DYNAMIC DATA DRIVEN FRAMEWORK TO FUSE OPTICAL AND PASSIVE RADAR DATA FOR ENHANCED SPACE SITUATIONAL AWARENESS
Abstract
Fundamental research aspects associated with tracking of space debris through passive radar as well as development of a dynamic, data driven composable framework consisting of next generation Space Situational Awareness (SSA) tools for non-parametric modeling, data association, maneuver detection, tracking and sensor tasking is proposed in this research. Riding on the folk theorem that a unique combination of data association, uncertainty characterization and sensor tasking methods do not exist, the proposed research pursues fundamental questions on each of these elements, while maintaining the modular interoperability and supporting a dynamic feedback structure to improve SSA. By furthering our understanding of the passive radar based “opportunistic” sensing for detection and tracking of small space debris and by fusing passive radar data with conventional optical sensors, unprecedented gains in the tracking and characterization of Resident Space Objects (RSOs) are anticipated. Analogous investigations to refine forward dynamics and sensor models through non-parametric models, quantify errors associated with model outputs, realize advanced data association and state estimation algorithms that readily utilize poorly characterized sensor measurements by leveraging effective and efficient methods of uncertainty propagation are proposed for enhanced space surveillance. Advanced sensor tasking methods that maximize the collective information of the sensor network ensure that the proposed algorithms are geared towards utilizing the best modalities for sensor observation to minimize the uncertainties of RSOs that pose a higher threat of colliding with other space objects. Analytical, computational and experimental methods of the proposed research are planned to mature the algorithms, models and methods of the data driven framework to ultimately find their way into the next generation space surveillance systems.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2021
- Source ID
- FA95502010176
Entities
People
- Puneet Singla
Organizations
- Air Force Office of Scientific Research
- Pennsylvania State University
- United States Air Force