Kari Sentz

The foundational principles of warfare have always required a combatant to "know the enemy and know yourself", and to consider the likelihood that methods of deception will be employed on both sides. Further, the need to estimate adversarial situations, threats, and intentions imputes a need for forecasting these states to future times to be relevant to decision-making. These combined requirements lead to a very challenging context for the design, development, and evaluation of multisensor information fusion processes and systems. This paper reviews the nature of these requirements, points out that there is a wide range of related research across varied communities on these topics, and that there is relatively little work in the situation management and information fusion communities currently addressing these challenges. This paper is a call for new and more directly applicable research involving multidisciplinary efforts to advance Level 3 situation and threat estimation capabilities with human and sensor-based fusion systems that is essential for comprehensive situational awareness and assessment in adversarial environments.

Knowledge Acquisition, Representation, Processing, & Presentation (KARPP) is a new system level approach to the acquisition and aggregation of mission-valued and qualified situation knowledge for use by a decision maker. KARPP is decomposed into a service oriented architecture (SOA) of knowledge aggregation services (KAS) with well-defined inputs and outputs which are implemented in hidden processes. The transfer of knowledge from one KAS to another is through a qualified knowledge request (QKR) which results in an aggregated knowledge response (AKR). The notion of qualified knowledge is introduced to specify the knowledge that is needed by a requesting KAS as well as the knowledge quality attributes and the magnitude of those qualities which are required to meet mission needs. The goals of this approach are to provide an architectural framework for realizing managed, adaptive fusion processing capabilities, and to integrate the sensor/database control and data acquisition with the fusion-based data processing which will maximize the expected knowledge value rate (EKVR) provided to mission managers.

Situation management involves the acquisition and dissemination of knowledge to effect situation awareness and assessment. In a companion paper, the concept of Knowledge Acquisition, Representation, Processing, & Presentation (KARPP) system has been introduced. In that paper, it was proposed to manage an adaptive data fusion process including the decomposition of a sensor and database knowledge acquisition and aggregation system into a service oriented architecture (SOA) of knowledge aggregation services (KAS) with standards-based inputs and outputs which are implemented in hidden processes. This paper reviews current approaches to SOA and expands the KARPP architecture into a taxonomy of required and support services as well as discusses particulars of a self-organizing implementation of KARPP in a dynamic SOA of microservices. In addition to a taxonomy of services, a self-documenting approach is introduced to allow for preplanned product improvement (PPI) and incremental development as well as the concept of a Knowledge Aggregation System Service broker.

The research described in this paper is addressing the problem of designing a comprehensive knowledge ontology of information quality combining ontology of quality characteristics and ontology of models for computing their values and incorporating the designed Knowledge Ontology in the architecture of information fusion systems. A use case illustrating the utility of the designed ontology in an earthquake scenario is also presented.

We propose a tensor train based data structure to accelerate the calculation of Dempster-Shafer operations such as belief and Dempster's rule of combination. This approach relies on the fact that the matrix representation of these operators possess rank-1 tensor network decompositions, allowing for far more efficient calculations in tensor train format. Numerical experiments demonstrate the superior performance of the proposed method in computing Dempster-Shafer quantities.

Dempster-Shafer Theory (DST) is a mathematical framework to handle imprecision and uncertainty in reasoning and decision making. One assumption of DST is that of a closed-world, or the assumption that all propositions are known a priori. In this work, we explore an alternative formulation of Dempster-Shafer that allows for the dynamic inclusion of new propositions. Specifically, we expand the framework to include the complement of every set of propositions. This adjustment enables an open-world interpretation that can support unspecified and dynamic propositions as we learn about the problem space. Including complementary sets distinguishes this from previous work in DST where the open world is attributed to the empty set. We demonstrate our open world Dempster-Shafer Theory on a variety of synthetic and real datasets.

Environmental monitoring applications often require 24/7 operation in harsh, low resource (e.g. power and communication) environments over a large scale area with ad-hoc deployment of sensors. Data processing at the sensor is required to minimize communication overhead. Such an application scenario presents opportunities for research in wireless sensor networks (WSN)s that are distinct from existing commercial off-the-shelf (COTS) solutions. We present a novel modular, highly flexible, hardware solution with a core feature of a System on a Chip (SoC) with add-ons such as memories, interfaces, and different transmission input/output I/O modalities. The system can manage, process, and transmit data directly within an ad-hoc self healing, self forming, mesh network over long distance (19 km between nodes in the current implementation) or as a stand-alone system. Hardware has been produced and the system has been validated in real-world deployments.

Land cover classification uses multispectral pixel information to separate image regions into categories. Image segmentation seeks to separate image regions into objects and features based on spectral and spatial image properties. However, making sense of complex imagery typically requires identifying image regions that are often a heterogeneous mixture of categories and features that constitute functional semantic units such as industrial, residential, or commercial areas. This requires leveraging both spectral classification and spatial feature extraction synergistically to synthesize such complex but meaningful image units. We present an efficient graphical model for extracting such semantically cohesive regions. We employ an initial hierarchical segmentation of images into features represented as nodes of an attributed graph that represents feature properties as well as their adjacency relations with other features. This provides a framework to group spectrally and structurally diverse features, which are nevertheless semantically cohesive, based on user-driven identifications of features and their contextual relationships in the graph. We propose an efficient method to construct, store, and search an augmented graph that captures nonadjacent vicinity relationships of features. This graph can be used to query for semantic notional units consisting of ontologically diverse features by constraining it to specific query node types and their indicated/desired spatial interaction characteristics. User interaction with, and labeling of, initially segmented and categorized image feature graph can then be used to learn feature (node) and regional (subgraph) ontologies as constraints, and to identify other similar semantic units as connected components of the constraint-pruned augmented graph of a query image.