Association of Target Features and Attributes

Producing SIAP requires correct processing at a number of steps. In the ideal scenario, each sensor in the network produces pure tracks. Then, each sensor reports its local tracks and measurements to other sensor platforms, where SIAP will be achieved. To avoid redundant tracks, the distributed network-level processors conduct measurement-to-track associations. A number of challenges may prevent SIAP from occurring. For example, if the environment contains closely spaced targets or is rich in false alarms, producing pure sensor-level tracks may be difficult. Measurement-to-track association at the network level is also quite challenging under such circumstances. Reliable methods are available for fusing kinematic track states and covariances, but little research has been done on the subject of fusing track and measurement features such as RCS. The work proposed here attempts to address these concerns. First, we will investigate frequency-diverse waveforms that will prove beneficial in feature-aided tracking in a decentralized, distributed sensor environment. Furthermore, we will investigate the feasibility of using lower frequency radars to extract RCS measurements that are beneficial. As part of this effort, we will be developing a RCS estimator. Finally, we will demonstrate the feasibility of our approach in the IAMD Benchmark.