Integrated Planning and Execution for Autonomous Spacecraft

An autonomous spacecraft must balance long-term and short-term considerations. It must perform purposeful activities that ensure long-term science and engineering goals are achieved and ensure that it maintains positive resource margins. This requires planning in advance to avoid a series of shortsighted decisions that can lead to failure. However, it must also respond in a timely fashion to a somewhat dynamic and unpredictable environment. Thus, in terms of high-level, goal-oriented activity, spacecraft plans must often be modified due to fortuitous events such as early completion of observations and setbacks such as failure to acquire a guidestar for a science observation. This describes an integrated planning and execution architecture that supports continuous modification and updating of a current working plan in light of a changing operating context.     

Coordinated Continual Planning Methods for Cooperating Rovers

Eight evaluation metrics are used to compare and contrast three coordination schemes for a system that continuously plans to control collections of rovers (or spacecraft) using collective mission goals instead of goals or command sequences for each spacecraft. These schemes use a central coordinator to either: 1) micromanage rovers one activity at a time; 2) assign mission goals to rovers; or 3) arbitrate mission goal auctions among rovers. A self-commanding collection of rovers would autonomously coordinate itself to satisfy high-level science and engineering goals in a changing partially understood environment - making the operation of tens or even a hundred spacecraft feasible     

Clutter and jammer multipath cancellation in airborne adaptiveradar

Airborne surveillance radars must detect and localize targets in diverse interference environments consisting of ground clutter, conventional jamming, and terrain scattered jammer multipath. Multidimensional adaptive filtering techniques have been proposed to adaptively cancel this interference. However, a detailed analysis that includes the effects of multipath nonstationarity has been elusive. This work addresses the nonstationary nature of the jammer multipath and its impact on clutter cancellation and target localization. It is shown that the weight updating needed to track this interference will also modulate sidelobe signals. At the very least, this complicates the localization of targets. At the worst, it also greatly complicates the rejection of clutter. Several techniques for improving cancellation of jammer multipath and clutter are proposed, including 1) weight vector interpolation, extrapolation, and updating; 2) filter architecture, constraint, and beamspace selection; 3) prefilters; 4) 3-D STAP architectures; and 5) multidimensional sidelobe target editing     

Improved adaptive clutter cancellation through data-adaptivetraining

Adaptive array algorithms based on sample matrix inversion (SMI) require the availability of a secondary data set to “train” the adaptive filter. Numerous data-independent rules have been proposed for selecting this training data. However, such rules often perform poorly in inhomogeneous environments. We present data-adaptive methodologies for selecting the training data. The techniques, called “Power Selected Training” and “Power Selected Deemphasis”, use measurements of the interference environment to select training data. This work describes these algorithms and their performance on recorded radar data