Advanced Multisensor Fused Track and Discrimination Architecture

This effort will extend the Phase I design, development, implementation, testing and performance evaluation of an automated prototype Multisensor Attributes and Contextual Information-Aided Track Correlation Algorithm. The proposed algorithm integrates the following modules based modified/enhanced existing stand-alone algorithms: a) A basic Multisensor Data Collection and Feature Extraction Module, b) A Data and Feature Fusion Engine that combines data and features from multiple sensors and automatically provides object dynamic motion histories, c) a Features-to-Attribute conversion module that automatically takes the data from the Data and Feature Fusion Engine and converts Features into Attributes, d) a Contextual Information Capture Algorithm based on, and e) an Enhanced Object Hypothesis Testing Module based on a new Cost Function Criteria for a joint Metric-Attributes-Contextual Information Track Correlator. The addition of contextual information is expected to enhance the algorithm performance in cases when: 1) the Features-to-Attributes process may be degraded due to insufficient data collection and/or poor feature extraction results, 2) two or more hand-over sensors have different numbers of objects in their field of view. The automated prototype algorithm will undergo Design-to-Capability testing at the MDA/DVH AMP facility and, as an option, it would be tested in one of the BMDS Fusion Testbed "Sidecar" processors.