Officials of the Space Electro-Optics Division of the Air Force Research Laboratory's Directed-Energy Directorate at Kirtland Air Force Base, N.M., issued a broad agency announcement on Friday for the Technical Applications for Optical Space Situational Awareness (TAOS) project.
TAOS seeks new components and subsystems that can improve the performance, reliability, maintainability, supportability, and affordability of ground-based remote sensing for space situational awareness.
The project also seeks to enhance the performance or reduce the costs, schedule, or risks of fielding totally new technological approaches in this area.
In addition, TAOS seeks new enabling electronic and electro-optical technologies in orbital mechanics, machine learning, and advanced atmospheric turbulence characterization and forecasting for new space object observational planning tools.
TAOS revolves around eight technical areas: beam control engineering and analysis; space object detection, orbit determination and characterization; meteorological forecasting of adaptive optic parameters (METFAP); quantum optics; space situational awareness; active sensing; astroinformatics via machine learning; and decision-making tools and processes.
The will remain active for five years, will spend about $49 million, and will involve several different contractors. All contracting will happen through future calls to solicit new research.
Beam control engineering and analysis involves optical beam control in the presence of atmospheric turbulence, and includes modeling, analysis, planning, design, and operation of advanced adaptive optical and other beam-control in laser propagation; optical imaging; target acquisition; pointing; and tracking.
Space object detection, orbit determination and characterization involves the detection, orbit determination, and characterization of space objects, and includes advanced algorithms in precision astrodynamics; imaging systems; representative atmospheric turbulence; and controllable source fields.
Meteorological forecasting of adaptive optic parameters (MetFAP) involves new ways to predict the effects of atmospheric turbulence on optical beam propagation using a whole-sky imager. This includes sun photometry with algorithms and codes for measuring sun radiance and boundary layer particles; a laser ceilometer for measuring cloud bases and column aerosols; and high-resolution weather measurement devices.
Quantum optics -- or measuring how light interacts with matter at the submicroscopic level -- involves quantum key distribution; quantum sensors; and propagation. This involves hardware, algorithms, and software for free-space quantum network applications.
Space situational awareness involves improving ground-based electro-optical telescopes for observing objects in space. This includes new algorithms for image processing, modeling, and simulating laser beacon returns; modeling and simulating system performance; designing new algorithms for control systems or data processing; advanced algorithms for daytime imaging or daytime techniques; laser and laser beacon technologies; modeling and simulating atmospheric turbulence; sensors and filters; and engineering electronic systems and architectures for improving current imaging capabilities.
Active sensing involves active laser sensors like laser detection and ranging (ladar) to characterize orbiting space objects, including precision 3-D tracking and real-time maneuver estimation.
Astroinformatics via machine learning involves using machine learning to extract useful information about an orbiting space object. This includes improved space object detection, enhanced scene simulation, imagery rating, object identification, closely spaced object detection, and sensor system control enhancement.
Decision making tools and processes involves improved data collection techniques for generating actionable space situational awareness information, including the forward and inverse projections of object models and observed imagery.