Method and apparatus for tracking an aimpoint with arbitrary subimages

Claim 1

A method for tracking an aimpoint, comprising the steps of: acquiring an aimpoint on a target and a set of subimages arbitrarily associated with the aimpoint by a sensor in a field of view; for a first time, for each subimage, determining whether the subimage is one-dimensional or two-dimensional; for the first time, calculating a normalized distance from each subimage to the aimpoint; for the first time, for each one-dimensional subimage, determining a maximum eigenvector for the subimage; for the first time, for each one-dimensional subimage, calculating a component of the normalized distance associated therewith which is along the maximum eigenvector therefor; for a second time, reacquiring at least one subimage; and estimating a subsequent location of the aimpoint based on the subsequent location of the subimage and on the normalized distance therefor and, if said reacquired subimage is one-dimensional, further based upon the maximum eigenvector and normalized distance component therefor.

Claim 2

A method for tracking an aimpoint, comprising the steps of: acquiring an aimpoint on a target and a set of subimages arbitrarily associated with the aimpoint by a sensor in a field of view; for a first time, for each subimage, determining whether the subimage is one-dimensional or two-dimensional; for the first time, calculating a normalized distance from each subimage to the aimpoint; for the first time, for each one-dimensional subimage, determining a maximum eigenvector for the subimage; for the first time, for each one-dimensional subimage, calculating a component of the normalized distance associated therewith which is along the maximum eigenvector therefor; for a second time, reacquiring at least one subimage; and estimating a subsequent location of the aimpoint based on the subsequent location of the subimage and on the normalized distance therefor and, if said reacquired subimage is one-dimensional, further based upon the maximum eigenvector and normalized distance component therefor.

Claim 5

A method for tracking an aimpoint on a target comprising the steps of: selecting an aimpoint on the target; for a first time, acquiring a set of subimages of the target arbitrarily associated with the aimpoint using predetermined subimage trackability criteria, using an image sensor operable to change its displacement relative to the target; for each subimage, determining whether the subimage is one-dimensional or two-dimensional, including the substeps of generating a set of data for each subimage, each set of the data representative of the difference between the gray level data of each subimage and the gray level data of the same subimage indexed through an image space around the subimage; fitting the set of data to a paraboloid; determining maximum and minimum eigenvalues from the constants of the parabola; calculating the ratio of the maximum eigenvalue to the minimum eigenvalue; calculating the geometric relationship of each subimage to the aimpoint, taking into account whether the subimage is one-dimensional or two-dimensional; between a first time and a second time, changing the displacement of the image sensor relative to the target; reacquiring at least one of the subimages at a second time using the sensor; and calculating a location of the aimpoint at said second time using the geometric relationship between the at least one subimage and the aimpoint.

Claim 6

A method for tracking an aimpoint on a target comprising the steps of: selecting an aimpoint on the target; for a first time, acquiring a set of subimages of the target arbitrarily associated with the aimpoint using predetermined subimage trackability criteria, using an image sensor operable to change its displacement relative to the target; for each subimage, determining whether the subimage is one-dimensional or two-dimensional, including the substeps of generating a set of data for each subimage, each set of the data representative of the difference between the gray level data of each subimage and the gray level data of the same subimage indexed through an image space around the subimage; fitting the set of data to a paraboloid; determining maximum and minimum eigenvalues from the constants of the parabola; calculating the ratio of the maximum eigenvalue to the minimum eigenvalue; calculating the geometric relationship of each subimage to the aimpoint, taking into account whether the subimage is one-dimensional or two-dimensional; between a first time and a second time, changing the displacement of the image sensor relative to the target; reacquiring at least one of the subimages at a second time using the sensor; and calculating a location of the aimpoint at said second time using the geometric relationship between the at least one subimage and the aimpoint.

Claim 9

A method for rejecting subimages erroneously associated with an aimpoint, comprising the steps of: determining the locations of a set of subimages associated with an aimpoint at a first time; for each subimage, determining whether the subimage is one-dimensional or two-dimensional; estimating the location of the aimpoint at a second time based on the locations of the set of subimages at the second time and on a geometric relationship between the subimages and the aimpoint at the first time; estimating a set of locations of the subimages which should have occurred at the second time, each estimated location based on the estimated aimpoint at the second time; setting a displacement threshold; for each one-dimensional subimage, calculating a residual based on a maximum eigenvector and a difference between an estimated location of the one-dimensional subimage and a corresponding measured location therefor; for each two-dimensional subimage calculating a difference between each estimated two-dimensional subimage location and the corresponding measured location therefor; and rejecting each subimage where a predetermined function of the residual or difference thereof exceeds the displacement threshold.

Claim 10

A method for rejecting subimages erroneously associated with an aimpoint, comprising the steps of: determining the locations of a set of subimages associated with an aimpoint at a first time; for each subimage, determining whether the subimage is one-dimensional or two-dimensional; estimating the location of the aimpoint at a second time based on the locations of the set of subimages at the second time and on a geometric relationship between the subimages and the aimpoint at the first time; estimating a set of locations of the subimages which should have occurred at the second time, each estimated location based on the estimated aimpoint at the second time; setting a displacement threshold; for each one-dimensional subimage, calculating a residual based on a maximum eigenvector and a difference between an estimated location of the one-dimensional subimage and a corresponding measured location therefor; for each two-dimensional subimage calculating a difference between each estimated two-dimensional subimage location and the corresponding measured location therefor; and rejecting each subimage where a predetermined function of the residual or difference thereof exceeds the displacement threshold.

Claim 15

A method for tracking an aimpoint, comprising the steps of: acquiring an aimpoint and a set of subimages arbitrarily associated with the aimpoint at a first time; for each subimage, determining whether the subimage is one-dimensional or two-dimensional; calculating the normalized distance from each two-dimensional subimage to the aimpoint; for each one-dimensional subimage, determining a maximum eigenvector for the subimage; for each one-dimensional subimage, calculating a component of the normalized distance from the aimpoint to the one-dimensional subimage which is parallel to the maximum eigenvector therefor; at a later time reacquiring a plurality of subimages; estimating a subsequent location of the aimpoint based on the reacquired subimage and on the normalized distances, and, if a reacquired subimage is one-dimensional, based on the maximum eigenvector therefor; setting a displacement threshold; estimating a set of locations of the subimages based on the subsequent location of the aimpoint and on the normalized distance and, to the extent that any of the subimages are one-dimensional, based on the maximum eigenvectors therefor; calculating the difference between the estimated location and the reacquired location of each subimage; rejecting each reacquired subimage where a predetermined function of the difference exceeds the displacement threshold; and re-estimating the aimpoint based on the remaining reacquired subimages.

Claim 16

A method for tracking an aimpoint, comprising the steps of: acquiring an aimpoint and a set of subimages arbitrarily associated with the aimpoint at a first time; for each subimage, determining whether the subimage is one-dimensional or two-dimensional; calculating the normalized distance from each two-dimensional subimage to the aimpoint; for each one-dimensional subimage, determining a maximum eigenvector for the subimage; for each one-dimensional subimage, calculating a component of the normalized distance from the aimpoint to the one-dimensional subimage which is parallel to the maximum eigenvector therefor; at a later time reacquiring a plurality of subimages; estimating a subsequent location of the aimpoint based on the reacquired subimage and on the normalized distances, and, if a reacquired subimage is one-dimensional, based on the maximum eigenvector therefor; setting a displacement threshold; estimating a set of locations of the subimages based on the subsequent location of the aimpoint and on the normalized distance and, to the extent that any of the subimages are one-dimensional, based on the maximum eigenvectors therefor; calculating the difference between the estimated location and the reacquired location of each subimage; rejecting each reacquired subimage where a predetermined function of the difference exceeds the displacement threshold; and re-estimating the aimpoint based on the remaining reacquired subimages.

Claim 18

A method for tracking an aimpoint, comprising the steps of: acquiring an aimpoint on a target, and a set of subimages arbitrarily associated with the aimpoint, by a sensor in a field of view; for a first time and for each subimage, determining whether the subimage is one-dimensional or two-dimensional by performing the following substeps: for each of a plurality of pixels of the subimage, acquiring a gray level datum of pixel brightness; shifting the subimage by a predetermined number of pixels; for each of a plurality of pixels in the shifted subimage, acquiring a gray level datum of pixel brightness; correlating each pixel in the subimage with a corresponding pixel in the shifted subimage to create a gray level data pair; for each gray level data pair, subtracting the gray level of one pixel from the gray level of the other pixel in the pair to obtain a gray level difference; summing the gray level differences for all of the gray level data pairs between the subimage and the shifted subimage to obtain a sum of differences therefor; storing the summed differences obtained for the subimage and the shifted subimage; repeating said substeps of shifting, acquiring a gray level datum for each pixel in the shifted subimage, correlating, obtaining a gray level difference and obtaining a sum of differences, and storing for each of a plurality of shifted subimages, each as compared with the subimage, over a predetermined image space surrounding the subimage; using the stored sums of differences, finding the constants a, b and c in the following relation: C(i,j)=ai.sup.2 +2bij+cj.sup.2 +d where C(i,j) is the sum of differences corresponding to a shifted subimage centered at location (i,j) and d is a constant; from constants a, b and c, determining maximum and minimum eigenvalues for C(i,j); determining a ratio of the maximum eigenvalue to the minimum eigenvalue; finding that the subimage is one-dimensional if the ratio exceeds a predetermined constant; finding that the subimage is two-dimensional if the ratio does not exceed a predetermined constant; for the first time, for each two-dimensional subimage, determining a normalized distance from the two-dimensional subimage to the aimpoint; for the first time, for each one-dimensional subimage, determining a maximum eigenvector from the found constants a, b and c; for the first time, for each one-dimensional subimage, determining a component of the normalized distance from the one-dimensional subimage to the aimpoint along the maximum eigenvector therefor; for a second time, reacquiring at least one of the subimages; and estimating a location of the aimpoint at the second time based on the location of said at least one subimage at the second time, the normalized distance of said at least one subimage to the aimpoint, and, if said at least one subimage is one-dimensional, the maximum eigenvector therefor and said component of the normalized distance along the last said maximum eigenvector.

Claim 19

A method for tracking an aimpoint, comprising the steps of: acquiring an aimpoint on a target, and a set of subimages arbitrarily associated with the aimpoint, by a sensor in a field of view; for a first time and for each subimage, determining whether the subimage is one-dimensional or two-dimensional by performing the following substeps: for each of a plurality of pixels of the subimage, acquiring a gray level datum of pixel brightness; shifting the subimage by a predetermined number of pixels; for each of a plurality of pixels in the shifted subimage, acquiring a gray level datum of pixel brightness; correlating each pixel in the subimage with a corresponding pixel in the shifted subimage to create a gray level data pair; for each gray level data pair, subtracting the gray level of one pixel from the gray level of the other pixel in the pair to obtain a gray level difference; summing the gray level differences for all of the gray level data pairs between the subimage and the shifted subimage to obtain a sum of differences therefor; storing the summed differences obtained for the subimage and the shifted subimage; repeating said substeps of shifting, acquiring a gray level datum for each pixel in the shifted subimage, correlating, obtaining a gray level difference and obtaining a sum of differences, and storing for each of a plurality of shifted subimages, each as compared with the subimage, over a predetermined image space surrounding the subimage; using the stored sums of differences, finding the constants a, b and c in the following relation: C(i,j)=ai.sup.2 +2bij+cj.sup.2 +d where C(i,j) is the sum of differences corresponding to a shifted subimage centered at location (i,j) and d is a constant; from constants a, b and c, determining maximum and minimum eigenvalues for C(i,j); determining a ratio of the maximum eigenvalue to the minimum eigenvalue; finding that the subimage is one-dimensional if the ratio exceeds a predetermined constant; finding that the subimage is two-dimensional if the ratio does not exceed a predetermined constant; for the first time, for each two-dimensional subimage, determining a normalized distance from the two-dimensional subimage to the aimpoint; for the first time, for each one-dimensional subimage, determining a maximum eigenvector from the found constants a, b and c; for the first time, for each one-dimensional subimage, determining a component of the normalized distance from the one-dimensional subimage to the aimpoint along the maximum eigenvector therefor; for a second time, reacquiring at least one of the subimages; and estimating a location of the aimpoint at the second time based on the location of said at least one subimage at the second time, the normalized distance of said at least one subimage to the aimpoint, and, if said at least one subimage is one-dimensional, the maximum eigenvector therefor and said component of the normalized distance along the last said maximum eigenvector.