초록 ▼

A multi-stage maximum likelihood target estimator for use with radar and sonar systems is provided. The estimator is a software implemented algorithm having four computational stages. The first stage provides angle smoothing for data endpoints thereby reducing angle errors associated with tie-down t

A multi-stage maximum likelihood target estimator for use with radar and sonar systems is provided. The estimator is a software implemented algorithm having four computational stages. The first stage provides angle smoothing for data endpoints thereby reducing angle errors associated with tie-down times. The second stage performs a coarse grid search to obtain the initial approximate target state to be used as a starting point for stages 3 and 4. The third stage is an endpoint Gauss-Newton type maximum likelihood target estimate which determines target range along two time lines. The final refinement of the target state is obtained by the fourth stage which is a Cartesian coordinate maximum likelihood target estimate. The four-stage processing allows the use of target historic data while reducing processing time and computation power requirement.

대표청구항 ▼

What is claimed is: 1. A multi-stage target estimation method for underwater and airborne targets, said method comprising the steps of: receiving data inputs from a target tracker; smoothing angle measurements at the endpoints of the data inputs; searching a coarse grid in endpoint coordinates to p

What is claimed is: 1. A multi-stage target estimation method for underwater and airborne targets, said method comprising the steps of: receiving data inputs from a target tracker; smoothing angle measurements at the endpoints of the data inputs; searching a coarse grid in endpoint coordinates to produce an initial target state estimate at two time lines, the time line 1 and the time line 2; performing a Gauss-Newton maximum endpoint coordinate likelihood estimate at the two time lines; calculating a Cartesian coordinate maximum likelihood estimate subsequent to said step of performing a Gauss-Newton maximum endpoint coordinate likelihood estimate; and outputting a target state to a display computer. 2. A multi-stage target estimation method as in claim 1 wherein said step of coarse grid searching comprises the steps of: selecting three frequency measurements from a target sensor and assigning, as a base frequency, a most recent frequency measurement; setting a minimum and maximum range at the time line 1 with respect to a sensor associated with the time line 1; setting a minimum and maximum range at the time line 2 with respect to a sensor associated with the time line 2; computing three values of range at time line 1 with respect to the sensor associated with the time line 1; computing three values of range at time line 2 with respect to the sensor associated with the time line 2; processing frequency data by computing five base frequency estimates; computing an endpoint coordinate performance index for each combination of the computed values of range and the computed base frequency estimates; and selecting the initial target state estimate of the computed values of range and the computed base frequency estimate associated with a smallest performance index. 3. A multi-stage target estimation method as in claim 2 wherein said step of performing a Gauss-Newton endpoint coordinate maximum likelihood estimation comprises the steps of: initializing values for the range at the time line 1 with respect to the sensor associated with the time line 1 using the estimates from said step of course grid searching; initializing values for the range at the time line 2 with respect to the sensor associated with the time line 2 using the estimates from said step of course grid searching; initializing the base frequency state using the computed base frequency estimate from said step of course grid searching; and performing Gauss-Newton iterations until an endpoint coordinate maximum likelihood is estimated. 4. A multi-stage target estimation method as in claim 3 wherein said step of performing Gauss-type iterations comprises the steps of: computing endpoint parameters at the time of measurement of the time lines 1 and 2; computing a matrix of partial derivatives; performing a Householder transformation on the matrix; extracting an upper matrix from the Householder transformed matrix; setting a Y vector to a last column of the Householder transformed matrix; computing a gain vector; and updating the states in which the states represent the endpoint coordinate maximum likelihood estimates at the two time lines. 5. A multi-stage target estimation method as in claim 4 wherein said step of calculating a Cartesian coordinate maximum likelihood estimate comprises the steps of: initializing values for an x-coordinate of target position from the Gauss-Newton endpoint coordinate maximum likelihood estimate; initializing values for a y-coordinate of target position from the Gauss-Newton endpoint coordinate maximum likelihood estimate; initializing values for an x-coordinate of target velocity from the Gauss-Newton endpoint coordinate maximum likelihood estimate; initializing values for a y-coordinate of target velocity from the Gauss-Newton endpoint coordinate maximum likelihood estimate; and performing Gauss-Newton iterations until the Cartesian coordinate maximum likelihood is estimated. 6. A multi-stage target estimation method as in claim 5 wherein said step of performing Gauss-type iterations for Cartesian coordinate maximum likelihood comprises the steps of: computing Cartesian endpoint parameters at the time of measurement of the time lines 1 and 2; computing an assigned matrix of partial derivatives; performing a Householder transformation on the assigned matrix; extracting an upper matrix from the assigned Householder transformed matrix; setting an assigned Y vector to a last column of the assigned Householder transformed matrix; computing an assigned gain vector; and updating the states in which the states represent the Cartesian coordinate maximum likelihood estimates at the two time lines at current time. 7. A multi-stage target estimation method as in claim 6 wherein said step of outputting a target state to a display computer comprises the steps of: computing a Cartesian coordinate covariance matrix; extrapolating the covariance matrix to current time; computing target range; computing target bearing; computing target course; computing target speed; computing base frequency standard deviations; computing major and minor localization ellipse axis length and orientation of major axis from North; and outputting as the target state. 8. A multi-stage target estimation method as in claim 7 wherein said step of coarse grid searching further comprises constraining a target to lie on azimuthal bearing lines. 9. A multi-stage target estimation method as in claim 7 wherein said step of coarse grid searching further comprises constraining a target to lie on conical angle hyperbolas. 10. A multi-stage target estimation method as in claim 7 wherein said step of coarse grid searching further comprises constraining a target to lie on conical angle hyperbolic asymptotes.