Abstract: A system and method for performing correlation processing for identifying an object of interest in a cloud of remote objects of different types comprises receiving RF and IR measurement data including rotational, translational bias and noise errors and determining and removing biases by minimizing a weighted distance metric. Correlation pairing metrics are then determined according to a maximum likelihood criteria. This method produces a correlation solution that minimizes the effects of biases and random noise while accounting for the statistical properties of the parameters being estimated.

Abstract: A system and method for determining a 3-dimensional target position and velocity using 2-dimensional IR sensor angular measurements for objects travelling in a ballistic manner within an IR sensor's field of view (FOV). Resulting 3-dimensional states may be used to generate updated inputs for correlation and object selection to drive missile guidance and intercept operations.

Abstract: A method for engaging a target missile includes sensing the position of the target and of an interceptor missile, and determining time-to-go to intercept and direction of thrust of the interceptor. A one-step intercept solution is determined based on position estimates of the target and the interceptor and is used to iteratively estimate at least two components of a three-dimensional unit thrust vector, and apply updated guidance commands to the interceptor. A system for thrust vector control of an interceptor against a target missile includes a processor for receiving sensed target signals, determining a one-step initial solution to produce time-to-go and current direction of thrust of the interceptor, iteratively estimating at least two components of a three-dimensional unit thrust vector, and producing a guidance vector for application to the interceptor.

Abstract: A target tracking method uses sensor(s) producing target signals subject to positional and/or angular bias, which are updated with sensor bias estimates to produce updated target-representative signals. Time propagation produces time-updated target states and sensor positional and angular biases. The Jacobian of the state dynamics of a target model produces the state transition matrix for extended Kalman filtering. Target state vector and bias covariances of the sensor are time propagated. The Kalman measurement residual is computed to produce state corrections, which are added to the time updated filter states to thereby produce (i) target state updates and (ii) sensor positional and angular bias updates.

Abstract: A fire control system for a boost phase threat missile includes sensors for generating target-missile representative signals, and a multi-hypothesis track filter, which estimates the states of various target hypotheses. The estimated states are typed to generate hypotheses and their likelihoods. The states, hypotheses and likelihoods are applied to a multihypothesis track filter, and the resulting propagated states are applied to an engagement planner, together with the hypotheses and likelihoods. The engagement planner initializes the interceptor(s). Interceptor guidance uses the initialization and the propagated states and typing information to command the interceptor.

Abstract: Unknown alignment biases of sensors of a tracking system are estimated by an iterative Kalman filter method. Current measurements are corrected for known alignment errors and previously estimated alignment biases. The filter time reference is updated to produce estimated target state derivative vectors. A Jacobian of the state dynamics equation is determined, which provides for observability into the sensor alignment bias through gravitational and coriolis forces. The target state transition matrix and the target error covariance matrix are propagated. When a new measurement becomes available, the Kalman gain matrix is determined, the state vector and covariance measurements are updated, and sensor alignment biases are estimated. The state vector, covariance measurements, and estimated sensor alignment biases are transformed to an estimated stable space frame for use in tracking the target and updating the next iteration.

Abstract: A computerized method generates unbiased estimates of parameters such as position, velocity, and acceleration of a vehicle. The method assigns initial vehicle states. Unbiased vehicle state estimates are obtained using a model incorporating a unique parameterization of the vehicle motion and state dynamics, and sensor registration bias states. Sensor measurements of the vehicle are used to iteratively update the vehicle states and covariances. Previous vehicle state and covariance, are propagated to produce current state and covariance estimates. If needed, a new vehicle model is adopted. Sensor registration biases are estimated to generate corrected measurement information. A measurement matrix and a measurement residual is determined. The Kalman gain matrix is determined, and used with measurement residual for state update. A new current state is generated. State covariance update is determined the same way, and used to generate a new current state covariance.

Abstract: Sensors determine the kinematic measurements of a boosting missile, and the information is applied to a plurality of pairs or sets of filters, one of which is matched to the characteristics of a particular target type, and the other of which is general, and not matched to a particular target, for producing from each filter of the set missile position, velocity, acceleration, and specific mass flow rate states, and covariances of those states. From the filtered information, the estimates are made of at least missile mass flow rate, thrust, velocity at stage burnout, and remaining burn time. The likelihood is computed that the states and covariances from the filter sets represent the same target. The largest likelihood is selected as representing the target. In one mode, the estimated parameters are weighted and summed for a composite likelihood.

Abstract: A unified spacecraft attitude control system includes a memory aboard the spacecraft, in which a linear transformation matrix [.alpha.] is stored, which includes information identifying pseudo-complementary pairs of thrusters, and the characteristics of each pseudo-complementary pair. During each control cycle of the spacecraft attitude control system, the error signal is multiplied by a gain representing a desired slew rate to form pulse-width signals {pw} for the pseudo-complementary paired thrusters. An augmented pulse-width vector matrix {PW} is formed by transformations, to eliminate negative values of pulse width. The actual thruster pulse widths {.DELTA.t} are calculated as {.DELTA.t}=[.alpha.]{PW}. The thrusters are energized by limited values of {.DELTA.t}.