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 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 concurrent visualization of sensor and communications operations. A method can include receiving mission data indicating a target, a sensor, a communications system, and an operation to be performed regarding the target, identifying one or more operational layer, functional layer, and physical layer models for the sensor and communications system, and a physical layer model for weather, identifying, based on a comparison of the physical models of the sensor and communications system to a propagation equation, any gaps or inconsistencies between the physical models of the sensor and communications system and the propagation equation, and executing a simulation model resulting in a visual display of execution of the mission using the sensor and the communications system, the simulation model generated based on a filler model that fills any identified gaps or fixes any identified inconsistencies.

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 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.

Abstract: Near-optimal schedules are generated for shared resources. Multiple instances of a schedule are generated for operation of the shared resources. Each instance of the schedule is processed to produce a corresponding locally-optimized schedule instance. Evaluation criteria and selection logic are applied to each locally-optimized schedule instance to select a best schedule to meet a current circumstance represented by a global rule set. An updated global rule set, which represents a change to the current circumstance, is received, and the evaluation criteria and the selection logic are adjusted in response to the updated global rule set to produce updated evaluation criteria and updated selection logic. The updated evaluation criteria and the updated selection logic are applied to each locally-optimized schedule instance to select the best schedule to meet the current circumstance represented by the updated global rule set.

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.

Abstract: Statistical movement analysis method that includes defining one or more movement signature types, wherein a movement signature type includes a set of movement parameters and values for the set of movement parameters and receiving data for objects monitored by a radar system at a plurality of times, the data including values acquired for the set of movement parameters. The method also includes discriminating the data for the monitored objects by comparing movement signatures of two or more of the monitored objects for a difference of statistical significance and characterizing the monitored objects based on the movement signatures of the monitored objects. The method also includes identifying one or more of the monitored objects by comparing the received data for a monitored object with the movement signature type to determine if any of the objects monitored by the radar system are moving consistent with the movement signature type.

Abstract: Statistical movement analysis method that includes defining one or more movement signature types, wherein a movement signature type includes a set of movement parameters and values for the set of movement parameters and receiving data for objects monitored by a radar system at a plurality of times, the data including values acquired for the set of movement parameters. The method also includes discriminating the data for the monitored objects by comparing movement signatures of two or more of the monitored objects for a difference of statistical significance and characterizing the monitored objects based on the movement signatures of the monitored objects. The method also includes identifying one or more of the monitored objects by comparing the received data for a monitored object with the movement signature type to determine if any of the objects monitored by the radar system are moving consistent with the movement signature type.