Lockheed Martin Corporation

Purusottam Mookerjee

Frank J Reifler

Jeffrey Bruce Boka

Naresh Raman Patel

Thu-Van T Luu

Michael J Harcourt

Jonathan Alan Boardman

Benjamin C Montone

A guidance system configured for providing control information to a guided object moving the guided object from an initial state to a target final state within a finite time interval. A controller receives information of the desired state and information representative of the guided object's current state including position and velocity. The controller includes a processor configured to calculate a control solution based on four variables relating to the present state, and the target state, wherein the processor is configured to apply coefficient weights to each of the four state variables and wherein a common coefficient weight is applied to the current state of position and the target state of position, and different coefficient weights are applied to each of the current state and target state of velocity.

System and method for control and guidance of an object

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

A state estimation system utilizing an Interacting Multiple Model (IMM) architecture and algorithms that can transition between different regimes of operation described by multiple models. Each individual model includes states, whose dynamics can be modeled, as well as extrinsic parameters, such as inputs to the system and sensor biases, whose dynamics cannot be modeled, but which are constrained to lie within a known range of values.

Rapid determination of model transitions for interacting models with bounded parameters

Information filters provide state estimation of a system when measurements provided by sensors are incomplete in that the states of the system are unobservable until a large number of measurements are collected. An example is the operation of multiple, asynchronous, one- or two-dimensional sensors to track an object in three-dimensional space. The disclosure describes an information filter that minimizes mean square estimation errors and provides accurate covariances for systems driven by unknown arbitrary time-varying inputs with unspecified dynamics but known bounds.

Information based optimal reduced state estimator for poorly conditioned estimation problems

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 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

The invention, called 'ORSE Track Fusion', combines sensor tracks from dispersed sites, when limited communication bandwidth does not permit sharing of individual measurements. Since estimation errors due to maneuver biases are not independent for each sensor, optimal fusion of tracks produced by Kalman filters requires transmission of all the filter gain matrices used to update each sensor track prior to the fusion time. For this reason, prior art has resorted to suboptimal designs. ORSE Track Fusion according to aspects of the invention overcomes this disadvantage by propagating, transmitting, and fusing separately calculated covariance matrices for random and bias estimation errors. Furthermore, with ORSE, each sensor can have its own criteria in forming its track, and track fusion can be performed with different criteria at each processing site. Thus, ORSE Track Fusion has the unique flexibility to optimize track fusion simultaneously for multiple criteria to serve multiple users.

Track fusion by optimal reduced state estimation in multi-sensor environment with limited-bandwidth communication path

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.

Method for compensating for the positional errors of a sensor

This invention relates to state estimation after processing time-delayed measurements with unknown biases that may vary arbitrarily in time within known physical bounds. These biased measurements are obtained from systems characterized by state variables and by multidimensional parameters, for which the latter are also unknown and may vary arbitrarily in time within known physical bounds. If a measurement is time-late, apply the measurements to an out-of-sequence filter using a mean square optimization criterion that accounts for all sources of uncertainty and delay time, to produce estimates of the true states of the system. If the measurement is not time-late, apply the measurements to an in-sequence filter using a mean square optimization criterion that accounts for all sources of uncertainty to produce estimates of the true states of the system. The estimates are applied to one of (a) making a decision, (b) operating a control system, and (c) controlling a process.

Reduced state estimation with biased and out-of-sequence measurements from multiple sensors

This invention relates to sate estimation after processing measurements with unknown biases that may vary arbitrarily in time within known physical bounds. These biased measurements are obtained from systems characterized by state variables and by multidimensional parameters, for which the latter are also known and may vary arbitrarily in time within known physical bounds. The measurements are processed by a filter using a mean square optimization criterion that accounts for random and biased measurement errors, as well as parameters excursions, to produce estimates of the true states of the system. The estimates are applied to one of (a) making a decision, (b) operating a control system, and (c) controlling a process.

Reduced state estimation with biased measurements

State estimation of a system having multidimensional parameters, which are unknown, arbitrarily time-varying, but bounded, in addition to state variables, is performed by initializing the state estimate and matrices representing its covariance and bias coefficients which linearly relate initial state estimation errors to the parameter errors. System matrices &#x3a6;, &#x393;, F, G and the mean valueof unknown, time-varying, but bounded parameters &#x3bb; are determined. A matrix &#x39b; is generated, representing their physical bounds. The state estimate {circumflex over (x)}(k|k) and matrices M(k|k) and D(k|k), characterizing the effects of measurement errors and parameter uncertainty, are extrapolated to generate {circumflex over (x)}(k+1|k), M(k+1|k), and D(k+1|k). The measurement noise covariance N is determined. The filter gain matrix K is calculated. The state estimate is updated with the filter gain matrix K weighting the measurement z(k+1) and the extrapolated state estimate {circumflex over (x)}(k+1|k) to generate the current system estimate {circumflex over (x)}(k+1|k+1), by minimizing its total mean square error due to measurement errors and parameter uncertainty. The matrices M(k+1|k) and D(k+1|k) are updated with the filter gain matrix K to generate M(k+1|k+1) and D(k+1|k+1).

Reduced state estimator for systems with physically bounded parameters

This invention relates to state estimation after processing measurements with time delays from multiple sensors of systems characterized by state variables and by multidimensional parameters, for which the latter are unknown and may vary arbitrarily in time within known physical bounds. If a measurement is time-late, apply the measurement to an out-of-sequence filter that uses a mean square optimization criterion that accounts for measurement errors and said bounding values, as well as the delay time, to optimally produce estimates of the true states of the system. If the measurement is not time-late, apply the measurements to an in-sequence filter that uses a mean square optimization criterion that accounts for measurement errors and said bounding values, to produce estimates of the true states of the system. The estimates are applied to one of (a) making a decision relating to the system, (b) operating a control system, and (c) controlling a process.

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Bridgewater, NJ, United States of America

Purusottam Mookerjee