A neural extended Kalman filter algorithm was embedded in an interacting multiple model (IMM) architecture for target tracking. The neural extended Kalman filter algorithm is used to improve motion model prediction during maneuvers. With a better target motion mode, noise reduction can be achieved through a maneuver. Unlike the interacting multiple model architecture which, uses a high process noise model to hold a target through a maneuver with poor velocity and acceleration estimates, a neural extended Kalman filter is used to predict the correct velocity and acceleration states of a target through a maneuver. The neural extended Kalman filter estimates the weights of a neural network, which in turn is used to modify the state estimate predictions of the filter as measurements are processed. The neural network training is performed on-line as data is processed. In this paper, the results of a neural extended Kalman filter embedded in an interacting multiple model tracking architecture will be shown. Highly maneuvering threats are a major concern for the Navy and DoD and this technology will help address this issue.
Mark Owen is a Senior Scientific and Technical Manager, Naval Information Warfare Center (NIWC), San Diego, CA. Mark Owen was selected as the NIWC’s first Multi-Intelligence Fusion and Correlation (MIFC) Technology Lead, a Senior Scientific and Technical Manager (SSTM) position in June 2017.
In his new role, Owen leads a diverse and highly technical group in the research, development, fielding and support of advanced Multi-INT Fusion technologies across all warfighter domains including space, air, land, sea, and undersea. Owen leads and manages the Center's Multi-INT fusion, correlation, and analysis technical efforts to ensure applicability to customer requirements, including National Intelligence Community initiatives, and defense-wide priorities. He provides technical leadership for the research, development, and validation of C4ISR MIFC technologies, systems, and processes, from concept through implementation
Event Date: April 7, 2023