Expeditionary Intelligence, Surveillance, Reconnaissance Science and Technology | Apply Online

Opportunity Summary:

The Expeditionary Intelligence, Surveillance, Reconnaissance (ISR) Science and Technology opportunity (Funding Opportunity Number N00014-15-R-BA010) is a Broad Agency Announcement (BAA) issued by the Office of Naval Research (ONR). It is a discretionary research and development solicitation aimed at improving tactical, expeditionary ISR by tackling a set of capability gaps ONR identifies as persistent shortcomings in real-world deployed environments. In practical terms, it is looking for new methods and prototype-level advances that help military users collect, manage, interpret, and fuse diverse information faster and more reliably in operational settings where data is messy, incomplete, and time-sensitive. The funding instrument types listed are grant and procurement contract, and the activity category is Science and Technology and other Research and Development under CFDA 12.300 (Basic and Applied Scientific Research). The program anticipates making about 8 awards out of an estimated total funding level of about $12,000,000, with no cost-sharing requirement specified.

The technical scope centers on extracting operationally useful intelligence from large, heterogeneous datasets, especially data that does not fit neatly into traditional relational databases or clean analytic pipelines. A major emphasis is advanced query capabilities against NoSQL data stores, reflecting the reality that modern ISR data often lives in document stores, key-value systems, or other scalable formats that are difficult to interrogate with complex analytical questions. Closely related is the need for network statistics on rich graphs, meaning analytic techniques that work on graph-structured data where nodes and edges can have multiple attributes, uncertainty, temporal behavior, and layered relationships. This supports the common ISR challenge of understanding relationships among people, groups, locations, events, communications, and resources, not just as simple link charts but as evolving, data-driven networks.

Another key area is conventional analysis of open source data, including complex event recognition. This targets the exploitation of publicly available information (such as reports, social media, news, and other open sources) and the ability to detect higher-level events that unfold over time and across multiple indicators. The BAA also highlights transform and opportunistic learning from open source, which implies techniques that can adapt quickly when new data sources appear, when the environment changes, or when labeled training data is scarce. The idea is to move beyond static models and toward approaches that can learn from whatever data is available, handle domain shifts, and still produce outputs that are usable in expeditionary operations.

The opportunity also calls for actionable empirical models of factional dynamics, pointing to the need for models that can represent and forecast how groups form, split, align, compete, and change behavior. Importantly, the wording emphasizes actionable and empirical, suggesting ONR is interested in models grounded in observable data that can produce decisions or recommendations, not purely theoretical constructs. Related to this is Level 1 and Level 2 fusion applied to complex graphs. In the standard fusion framing, Level 1 often relates to object/entity refinement (identifying, tracking, and characterizing entities), while Level 2 relates to situation assessment (understanding what is happening in context). Applying these fusion levels to complex graphs indicates a desire to merge multi-source data into coherent entity representations and situational interpretations within graph-based representations, where uncertainty, provenance, and relationships matter.

Several focus areas address prediction quality and the conditions under which predictions should be trusted. One is calculations of cost functions for predictions from embedded data, which suggests methods for quantifying tradeoffs, risks, and operational costs associated with prediction errors, especially when predictions are derived from embedded representations (for example, vector embeddings of entities, text, or graphs). Another is complex event detection conditioned by entity pedigree, which is essentially about incorporating provenance, lineage, and credibility of entities and sources when deciding whether an event has occurred. In expeditionary settings, the pedigree of an entity or report (who provided it, how it was generated, how reliable it has been historically, and how it relates to other sources) can be as important as the content itself, so the program is signaling interest in event detection that explicitly accounts for that.

The BAA also includes knowledge-based workflow management, pointing to tools that can guide analysts or automated systems through complex ISR workflows using explicit representations of knowledge, rules, constraints, and objectives. This is aimed at making analysis more repeatable, explainable, and efficient, especially when teams rotate, conditions change, or multiple echelons need to collaborate. Finally, Civil Information Exploitation is listed as a development area, which generally refers to collecting and analyzing civil domain information (governance, infrastructure, public sentiment, services, economics, humanitarian factors, and other civil considerations) to support operational planning and decision-making. In expeditionary contexts, civil information can be crucial to understanding the operating environment and anticipating second-order effects.

Administratively, the opportunity was posted June 19, 2015, with dates in late August and early September 2015 noted for closing, alongside a reference to the BAA for whitepaper due dates, implying a two-step or gated process where short concept papers or whitepapers may be reviewed before inviting full proposals. Eligibility is described as unrestricted, meaning it is generally open to a wide range of applicant types, subject to details in the formal eligibility section of the announcement. For access issues or questions, the listed point of contact is Justin Zerbato, Senior Contract Specialist, with a phone number provided, and there is an additional information link directing applicants to the ONR BAA website for the full text, instructions, and any required formats or submission procedures.

Overall, this ONR BAA is best understood as a targeted R&D call for capabilities that improve how expeditionary ISR systems and analysts query non-traditional data stores, analyze and fuse graph-centric and open-source information, recognize complex events with provenance-aware logic, model factional and civil dynamics, and manage analytic workflows in a way that produces timely, operationally relevant outputs under realistic field constraints.

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