Optimizing Track-to-Track Data Fusion for Variable Cases

In current tracking and data fusion applications, each mission, each scenario, each different configuration of sensors and measurement types typically results in a different tracking algorithm or configuration that produces the optimal performance results. However, the ability to quickly and easily evaluate these different approaches to allow for a detailed analysis does not exist today. As part of this effort, Black River Systems Company proposes to develop a model-based tracking architecture that is able to capture the key functional components of a tracker and allow for an objective determination of which tracker configuration and algorithmic components produce the optimal results. Our approach will break down the tracking problem into separate functional components and provide a software tool that is capable of selecting different algorithmic solutions for each of the functional blocks. In conjunction with this, we will also create scenarios that will be used to evaluate multiple combinations of tracker functionality. The final piece of our proposed solution is the development of tracking metrics that allow for an objective comparison of the performance of the various algorithmic components.