Stationary movers (wind turbines, etc.) adversely affect the interference rejection, detection and tracking processes of airborne radars. This impact is more severe for surveillance of difficult targets (stealth aircraft, slow moving drones, and surface targets). Wind farms are comprised of multiple wind turbines with rotating blades reaching to heights of 500ft. These turbine blades have large RCS and occupy portions of the spatial domain and Doppler spectrum used by E2C and other Navy systems to detect/track critical targets. The UD/UD team proposes to design, evaluate and demonstrate an adjunct Wind Farm Airborne Radar Processor (WARP) for the mitigation of wind farm clutter. The innovation arises in US/UDs proposal for rule based control of training data selection in filtering and false alarm control (in the rejection of clutter). This control will span across multiple channels and Doppler filters. Another proposed innovation is the application of dynamic logic (DL) principles to the hybrid clutter canceller (HCC) described in U.S. Patent #5,061,934 [5]. The HCC will also incorporate a Knowledge Aided Extended (Discrete Time) Kalman Filter KA-EDTKF. The KA-EDTKF will be used for parameter estimation in the WARP for wind turbine backscatter prediction and cancellation with no discernable impact on target visibility.
Benefit: Anticipated
Benefits: The Wind Farm Airborne Radar Processor will greatly improve surveillance performance in regions surrounding the wind farms without negatively impacting performance in other regions. Performance in joint urban/littoral/wind farm regions will also be improved. Application to other Navy radars, radars from other services and FAA radars will be addressed.
Keywords: Kalman Filtering, Kalman Filtering, Dynamic Logic, interference rejection, airborne radar, trading data selection, wind turbines, detection, tracking
