Automated Target Recognition
Current acquisition, tracking, and pointing (ATP) technologies employ optical image processing and/or inertial measurement unit (IMU) data to mitigate image jitter in critical air and space borne targeting applications. Image processing is typically performed using the Kalman filter, or some variant thereof, despite the fact that these filters have known shortcomings. In general, they are ill suited for nonlinearly moving targets which is exactly what would be observed in a jittering image stream. Recent development of an enhanced image processor based on the Particle Filter Algorithm (PFA) is attractive because, among other advantages, PFAs inherently handle target nonlinearities. The enhanced PFA has been designed to improve speed, compensate jitter, and reduce detectable target SNR through the incorporation of a number of preprocessing components.
Countering Unmanned Aerial Systems (C-UAS) is an ever-increasing challenge due to the variety of threats: UAS airframes, materials, propulsion types, and control systems. Further, the control systems in particular are continually evolving which complicates detection, identification, and tracking. A robust and reliable means of completing these steps in the kill chain is required. Even significant technology advancements will not substantially change the optical signature of the airframe. Polaris Sensor Technologies thus proposes to enhance the optical signatures over that of standard sensors with polarization sensitive cameras operating from the visible and to the long-wave infrared. This offers a multi-modal, multi-spectral sensor suite which allows us to implement algorithms that take advantage of the multi-modal sensing for optimal contrast and detection, identification, and tracking performance. The C-UAS Detection and Discrimination system is a passive approach, and with the added sensing mode of polarization, will provide good performance for day, night, and ranges relevant for C-UAS.
All Weather Trackers
The High Energy Laser (HEL) Mobile Demonstrator is an effective system to counter Rockets, Artillery, and Mortars (RAM) and has the potential to address the threat of UASs. The system effectiveness is limited by the supporting acquisition and tracking systems that place the kill laser on the target. In particular, adverse weather conditions limit acquisition and tracking performance and hence overall system performance. Enhanced acquisition and tracking sensors and algorithms are required to address this limitation. Polaris Sensor Technologies will investigate a variety of sensing modalities across the infrared to improve acquisition and tracking performance in adverse weather. Atmospheric propagation models will be used and additional models developed to assess atmospheric and weather effects of representative RAM signatures. The effort will focus on optimal choice of spectral band and the potential for polarimetric sensing to improve signal to background ratios but will also include the impact of these signatures on acquisition and tracking system performance. Model results will be compared to measurements. System concepts will be developed based on the results, and the Phase II effort will build and demonstrate the optimal system.