Abstract: Space-based ground-imaging lidar has become increasingly feasible with recent advances in compact fiber lasers and single-photon-sensitive Geiger-mode detector arrays. A challenge with such a system is imperfect pointing knowledge caused by angular jitter, exacerbated by long distances between the satellite and the ground. Unless mitigated, angular jitter blurs the 3D lidar data. Unfortunately, using mechanical isolation, advanced IMUs, star trackers, or auxiliary passive optical sensors to reduce the error in pointing knowledge increases size, weight, power, and/or cost. Here, the 2-axis jitter time series is estimated from the lidar data. Simultaneously, a single-surface model of the ground is estimated as nuisance parameters. Expectation Maximization separates signal and background detections, while maximizing the joint posterior probability density of the jitter and surface states.

Abstract: Space-based ground-imaging lidar has become increasingly feasible with recent advances in compact fiber lasers and single-photon-sensitive Geiger-mode detector arrays. A challenge with such a system is imperfect pointing knowledge caused by angular jitter, exacerbated by long distances between the satellite and the ground. Unless mitigated, angular jitter blurs the 3D lidar data. Unfortunately, using mechanical isolation, advanced IMUs, star trackers, or auxiliary passive optical sensors to reduce the error in pointing knowledge increases size, weight, power, and/or cost. Here, the 2-axis jitter time series is estimated from the lidar data. Simultaneously, a single-surface model of the ground is estimated as nuisance parameters. Expectation Maximization separates signal and background detections, while maximizing the joint posterior probability density of the jitter and surface states.

Abstract: The technology described herein includes a system and/or a method for identifying an object. The technology includes determining an alpha parameter that is associated with a fusion function. The technology includes determining a beta parameter that is associated with a degree of expected independence of a received set of data and the received set of data including information associated with a classification of the object. The technology includes fusing the received set of data based on the alpha parameter and the beta parameter. The technology includes generating a probability of identification of the classification of the object based on the fused data.

Abstract: A method and system for of fusing the outputs of multiple sensors. The output of each sensor including a sensor class probability vector based on each sensor's classification database is input to a fusion engine which estimates a base class probability vector based on the sensor class probabilities output by the sensors and a preconfigured base database including base classes.

Abstract: A method and system for of fusing the outputs of multiple sensors. The output of each sensor including a sensor class probability vector based on each sensor's classification database is input to a fusion engine which estimates a base class probability vector based on the sensor class probabilities output by the sensors and a preconfigured base database including base classes.