A system and method to perform multisensory data fusion in a distributed sensor environment for object identification classification. Embodiments of the invention are sensor-agnostic and capable handling a large number of sensors of different types via a gateway which transmits sensor measurements to a fusion engine according to predefined rules. A relation exploiter allows combining sensor measurements with information on object relationships from a knowledge base. Also included in the knowledge base is a travel model for objects, along with a graph generator to enable forecasting of object locations for further correlation of sensor data in object identification. Multiple task managers allow multiple fusion tasks to be performed in parallel for flexibility and scalability of the system.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A data fusion system for identifying, an object of interest, the data from multiple data sources, the system comprising: a gateway, for receiving sensor measurements from a sensors set; a knowledge base stored in a non-transitory data storage, the knowledge base for storing information about plurality of objects and relationships there-between; a relation exploiter, for extracting one or more of the objects from the knowledge base, responsive to their relationship to the object of interest; a fusion engine, for receiving the sensor measurements from the gateway, the fusion engine comprising: an orchestrator module, for combining at least two of the sensor measurements, responsive to the relationships of the one or more objects to the object of interest and; at least one task manager, for receiving a fusion task from the orchestrator module, for creating a fusion task data structure from the at least two combined sensor measurements, and for managing the fusion task data structure to identify the object of interest; and a Bayesian fusion unit for performing the fusion task for the at least one task manager. 2. The data fusion system of claim 1, wherein the at least one task manager is a plurality of task managers. 3. The data fusion system of claim 1, wherein the knowledge base further contains a travel model of at least one of the plurality of objects. 4. The data fusion system of claim 3, further comprising a graph generator, for generating a graphical representation of the potential locations of the at least one object according to the travel model. 5. The data fusion system of claim 1, wherein the relation exploiter extracts one or more identifiers for the one or more objects from the knowledge base related to the object of interest. 6. A computer implemented data fusion method for identifying an object of interest, the data from multiple data sources, the method comprising: receiving sensor measurements from a sensors set; extracting one or more objects related to the object of interest from a knowledge base, the knowledge base comprising information about plurality of objects and relationships there-between; managing at least one fusion task, responsive to the relationships of the one or more objects to the object of interest, the fusion task comprising fusing at least two of the sensor measurements into a data structure therefrom; and using the data structure to identify the object of interest; wherein at least one of fusion tasks comprising Bayesian fusion. 7. The method of claim 6, wherein the knowledge base further contains a travel model of at least one of the plurality of objects. 8. The method of claim 7, further comprising generating a graphical representation of the potential locations of the at least one object according to the travel model. 9. The method of claim 6, further comprising extracting one or more identifiers for the one or more objects from the knowledge base related to the object of interest. 10. A non-transitory computer readable medium (CRM) that, when loaded into a memory of a computing device and executed by at least one processor of the computing device, configured to execute the steps of a computer implemented data fusion method for identifying an object of interest, the data from multiple data sources, the method comprising: receiving sensor measurements from a sensors set; extracting one or more objects related to the object of interest from a knowledge base, the knowledge base comprising information about plurality of objects and relationships there-between; managing at least one fusion task, responsive to the relationships of the one or more objects to the object of interest, the fusion task comprising fusing at least two of the sensor measurements into a data structure therefrom; and using the data structure to identify the object of interest; wherein at least one of fusion tasks comprising Bayesian fusion.