Stottler Henke

Globally Optimized Scheduling for Space Object Tracking

Tracking of objects in earth orbit is an extremely important task for maintaining the safety and viability of manned and unmanned spacecraft. The sensors used to track these objects are mechanical and phased array radar and optical telescopes. Unfortunately, there are not enough resources to easily make enough observations to track every object’s orbit to enough accuracy. This problem is exacerbated by the current space object tracking sensor scheduling process. A scheduler at each sensor site determines which opportunities and when within each opportunity to make observations (i.e. it schedules), without reference to what other sensors are observing the same object (and when) and which orbit metrics will contain the most and least errors. The schedule is not globally optimized, since scheduling occurs only locally without reference to what is assigned or scheduled at other sensor sites. By considering the effects of other observations of the same object by other sensors, specific sensors at specific times can be chosen that provide complementary observations of the object, which will reduce the object’s orbit metric error covariance. We developed a scheduling algorithm that takes as input the space catalog and the associated covariance matrices and produces a globally optimized schedule for each sensor site as to what objects to observe and when. This algorithm is able to schedule observations that are more complementary, in terms of the precision with which each orbit metric is known, in order to produce a satellite observation schedule that, when executed, minimizes the covariances across the entire space object catalog. The algorithm has been prototyped and tested with the deep space objects in the space catalog, and the resulting orbit metric covariances calculated. Significant reductions in covariances were observed and are presented here.

Rapid Scheduling of Multi-tracking Sensors for a Responsive Satellite Surveillance Network

Satellites currently play a critical role in military operations, providing functions such as targeting, surveillance, communications and navigation. The Satellite Surveillance Network (SSN) is a small network of geographically distributed sites that provides tracking for all orbiting objects, and maintaining a Space Catalog of their orbits. The SSN currently tracks over 16,000 objects. For each object there is an associated priority (1 through 5) and a requisite number of tracks to obtain per day. Most SSN sites consist of sensors that can either track 1 object at a time (such as mechanically tracked parabolic dish radars) or that have a fixed field of view and can track many objects at a time such as phased array radars. But there are also interesting future sensor systems that can track multiple objects at a time and have a movable field of view. Scheduling of the observations of these types of assets is the emphasis of this paper. Examples include the Space Surveillance Telescope (SST) developed by DARPA and MIT/LL which is wide angle searching and tracking telescope and airborne and space-based phased array radars. The key scheduling task is to determine where the sensor should be aimed (i.e., what volume of the sky should be viewed) over time in order to ensure that they can obtain as many of the tracks with which they are tasked as possible, and that all the highest priority tracks are obtained. This is a difficult optimization problem in a continuous three-dimensional space (the coordinates are the azimuth and elevation at which to aim the sensor, and the time at which it should be aimed there). The objective is to maximize the number of targets visible at any given time so as to satisfy as many track requests as possible, while conforming to the requirements concerning priority, number of tracks and number of observations per track. We describe a new approach to this scheduling task that employs a novel data-driven approach to discretizing the search space. This approach results in rapid search of a tractable search space containing high-quality options that maximize the number of tracks that can be scheduled. Our approach defines techniques for clustering objects whose visibility sets intersect. These intersections are discrete subsets

Incorporating High-Speed, Optimizing Scheduling into NASA's EUROPA Planning Architecture

This paper describes an effort investigating improvements possible to NASA’s EUROPA planning system development toolset through the addition of high-speed, high-quality scheduling algorithms. We determined that such additions were beneficial, feasible, and could be readily taken advantage of by planning system developers. We designed two different integration mechanisms (re-implementation of scheduling algorithms within EUROPA and interfacing an existing c++ library of scheduling algorithms to EUROPA), determined that there were advantages to each approach. We prototyped the reimplementation integration option and showed its benefits with two prototypes ‐ one directed toward International Space Station (ISS) Extravehicular Activity (EVA) planning (that showed significantly better results in less time than EUROPA operating alone) and one directed toward automatic Score ISS crew scheduling that was able to quickly, automatically schedule with realistic data while obeying a large number of hard and soft constraints. (No automatic Score ISS scheduling capability currently exists.) Finally, we added the scheduling-enhanced EUROPA prototype to an independently-developed EUROPA application, which allowed it to finish planning and find an optimal schedule in under 4 minutes instead of not returning at all after 80 minutes.

General Scheduling Service using Intelligent Resource Management Techniques

This paper describes a general, automated scheduling solution to handle resource assignment and scheduling challenges for a variety of space mission applications, and how this solution is packaged as a general scheduling service provided in a Service Oriented Architecture (SOA). The full design for the system, the various software methods that are part of that design, and a recently completed prototype are discussed. The techniques described here have been successfully applied to a variety of space mission application and full-scale, operational development of the general scheduling service is proceeding. I. Problem Description Managing real-time space mission and space situational related activities is a complex task, especially when considering all of the resources directly and indirectly involved. Space missions and related activities include space-based observations such as commercial overhead imagery, communication link scheduling (both for health monitoring and commanding and for communication users of communication satellites), ground based observations of space objects, launch support, orbit maneuver support, reacting to space weather events and notifications, and military operations. Space-related resources include the space systems themselves such as satellites and their components, and terrestrial systems such as ground stations, and communication links. The resources and activities to be scheduled are satellite payloads and busses, maneuvering the satellite, modifying the communications, processing management, sensor and data collection management, and satellite communication network scheduling. Therefore, there are several resource assignment and optimization issues associated with space missions, requiring a large number of both separate and integrated scheduling and resource optimization applications. An automated system is clearly called for to handle both the complexity and time constraints. Actions resulting from the responses to simultaneous events may interfere with each other or there may be an opportunity for synergy. Additionally, new actions to be scheduled may interfere with ongoing and already scheduled operations. At a minimum, the new activities are going to require resources. For example, part of the response to a space weather event may be to divert activities currently scheduled on satellites with space weather issues to other assets, but these ground and space-based resources will have other, competing demands and it is important to optimally allocate these assets across all the demands for them. These demands (often called requests) fall into a number of seemingly disparate categories that nonetheless can be handled similarly. One category involves activities scheduled on the satellite experiencing space weather issues. Actions such as closing shutters, changing operating modes and maneuvering will affect the ability of the satellite to perform its mission, i.e. its current set of tasks. Those tasks may need to be re-allocated to another satellite. The constraints on which other satellites can assume these tasks will be based on timing and line-of-sight (LOS) issues (orbital mechanics). Another category is tasking earth-based sensors (optical or radar) to focus on specific space objects or to search a particular volume of space. Which resources can perform these tasks and when will also be based on timing and LOS issues involving orbital mechanics. Another category is the tasking of spacebased sensors to focus on specific objects or areas on the earth, such as to fulfill requests for commercial satellite imagery. Which resources can perform these tasks and when will, again, be based on orbital mechanics. Therefore

Integrating Phased Array Path Planning with Intelligent Satellite Scheduling

As the demand for satellite-driven communication increases in both the commercial and military sectors, so do the numbers of active satellite constellations and the parallel requirements for ground-based support capacity. Phased array antennae have been identified as a cost-effective hardware solution for increasing communications capacity at ground stations, due to their ability to support multiple contacts simultaneously and their design compatibility with cost-effective commercial components. However, with increased communications capacity comes added complexity for the task of scheduling satellite supports in a network of satellites and ground stations with multi-beam phased array antennae. This task breaks down into two inter-related goals. First, the network-level challenge remains to allocate contacts to specific local sites. This is already complex in the case where ground stations exclusively use traditional mechanically steered reflector antennae, as schedulers seek to optimize resource usage within the confines of satellite visibilities and equipment availability at different sites. Second, with the introduction of phased array antennae, there is an additional local-level challenge to calculate active areas and paths on the surface of the phased array, to determine whether a candidate allocation with multiple contacts can actually be supported. These are inter-related because the local path planning analysis is predicated on an allocation developed at the network-level, whereas the network-level reasoning is most effective if it can be informed by knowledge of incompatibilities manifested at the local level. This paper describes an Artificial Intelligence based approach for handling these mutual dependencies efficiently while generating nearly optimal solutions.