Modern military engagements require detecting and monitoring the movement of people and resources, both military and civilian, in real time over a wide area with little direct human supervision. This problem poses a number of challenges to conventional sensing and data processing technologies. In the context of such behavior, it has become increasingly difficult to automatically detect such movements, since the relevant patterns may exist on many disparate levels. In some situations, combinations of geographical movement of personnel, vehicles and objects may need to be analyzed simultaneously. Currently this is a very human-intensive operation, typically requiring numerous individuals.
Active monitoring of human and non-human movement involves the detection and classification of spatio-temporal patterns across a large number of real-time data streams. Approaches that analyze data in a central computing facility tend to be overwhelmed with the amount of data that needs to be transferred and processed in a timely fashion. Also, centralized processing raises proprietary and privacy concerns that may make many data sources inaccessible.
The detection and classification of subtle changes in an activity requires the integration of a wide variety of non-specific real-time data sources into the operation of the tracking system. Furthermore, to guarantee the early detection of a suspicious or critical movement, the system must operate on real-time data that is continuously updated. As a result, there is an immense amount of data that needs to be processed in a timely fashion.
Our co-pending U.S. patent application Ser. No. 10/352,288, entitled “Swarming Agents For Distributed Pattern Detection and Classification,” incorporated herein by reference, describes a swarming agent architecture for the distributed detection and classification of spatio-temporal patterns in a heterogeneous real-time data stream. The invention is not limited to geographic structures or patterns in Euclidean space, and is more generically applicable to non-Euclidean patterns such as topological relations in abstract graph structures. According to this disclosure, large populations of simple mobile agents are deployed in a physically distributed network of processing nodes. At each such node, a service agent enables the agents to share information indirectly through a shared, application-independent runtime environment. The indirect information sharing permits the agents to coordinate their activities across entire populations. The architecture may be adapted to the detection of various spatio-temporal patterns and new classification schemes may be introduced at any time through new agent populations. The system is scalable in space and complexity due to the consequent localization of processing and interactions. The system and method inherently protect potentially proprietary or private data through simple provable local processes that execute at or near the actual source of the data. The fine-grained agents, which swarm in a large-scale physically distributed network of processing nodes, may be designed to perform three major tasks: 1) they may use local sensors to acquire data and guide its transmission; 2) they may fuse, interpolate, and interpret data from heterogeneous sources, and 3) they may make or influence command and control decisions. In contrast to previous approaches involved with command and control, or data acquisition and transmission, such an approach facilitates a swarming agent architecture for distributed pattern-detection and classification.