Real Time Enhanced Fine Tracking in Directed Energy Applications

Current tracking capabilities in DE utilize 2D imagery. Low flying targets against cluttered backgrounds (e.g., drones) present a challenging tracking environment for track algorithms that leverage 2D information alone. A current tracking algorithm might use something like a Fitts correlation tracker [2], which correlates features in the scene with a known template of the target. This requires target identification followed by reference to a look-up table to determine the proper template to use with the correlation tracker, which can be difficult with 2D imagery, as a 2D image of the target will only provide a projection of the target image along the optical axis. 3D coherent imaging with digital holography [3,4] provides a solution for the problems associated with 2D imaging that are described here. Emerging coherent 3D imaging modalities provide complex-valued images (referred to as 2.5D images) with amplitude corresponding to a conventional 2D image and phase corresponding to the range to the target. The range images themselves (which describe the range to the target for each transverse pixel) are highly robust to scintillation and speckle [5,6]. Going further, novel methods in image restoration that include a combination of physics-based [7-11] and machine-learning [10,11] models can provide effective and computationally efficient ways to remove the anisoplanatic distortions in the image. Note that alternative 3D imaging methods do exist (e.g., Geiger mode 3D imaging with avalanche photodiodes [5]). The Phase I effort should include an analysis of existing methods before selecting one to explore for the given application. The topic proposed here is nominally focused on the fine track portion of the DE kill chain, though the technology could be used for acquisition/coarse tracking as well. We briefly summarize the key features of fine tracking in DE, which performers should keep in mind when designing systems: For fine tracking, the atmosphere and aero-optic disturbances impose phase aberrations that can severely degrade imagery. Importantly, tracking at this stage influences aimpoint maintenance, which requires fine track loops to run at much higher framerates (> 1 kHz) to keep up with the changing atmosphere while employing narrower fields of view (fractions of a degree). 3D imaging provides a path forward for enhanced pose estimation and aimpoint maintenance in the context of fine tracking. With regards to illumination for fine tracking, active coherent illumination can result in non-uniform illumination patterns on target, due to scintillation, and in speckle noise in received target imagery. Partially coherent (or multiple coherent) illuminators can reduce the effects of these two noise terms, but at the cost of the ability to perform coherent imaging and/or additional illuminators. On a similar note, coherent imaging techniques (such as digital holography and phase retrieval) can allow for digital image correction of anisoplanatic phase errors using physics-based [7-11] and machine-learning-based [10,11] image reconstruction algorithms. Note that active illumination, in general, enables tracking technology that can perform in day or night. In general, performers should be aware of the general requirements for fine tracking and should tailor designs to the technology best suited for the application. This topic seeks the development and testing of a computationally efficient processing pipeline that meets the requirements for real-time integration into a directed energy system. The goals are to design (Phase I) and demonstrate (Phase II) the processing pipeline for a 3D imaging system that can operate effectively over long standoff ranges through aerooptics/turbulence with extended, non-cooperative targets against cluttered backgrounds. The Phase I effort will design the 3D measurement technique, corresponding algorithms, and a concept for the integrated hardware. This should include analysis of the latency and throughput of the algorithms and processing hardware for real-time, closed-loop aimpoint maintenance. Then, the Phase II effort will implement the approach in a prototype demonstration system, which would deploy such algorithms onto high-speed processors and integrate with hardware that allows for real-time closed-loop correction of the line-of-sight. Such a demonstration could take place with a scaled-laboratory optical system. Alternatively, an outdoor demonstration would be appropriate as well. Key products include: 1) algorithms and software to improve 3D imagery and maintain an aimpoint by taking advantage of the 3D data, and 2) demonstrated prototype processor with real-time test results.To meet the objective, potential tasks in several research directions are described below. These tasks are open ended, and comprehensive results are not expected before Phase III.Basic research in 3D imaging1.Determine system-level requirements for a real-time 3D imaging and tracking system2.Explore the available motion-compensated 3D imaging techniques and select one for the prototype system after a pros/cons analysis3.Develop an empirical and/or theoretical signal-to-noise ratio model of the 3D imaging systemin questionApplied research in 3D tracking and aimpoint maintenance1.Mature and develop existing 3D tracking algorithms to work with 3D data from a vast array of imaging modalities2.Determine what benefits arise from tracking algorithms that make use of coherent 3D images (and 2.5D images as well), in specific3.Develop software architecture that is capable of real-time trackingHardware development (Phase II and beyond)1.Leverage system-level requirements (see Task 1) to build a prototype illuminator system2.Leverage software development (see Task 2) to integrate real-time tracking capabilities3.Demonstrate hardware in laboratory or outdoor testing environment