Lockheed Martin Corporation

Lockhead Martin Corp.

Jeffrey Bruce Boka

Naresh Raman Patel

Purusottam Mookerjee

Jonathan Alan Boardman

Peter J Mavroudakis

Thu-Van T Luu

Christian E Pedersen

Michael J Harcourt

Joseph T Corso

Koeunyi Bae

Walter R Cottee

Frederick U Diaz

Peter N Bishop

Allen W Block

Joseph P Tadduni

Carl J Bauer

A system can include an inertial measurement unit, a magnetometer configured to measure a magnetic field, and a processing system having an ORSE filter. The processing system can be configured to determine an estimated position based on a change in velocity or angular rate based on data received from the inertial measurement unit, determine a difference between the measured magnetic field from the magnetometer and the expected magnetic field measurement, and determine a state estimate using the ORSE filter by updating a time propagation of state and covariance with a measurement update of the state and covariance. The processing system can be configured to transform a change in velocity, a change in angulate rate, and the measured magnetic field to an Earth-centered inertial reference frame for the ORSE filter.

Terrestrial land air and sea navigation system

A defensive interceptor missile is provided for defending a target against a radar-homing attack missile. A Missile Anti-Ship Kill Enhancement System (MASKES) comprises a defensive missile with digital RF memory device for (a) receiving radar signals from an attack missile, (b) processing received attack missile signals, and (c) transmitting amplified, Doppler shifted signals toward the attack missile such that the attack missile would interpret signals as being reflected off ship and target the source of the reflective signal, the defensive interceptor missile.

System and method for defense against radar homing missiles

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 tracking and engaging a boosting airborne object comprises a plurality of sensors, a first vehicle, and a second vehicle. The plurality of sensors are configured to detect and track the boosting airborne object. The first vehicle is configured to launch and guide an intercept object based on data it receives from the plurality of sensors. A second vehicle is configured to receive a cue from the first vehicle to acquire track of the airborne object and then provide track data to the first vehicle even after burnout, thereby enabling the first vehicle to provide midcourse guidance to the intercept object. The first vehicle may be positioned farther from a launch point of the airborne object than the second vehicle, thereby providing the first vehicle an increased engagement time and the second vehicle a better position to acquire track of the boosting airborne object.

System and method for early intercept ballistic missile defense

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

A method, Kinematic Algorithm for Rocket Motor Apperception (KARMA), for processing radar returns for identifying the type of a missile target includes generating tracks representing the missile, and applying the tracks to a set of plural template-based filters, each representing one missile hypothesis, to generate plural sets of missile states, one set for each hypothesis. The missile states are processed to generate kinematic parameter likelihood values (LLHs). The LLH values for each filter hypothesis are normalized and weighted. A weighted maximum likelihood value (WMLH) is calculated for each hypothesis. The correct hypothesis is deemed to be the one having the maximum WMLH, thus identifying the missile type.

Kinematic algorithm for rocket motor apperception

A system senses the presence of a boosting missile or target and processes the information by comparison of the data with a plurality of predetermined templates of nominal missile characteristics, in order to determine the state of the missile. The processing includes estimation of burnout time and of early thrust termination. Both are determined by generating current stage state estimates including position, velocity, time index error into the thrust template, and motor scale factor error. The change in motor scale factor is compared with a threshold to determine if early thrust termination has occurred. The estimated burnout time of the current stage is calculated from the burnout times of the current and the previous stage processed with the estimated motor scale factor and with state estimates from a filter.

Burnout time estimation and early thrust termination determination for a boosting target

A method and a system for sensing a boosting target missile, estimate position and velocity and boost acceleration parameters of the target missile, and control an interceptor missile to the target missile. A boost-phase missile target state estimator estimates at least acceleration, velocity, and position using an acceleration template for the target vehicle. The nominal template is incorporated into an extended Kalman filter which corrects the nominal template acceleration with the filter states to predict future thrust acceleration, velocity and position. The correction can compensate for motor burn variations and missile energy management (lofted/depressed trajectory).

Template updated boost algorithm

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 for guiding a multistage interceptor missile toward a target missile that may transition from a boost mode to a ballistic mode during the engagement. The method comprises the steps of tracking the position of the target missile with a sensor, generating a predicted intercept point and time, and loading the predicted intercept point and time into the interceptor missile guidance system. The interceptor missile is launched and transition is made to the second stage of propulsion of the interceptor missile. Second-stage midcourse guidance acceleration commands are generated in response to an elevated predicted intercept point generated using the Runge-Kutta integration method working on predicted target missile position, velocity, and acceleration. During third stage propulsion said interceptor missile is guided toward an updated predicted intercept point of the target missile. During fourth stage, the kinetic warhead effects a hit-to-kill intercept of the target missile.

Generalized midcourse missile guidance

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 method for determining or compensating for the positional errors of a sensor tracking a target comprises the steps of operating the sensor to generate sensed information relating to the target and adding any sensor positional bias update information to produce updated sensed information. The target state is propagated to produce time updated state estimates. The Jacobian of the state dynamics and the state transition matrix for the extended Kalman filter algorithm are computed. The covariance of a state vector is time propagated using the state transition matrix.

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Lumberton, NJ, United States of America

Jeffrey Bruce Boka