초록 ▼

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 targe

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.

대표청구항 ▼

What is claimed is: 1. A method for compensating for the positional errors of a sensor tracking a target with known acceleration, said method comprising the steps of: operating said sensor to generate sensed information relating to the target; adding to said sensed information any sensor positional

What is claimed is: 1. A method for compensating for the positional errors of a sensor tracking a target with known acceleration, said method comprising the steps of: operating said sensor to generate sensed information relating to the target; adding to said sensed information any sensor positional bias update information to produce updated sensed information which ultimately provides improved target state information; propagating the target state to produce time updated state estimates comprising the target position and velocity and the positional bias of the sensor; computing the Jacobian of the state dynamics vector of the target; computing, from said Jacobian, the state transition matrix for the extended Kalman filter algorithm; and using said state transition matrix, time propagating the covariance of a state vector comprising the position and velocity states of said target and the positional bias of the sensor. 2. A method according to claim 1, wherein said step of propagating the state of said updated sensed information to produce time updated state estimates of the target position further comprises the step of: generating a state derivative vector; and numerically integrating the state derivative vector. 3. A method according to claim 1, wherein said step of computing the Jacobian of the state dynamics of the target further comprises the step of: taking partial derivatives of the state dynamics vector with respect to the states which provide observability of the sensor positional bias error through gravitation and coriolis forces. 4. A method according to claim 1, wherein said step of time propagating the covariance of the state vector of said target comprises the step of: generating a state transition matrix using the Jacobian matrix to time propagate the covariance of the state vector, where said state vector comprises the position and velocity states of said target and the position bias of said sensor. 5. A method according to claim 1, further comprising, after said step of time propagating the covariance of a state vector comprising the position and velocity states of said target and the position bias of said sensor, the step of determining if target position information is available, and if target position is available, performing the further steps of: calculating gain of the Kalman filter to generate Kalman filter gain. 6. A method according to claim 5, wherein said step of calculating gain of the Kalman filter to generate Kalman filter gain comprises the step of calculating K=P(ti)HT[HP( ti)HT+R]-1 where: H=[I3×3 03×3 I3×3] is the measurement matrix; and R is the measurement noise covariance matrix associated with the currently reporting sensor. 7. A method according to claim 5, further comprising, after said step of calculating gain of the Kalman filter to generate Kalman filter gain, the steps of: generating updates of the states of said target and of the covariance of said states of said target. 8. A method according to claim 1, further comprising, after said step of time propagating the covariance of a state vector comprising the position and velocity states of said target, the step of determining if target position and target velocity information are available, and if target position and target velocity are available, performing the further steps of: calculating gain of the Kalman filter to generate Kalman filter gain in accordance with K=P(ti)HT[HP( ti)HT+R]-1 where: is the measurement matrix; and R is the measurement noise covariance matrix associated with the currently reporting sensor. 9. A method for compensating for the positional errors of a sensor tracking a target with known acceleration, said method comprising the steps of: defining the estimator state given by comprising the target position X and velocity X and the positional bias δX of the sensor; operating said sensor to generate sensed information Z relating to the target according to Z=L+X where L is the location of the reporting sensor; adding to said sensed information any sensor positional bias update information Δ{tilde over (X)}=Δ{circumflex over (X)}+δ{circumflex over (X)}, where Δ{circumflex over (X)} is the position bias estimate from the previous cycle, Δ{tilde over (X)} is the position bias estimate from the current cycle, and δ{circumflex over (X)} is the position bias error estimate, where the tilde ˜ represents the update and the caret ^ represents an estimate, to produce updated sensed information which ultimately provides improved target state estimates; propagating from the previous time ti-1 to the current time ti where the subscript i refers to the filter cycle iteration to produce time updated state estimates according to computing the Jacobian of the state dynamics of the target according to and the [[·]] notation denoting a skew symmetric matrix of the vector argument; computing, from said Jacobian, the state transition matrix for the extended Kalman filter algorithm Φ≈I+JΔt+0.5J2Δt 2; and using said state transition matrix, time propagating the covariance of a state vector comprising the position and velocity states of said target and the positional bias of the sensor given by P(ti)=ΦP(ti-1 )ΦT+Q, where Q is the state noise matrix.