Abstract: There is provided a method of starting a gas turbine engine, comprising: selecting a baseline starting procedure of the gas turbine engine based on environmental data; receiving condition information for the gas turbine engine and/or for a starting system of the gas turbine engine, the condition information comprising at least one of: health information for the gas turbine engine and/or the starting system; maintenance information for the gas turbine engine and/or the starting system; and operation information relating to an expected future usage of the gas turbine engine. The method further comprises determining a predicted degradation profile for the gas turbine engine and/or the starting system based on the condition information; determining a starting procedure of the gas turbine engine by adapting the baseline starting procedure based on the predicted degradation profile; and controlling the gas turbine engine and/or the starting system according to the determined starting procedure.

Abstract: A target estimator that properly conditions measurement variates in the case of a series of sensor measurements collected against a target, a system model that captures visible and hidden stochastic information including but not limited to target state, target identity, and sensor measurements and that marginalizes measurement failure and a dynamic mixed quadrature expression facilitating real-time implementation of the estimator are presented.

Abstract: A target estimator that properly conditions measurement variates in the case of a series of sensor measurements collected against a target, a system model that captures visible and hidden stochastic information including but not limited to target state, target identity, and sensor measurements and that marginalizes measurement failure and a dynamic mixed quadrature expression facilitating real-time implementation of the estimator are presented.

Abstract: A method for target identification includes receiving, at one or more processors, a number of measurements of a target, each measurement from the number of measurements being observed at a predetermined time (zk), a number of target types (T), each one of the number of measurements, each one the number of target type and each one of one or more hidden states, each hidden state (xk) being characterized at the predetermined time, being correlated to one another, providing, using the one or more processors, a first conditional probability distribution, a conditional probability of a target type given a number of measurements, defined inductively, and obtaining an estimate of the target type from the first conditional probability. Systems that implement the method are also disclosed.

Abstract: A method for target identification includes receiving, at one or more processors, a number of measurements of a target, each measurement from the number of measurements being observed at a predetermined time (zk), a number of target types (T), each one of the number of measurements, each one the number of target type and each one of one or more hidden states, each hidden state (xk) being characterized at the predetermined time, being correlated to one another, providing, using the one or more processors, a first conditional probability distribution, a conditional probability of a target type given a number of measurements, defined inductively, and obtaining an estimate of the target type from the first conditional probability. Systems that implement the method are also disclosed.