Lockheed Martin Corporation

General Electric Company

Naresh Raman Patel

Jeffrey Bruce Boka

Jonathan Alan Boardman

Purusottam Mookerjee

Peter J Mavroudakis

John B Stetson, Jr

Christian E Pedersen

Randall Deen Morris

Koeunyi Bae

Joseph P Tadduni

Carl J Bauer

Jeffrey B Boka

David S Shaw

Peter N Bishop

Kevin D Kim

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.

Correlation/estimation reporting engagement system and method

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.

Passive range estimating engagement system and method

An object of interest in a cloud of objects is identified by RF and IR sensing. The RF and IR signals are separately discriminated to determine the probability that the RF tracked object is one of a predetermined number of possible object types, and the IR tracked object is one of the possible object types. Joint probabilities are calculated for all pairs of RF and IR signals and all objects, and the joint probabilities are normalized. Marginal probabilities of the joint RF/IR discrimination results are calculated to produce a vector set of marginal optical probabilities. The vector set is normalized over all object types to thereby produce a vector set of normalized marginal optical probabilities. The object of interest is selected to be the IR object of said vector set of normalized joint optical probabilities with the highest probability of being the object type of interest.

Explicit probabilistic target object selection and engagement

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.

Inertial boost thrust vector control interceptor guidance

Current targeting approaches involve guiding to a spatially derived guidepoint of a group of objects likely to be the preferred object. This method may not allow the intercepting missile to contain the preferred, or other probable object(s), within its divert capability. The guidepoint is shifted closer to the preferred object using specific energy and angular momentum, constants of orbital motion, which describe properties of an object's trajectory. Guiding to the specific energy derived guidepoint does not offer significant benefit over guiding to the spatially derived guidance point. However, computing the spatial rate of change of specific energy within the plane formed by the guidance objects establishes a vector pointing close to the preferred object. This is the direction to shift the guidepoint in order to contain the preferred object within the interceptor's divert capability.

Method for targeting a preferred object within a group of decoys

A method of selecting a sub-set of a plurality of available sensors to guide an interceptor to a target is described. The method includes characterizing a quality of position estimate received from each of the plurality of available sensors, projecting the positioning errors of the sensors onto a plane normal to a line-of-sight of the interceptor, and selecting the sub-set of the plurality of available sensors based on the projection of positioning errors.

Geometry and sensitivity weighted dynamic multi-sensor selection system and method

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. The covariance of a state vector comprising target states and sensor positional and angular biases is propagated, producing measurement updated state covariance including (i) target position and velocity measurement covariance updates and (ii) the sensor positional and angular bias measurement covariance updates.

Unified navigation and inertial target tracking estimation system

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.

Boost phase intercept missile fire control system architecture

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.

Instantaneous multisensor angular bias autoregistration

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. These steps are repeated, producing unbiased vehicle parameter estimates.

Computerized method for generating low-bias estimates of position of a vehicle from sensor data

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.

Missile identification and tracking system and method

An arrangement for coordinating positional and angle information made on separate relatively moving platforms, such as aircraft, having independent coordinate systems, uses measurements of a common reference made on both platforms. The measurements are transmitted to a common location. Measurements made at a first time are processed to determine two of three coordinate transformation angles. After a period of time, a second set of measurements is used to determine the third coordinate transformation angle. In a particular embodiment of the invention, the direction of motion of one of the platforms is controlled to be orthogonal to a coordinate axis of the other platform. When the coordinate transformation is determined, it can be used to coordinate or align navigation instruments, weapons, or the like. In one embodiment of the invention, a missile is directed toward a target, in a situation in which the target is viewed from the missile and another moving vehicle.

Remote alignment system

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}.

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Bellmawr, NJ, United States of America

Naresh Raman Patel