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Our approach to onboard processing will enable a quicker return and improved quality of processed data from small, remote-sensing satellites. We describe an intelligent payload concept which processes RF lightning signal data onboard the spacecraft in a power-aware manner. Presently, onboard processing is severely curtailed due to the conventional management of limited resources and power-unaware payload designs. Delays of days to weeks are commonly experienced before raw data is received, processed into a human-usable format, and finally transmitted to the end-user. We enable this resource-critical technology of onboard processing through the concept of Algorithm Power Modulation (APM). APM is a decision process used to execute a specific software algorithm, from a suite of possible algorithms, to make the best use of the available power. The suite of software algorithms chosen for our application is intended to reduce the probability of false alarms through posbprocessing.Each algorithm however also has a cost in energy usage. A heuristic decision tree procedure is used which selects an algorithm based on the available power, time allocated, algorithm priority, and algorithm performance. We demonstrate our approach to power-aware onboard processing through a preliminary software simulation.

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As the numbers and applications of laser systems grow worldwide, the potential risks to satellite optical systems from inadvertent ground-based laser operations are a growing cause for concern. Although the hazards from United States Department of Defense laser program operations are mitigated with a formal predictive avoidance process, all satellite owners and operators are implicitly accepting risks from other domestic and foreign lasers. Yet, these risks are generally not well known in space communities. To better understand these risks, a probabilistic risk analysis methodology has been developed to quantify the risks posed to all or a select group of satellite optical payloads by a single laser or classes of laser systems. The methodology uses a Monte Carlo approach to account for factors relating to the chance that a satellite can be illuminated, the probability the optical sensor is “looking” at the laser, the uncertainties related to atmospheric effects on the beam, and the probability of exceeding the damage-onset threshold for a single pixel of a given detector material. A Poisson process models the risk as a function of the number of laser operations.