Coherent correlation addition for increasing match information in scene matching navigation systems

Claim 3

The method of claim 1, wherein said combining step comprises the step of adding coherently said correlation surfaces.

Claim 4

The method of claim 2, wherein said adding step comprises the step of superposing said correlation surfaces using data which relate said correlation surfaces to each other.

Claim 5

The method of claim 2, wherein the match point correlation amplitude for each of said correlation surfaces are coherently added.

Claim 6

The method of claim 2, wherein the anti-match point correlation amplitude for each of said correlation surfaces are coherently added.

Claim 9

An image matching method comprising the steps of: acquiring a sequence of sensed images with at least two of said sensed images being located within a reference map; correlating each of said sensed images with said reference map to generate a correlation surface for each of said sensed images comprising the steps of: comparing each of said sensed images to said reference map at every location in said reference map at which said sensed image is entirely within said reference map, and computing a correlation amplitude for each location of said sensed image in said reference map, said correlation amplitudes forming said correlation surface; combining said correlation surfaces to form a composite correlation surface; and determining a match point for said sequence of sensed images in said reference map using said composite correlation surface.

Claim 10

An image matching method comprising the steps of: acquiring a sequence of sensed images with at least two of said sensed images being located within a reference map; correlating each of said sensed images with said reference map to generate a correlation surface for each of said sensed images comprising the steps of: comparing each of said sensed images to said reference map at every location in said reference map at which said sensed image is entirely within said reference map, and computing a correlation amplitude for each location of said sensed image in said reference map, said correlation amplitudes forming said correlation surface; combining said correlation surfaces to form a composite correlation surface; and determining a match point for said sequence of sensed images in said reference map using said composite correlation surface.

Claim 11

The method of claim 6 or 7, wherein said combining step comprises the step of coherently adding said correlation surfaces.

Claim 12

The method of claim 8, wherein said adding step comprises the step of superposing said correlation surfaces using data which relate said correlation surfaces to each other.

Claim 13

The method of claim 8, wherein the match point correlation amplitude for each of said correlation surfaces are coherently added.

Claim 14

The method of claim 8, wherein the anti-match point correlation amplitude for each of said correlation surfaces are coherently added.

Claim 16

The method of claim 12, wherein said searching step further comprises the step of adjusting said upper and lower threshold values based on the standard deviation of actual correlation amplitudes excluding the extremes.

Claim 18

The method of claim 14, further comprising the step of repeating said forming and redetermining steps of claim 14 until said match point is determined or until the last acquired of said sensed images located in said reference map is correlated, whichever occurs first.

Claim 19

The method of claim 14, wherein said forming step comprises the steps of: subtracting from said composite correlation surface said correlation surface generated from the oldest acquired of said sensed images; and adding to said composite correlation surface said correlation surface generated from the latest acquired of said sensed images to form a new composite correlation surface.

Claim 20

The method of claim 12, wherein said using step comprises the steps of: computing peak-to-sidelobe ratio (PSR) and antipeak-to-sidelobe (APSR) ratio for said composite correlation surface using said maximum and minimum correlation amplitudes; and using either said PSR or APSR to determine said match point.

Claim 22

An image matching method comprising the steps of: acquiring a sequence of sensed images with at least three of said sensed images being located within a larger reference map; correlating each of said sensed images with said reference map to generate a correlation surface for each of said sensed images comprising the steps of: comparing each of said sensed images to said reference map at every location in said reference map at which said sensed image is entirely within said reference map, and computing a correlation amplitude for each location of said sensed image in said reference map, said correlation amplitudes forming said correlation surface; adding coherently said correlation surfaces to form a composite correlation surface; searching said composite correlation surface for a maximum correlation amplitude that exceeds an upper threshold value and for a minimum correlation amplitude which is below a lower threshold value wherein said upper and lower threshold values are adjusted based on the standard deviation of actual correlation amplitudes excluding the extremes; computing peak-to-sidelobe ratio (PSR) and antipeak-to-sidelobe ratio (ASPR) for said composite correlation surface using said maximum and minimum correlation amplitudes; using said PSR or APSR to determine a match point of said sequence of sensed images in said reference map; forming a new composite correlation surface when said match point cannot be determined using said composite correlation surface comprising the steps of: subtracting from said composite correlation surface said correlation surface generated form the oldest acquired of said sensed images; and adding to said composite correlation surface said correlation surface generated from the latest acquired of said sensed images to form said new composite correlation surface; redetermining said match point of said sequence of sensed images in said reference map using said new composite correlation surface; and repeating said above forming and redetermining steps until said match point with greatest PSR or APSR is determined or until the last acquired of said sensed images located in said reference map is correlated, whichever occurs first.

Claim 23

An image matching method comprising the steps of: acquiring a sequence of sensed images with at least three of said sensed images being located within a larger reference map; correlating each of said sensed images with said reference map to generate a correlation surface for each of said sensed images comprising the steps of: comparing each of said sensed images to said reference map at every location in said reference map at which said sensed image is entirely within said reference map, and computing a correlation amplitude for each location of said sensed image in said reference map, said correlation amplitudes forming said correlation surface; adding coherently said correlation surfaces to form a composite correlation surface; searching said composite correlation surface for a maximum correlation amplitude that exceeds an upper threshold value and for a minimum correlation amplitude which is below a lower threshold value wherein said upper and lower threshold values are adjusted based on the standard deviation of actual correlation amplitudes excluding the extremes; computing peak-to-sidelobe ratio (PSR) and antipeak-to-sidelobe ratio (ASPR) for said composite correlation surface using said maximum and minimum correlation amplitudes; using said PSR or APSR to determine a match point of said sequence of sensed images in said reference map; forming a new composite correlation surface when said match point cannot be determined using said composite correlation surface comprising the steps of: subtracting from said composite correlation surface said correlation surface generated form the oldest acquired of said sensed images; and adding to said composite correlation surface said correlation surface generated from the latest acquired of said sensed images to form said new composite correlation surface; redetermining said match point of said sequence of sensed images in said reference map using said new composite correlation surface; and repeating said above forming and redetermining steps until said match point with greatest PSR or APSR is determined or until the last acquired of said sensed images located in said reference map is correlated, whichever occurs first.

Claim 24

A correlation processor comprising: means for correlating a sequence of sensed images with a reference map to generate a correlation surface for each of said sense images, said correlating means comprising: means for comparing each of said sensed images to said reference map at every location in said reference map said correlation amplitudes forming said correlation surface; means for combining said correlation surfaces to form a composite correlation surface; and means for determining a match point for said sequence of sensed images in said reference map using said composite correlation surface.

Claim 25

A correlation processor comprising: means for correlating a sequence of sensed images with a reference map to generate a correlation surface for each of said sense images, said correlating means comprising: means for comparing each of said sensed images to said reference map at every location in said reference map said correlation amplitudes forming said correlation surface; means for combining said correlation surfaces to form a composite correlation surface; and means for determining a match point for said sequence of sensed images in said reference map using said composite correlation surface.

Claim 26

The correlation processor as recited in claim 20, wherein said combining means comprises means for adding coherently said correlation surfaces.

Claim 27

The correlation processor as recited in claim 21, wherein said adding means comprises means for superposing said correlation surfaces using data which relate said correlation surfaces to each other.

Claim 28

The correlation processor as recited in claim 22, wherein said determining means comprises: means for searching said composite correlation surface for a maximum correlation amplitude which exceeds an upper threshold value and for a minimum correlation amplitude which is below a lower threshold value; and means for using either said maximum or minimum correlation amplitudes to determine said match point.

Claim 29

The correlation processor as recited in claim 23, wherein said determining means further comprises means for adjusting said upper and lower threshold values based on the standard deviation of actual correlation amplitudes excluding the extremes.

Claim 30

The correlation processor as recited in claim 24, further comprising: means for forming a new composite correlation surface when said match point cannot be determined using said composite correlation surface, said forming means comprising: means for subtracting from said composite correlation surface said correlation surface generated from the oldest acquired of said sensed images; and means for adding to said composite correlation surface said correlation surface generated from the latest acquired of said sensed images to form said new composite correlation surface; means for redetermining said match point of said sequence of sensed images in said reference map using said new composite correlation surface; and means for repeating said above forming and redetermining steps until said match point is determined or until the last acquired of said sensed images located in said reference map is correlated, whichever occurs first.

Claim 31

The correlation processor as recited in claim 25, wherein said correlating means further comprises means for generating only that portion of said correlation surface for each of said sensed images needed to form said composite correlation surface.