G06N 3/02, G06N 3/0454, G06N 3/08, G06N 7/005
A noise and bias can be determined for a sensor. An input vector can be received. A parameter vector can be generated based at least in part on a feed-forward neural network. Components can be determined using the parameter vector based at least in part on a mixture model. A conditional probability density function can be generated based at least in part on the conditional probability density function.
Sun, Yu and Williams, Troi, "Learning state-dependent sensor measurement models for localization" (2020). USF Patents. 1128.
University of South Florida