular phones, satellite phones, app-based GNSS metadata, and GNSS receivers. There is no preference to the source of the datasets (e.g., space-based or terrestrial) -- only its utility and efficiency in relation to the subsequent analytic models and tools.
- GNSS interference analytics: Provide automatic screening, pattern recognition, and cueing of CAI data sources and available databases (a) for known GNSS interference observables and related sources, (b) identify GNSS observables that are not currently associated with GNSS interference, (c) improve and optimize CAI data source selection based on targeted-GNSS interference data, (d) identify new GNSS interference events, and (e) predict future behaviors based on historical records, if possible.
- Output Schema: Data should be output in common, open formats (i.e., non-proprietary), supporting both real-time and historical detection using visualization tools and in-depth, real-time, and historical GNSS interference event analysis. Prototype output is envisioned to provide:
- A near-real-time, common operating picture for stakeholders and safety-of-life operations.
- Event identification and analysis
- Automated alerting for new events once manifestations are mature.
Additional capabilities of interest:
- Although the examples found in open-source literature are primarily examples of GNSS spoofing, the desired prototype may be extended to other use cases and their respective analytics. Alternative use cases include, but are not limited to:
- GNSS denial through in-band and purposeful interference: inability to use and report geo-spatial systems within a region.
- Detection of improper, fake, false, counterfeit, or multiple redundant vehicle position reports.
- Geospatial denial and deception techniques for marine vessels: sudden/unconventional headings/speeds, intermittent transmission of geospatial transponder.
- Tailored solutions for continental United States region of interest.
- Correcting instances of GNSS disruption via passive and/or model-based capabilities.
Qualities of interest include:
- Utilization of a mixture of relevant data--commercial or open-source-- that maximizes a balance of persistent coverage with the cost for access.
- Provision of tools are accessible in open, cloud-based environment.
- Interoperability with open APIs (e.g., Seavision) or other USG visualization tools.
- Accommodation of consistent model retraining and maintenance.
Companies providing analytic capabilities only as well as those providing unique datasets only are both encouraged to apply; in such cases teaming is highly recommended. Vendors may provide multiple solutions in response to the AOI.
Click here for details on eligibility requirements or submit your solution.