| Sol No.: |
Navy SBIR FY2016.1 |
| Topic No.: |
N161-004 |
| Topic Title: |
Maritime Traffic Model Aided Tracking |
| Proposal No.: |
N161-004-0586 |
| Firm: |
Toyon Research Corp.
6800 Cortona Drive
Goleta, California 93117 |
| Contact: |
Robert Wilkerson |
| Phone: |
(805) 968-6787 |
| Web Site: |
http://www.toyon.com |
| Abstract: |
Littoral waterways impose a challenging surveillance requirement for naval forces tasked with protecting its surface assets. To achieve a high-level of situational awareness, it is important to persistently track and characterize the various entities that may pose a threat. However, dense surface traffic results in more frequent data association ambiguity which leads to more tracking errors. Toyon shall research and develop enhanced kinematic prediction techniques to improve persistent tracking performance in littoral waterways. Toyon shall develop an algorithm for estimating maritime traffic flow in the littoral region. The algorithm shall estimate the parameters of the maritime traffic flow model using a variety of a priori and real time data, including observed target trajectories derived from collected surveillance data. To support evaluation of the prediction algorithm, Toyon shall integrate the traffic flow model with its Tracked Object Management (TOM) framework for improving persistent signature-aided tracking of small boats. Toyon proposes to team with Raytheon Company on this effort. Toyon will work with radar experts at Raytheon to produce a high-fidelity simulation model of the AN/APY-10 radar system within SLAMEMr. The simulation model will be used for proof-of-concept demonstration of the enhanced TOM algorithm. |
| Benefits: |
The anticipated results of this research are algorithms and software that improve autonomous tracking of surface targets in the littoral domain using fielded radar systems such as AN/APY-10. The target prediction algorithms developed during the program will reduce the error statistics in predicted target state probability density functions (PDFs) thereby reducing the overall kinematic errors in tracks produced by any tracking system that includes the prediction technology. |