Abstract: Apparatus, computer-readable medium, and method for optimal delivery of one or more satellite effects via reverse time heat equation isomorphism including constructing a generalized formulation of the heat equation corresponding to a commander's intent for delivery of the one or more satellite effects, performing a separation of variables to decouple a plurality of variables into a plurality of corresponding separated equations, solving the plurality of separated equations to produce candidate solutions, applying boundary conditions to each candidate solution, filtering the candidate solutions to mitigate ill-posedness of solving the inverse of the heat equation, assessing formal differences between the candidate solutions and the inverse of the heat equation, and generating an optimized schedule of the one or more satellite effects based on the assessment of the formal differences of the candidate solutions.

Abstract: Embodiments regard improved satellite position determination with age of measurement data. A method includes receiving measurement data from a satellite, in a first iteration, increasing a dimensionality of the measurement data to a specified number of dimensions resulting in N-dimensional input data, performing dynamic mode decomposition on the N-dimensional measurement data resulting in a Koopman operator and modes of the N-dimensional measurement data, adaptive filtering a time domain residue resulting in a filtered residue, and updating, based on the filtered residue and a time domain deterministic component of the N-dimensional measurement data, a state vector of an object associated with the satellite measurement data.

Abstract: Systems, devices, methods, and computer-readable media for location determination of an orbiting device. A method can include receiving measurement data from a monitor device, the measurement data representative of a position of an orbiting space object with a mechanically moveable antenna, receiving, by processor circuitry, mission data indicating orientation of the antenna is changing from a first orientation to a second, different orientation by a specified time, determining, based on the mission data, an inter-signal correction (ISC), applying the determined ISC to the measurement data resulting in corrected measurement data, providing the corrected measurement data to a Kalman filter, and receiving, from the Kalman filter, a position estimate of the orbiting space object.

Abstract: Apparatus, computer-readable medium, and method for optimal delivery of one or more satellite effects via reverse time heat equation isomorphism including constructing a generalized formulation of the heat equation corresponding to a commander's intent for delivery of the one or more satellite effects, performing a separation of variables to decouple a plurality of variables into a plurality of corresponding separated equations, solving the plurality of separated equations to produce candidate solutions, applying boundary conditions to each candidate solution, filtering the candidate solutions to mitigate ill-posedness of solving the inverse of the heat equation, assessing formal differences between the candidate solutions and the inverse of the heat equation, and generating an optimized schedule of the one or more satellite effects based on the assessment of the formal differences of the candidate solutions.

Abstract: Systems, devices, methods, and computer-readable media for improved location determination of an orbiting device. A method can include receiving, at a transceiver of a device, measurement data from a monitor device, the measurement data representative of a physical state of a mobile object, filtering, using a first of a plurality of first filters of the device, the measurement data based on a character parameter of a state transition matrix representative of the physical state resulting in filtered measurement data, filtering, using a Kalman filter, the filtered measurement data resulting in further filtered measurement data, and providing, by the transceiver, the further filtered measurement data.

Abstract: Systems, devices, methods, and computer-readable media for location determination of an orbiting device. A method can include receiving measurement data from a monitor device, the measurement data representative of a position of an orbiting space object with a mechanically moveable antenna, receiving, by processor circuitry, mission data indicating orientation of the antenna is changing from a first orientation to a second, different orientation by a specified time, determining, based on the mission data, an inter-signal correction (ISC), applying the determined ISC to the measurement data resulting in corrected measurement data, providing the corrected measurement data to a Kalman filter, and receiving, from the Kalman filter, a position estimate of the orbiting space object.

Abstract: Systems, devices, methods, and computer-readable media for improved location determination of an orbiting device. A method can include receiving, at a transceiver of a device, measurement data from a monitor device, the measurement data representative of a physical state of a mobile object, filtering, using a first of a plurality of first filters of the device, the measurement data based on a character parameter of a state transition matrix representative of the physical state resulting in filtered measurement data, filtering, using a Kalman filter, the filtered measurement data resulting in further filtered measurement data, and providing, by the transceiver, the further filtered measurement data.

Abstract: A learning automaton can be trained to merge data from input data streams, optionally with different data rates, into a single output data stream. The learning automaton can learn over time from the input data streams. The input data streams can be low-pass filtered to suppress data having frequencies greater than a time-varying cutoff frequency. Initially, the cutoff frequency can be relatively low, so that the effective data rates of the input data streams are all equal. This can ensure that initially, high data-rate data does not overwhelm low data-rate data. As the learning automaton learns, an entropy of the learning automaton changes more slowly, and the cutoff frequency is increased over time. When the entropy of the learning automaton has stabilized, the training is completed, and the cutoff frequency can be large enough to pass all the input data streams, unfiltered, to the learning automaton.

Abstract: A method for preprocessing data for device operations can include preprocessing measurement data using a machine learning technique, determining, by a Kalman filter and based on (1) the preprocessed measurement data or the measurement data and (2) prediction data from a prediction model predicting a measurement associated with the measurement data, corrected measurement data, and providing the corrected measurement data based on the predicted measurement and the preprocessed measurement data.

Abstract: A method for preprocessing data for device operations can include preprocessing measurement data using a machine learning technique, determining, by a Kalman filter and based on (1) the preprocessed measurement data or the measurement data and (2) prediction data from a prediction model predicting a measurement associated with the measurement data, corrected measurement data, and providing the corrected measurement data based on the predicted measurement and the preprocessed measurement data.

Abstract: A learning automaton can be trained to merge data from input data streams, optionally with different data rates, into a single output data stream. The learning automaton can learn over time from the input data streams. The input data streams can be low-pass filtered to suppress data having frequencies greater than a time-varying cutoff frequency. Initially, the cutoff frequency can be relatively low, so that the effective data rates of the input data streams are all equal. This can ensure that initially, high data-rate data does not overwhelm low data-rate data. As the learning automaton learns, an entropy of the learning automaton changes more slowly, and the cutoff frequency is increased over time. When the entropy of the learning automaton has stabilized, the training is completed, and the cutoff frequency can be large enough to pass all the input data streams, unfiltered, to the learning automaton.