DETAILED ACTION
Status of Claims
The following is a non-final Office action in response to the communication filed 4/30/2025.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
Information Disclosure Statement received 4/15/2024 and 9/1/2022 have been reviewed and considered.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s amendments and associated arguments, filed 4/30/2025, regarding the rejection of claims 10-15 under 35 U.S.C. §112(b) have been fully considered and are persuasive. The rejection of claims 10-15 under 35 U.S.C. §112(b) has been withdrawn.
Upon further search and consideration, references were identified that could be applied in a rejection of the claims under 35 USC 102 and 35 USC 103. The identification of allowable subject matter has been withdrawn and a new grounds of rejection is presented below.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 7, 9-10 and 15-17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wu et al. (US5672872).
Regarding claim 1, Wu discloses:
A navigation system comprising:
an inertial navigation system (INS) that is configured to provide a coordinate frame corresponding to an inertial reference of the INS relative to a geodetic coordinate system, the coordinate frame comprising a reference axis having a reference orientation relative to physical axes of the INS;
Wu (3) This invention relates generally to a boresight alignment scheme for target detection and tracking and, more particularly, to a forward-looking infrared (FLIR) boresight alignment scheme that aligns a sensor boresight to a set of navigation coordinates using a GPS/INS system and a Kalman filter algorithm (Col. 1, ll. 5-10)
Wu (10) FIG. 3 depicts a navigation reference frame that defines the orientation of the aircraft 10 in cartesian coordinates for the aircraft mission control computer. The orientation of the navigation reference frame relative to a navigation frame is determined by an aircraft inertial navigation system (INS) (not shown). An aircraft INS is a well known device for giving aircraft position, velocity and attitude. The INS gives the aircraft reference position to the mission control computer. In order for the weapons 12 to be accurately guided to the target, its location must be determined by the FLIR system; precision alignment between the pod LOS and the navigation reference frame is necessary. For current state of the art systems, this alignment must be within 1 milli-radian per axis, 1 sigma (Col. 2, ll. 17-30, Fig. 3)
Wu (3) The present invention proposes modifying the currently existing FLIR boresight alignment technique, as outlined above, to increase its accuracy and response time, and reduce its cost and hardware implementation. To accomplish this goal, the present invention proposes, in one embodiment, to replace the autocollimator and the active structure alignment system in the existing pod 20 with a pod INS, and by using a Kalman filter algorithm to maintain its alignment to a set of navigation coordinates. (Col. 4, ll. 17-25)
an optical tracking device configured to obtain a reference image to determine an orientation of a boresight axis of the optical tracking device; and
FIG. 4 is a depiction of a pod mounting reference frame, line-of-sight reference frame, and boresight alignment reference frame relative to a pod of FLIR system (Col. 3, ll. 60-62, Fig. 4);
Wu (25) At each observation, the target geolocation determination accuracy is a function of target range error and target LOS angle errors. However, if a multiple look at the target is possible, then a Kalman filter including target position as states can be implemented to improve the target CEP performance, since the random noise or high frequency components of range error and target LOS angle errors can be averaged out during the multi-look operation. Some components of IR sensor boresight to IMU sensor misalignment are observable from changes in the LOS angles to the target….(Col. 8, ll. 5-15)
Wu (26) The multiple look operation system 100 includes an IR receiver (sensor) 102 and a pod INS/GPS 104 intended to represent the IR receiver 58 and the INS/GPS 62, respectively. A laser range finder 106, which may be positioned within the pod 20 or elsewhere, provides target distance from the aircraft 10. The IR receiver 102 provides image data to a pixel location determination system 108…. (Col. 8, ll. 15-35, Fig. 8-9)
an alignment controller configured to compare the reference axis based on the coordinate frame and the boresight axis based on the reference image to determine a three-dimensional angular misalignment between the reference axis and the boresight axis, and to adjust the reference orientation to provide an adjusted reference axis that is aligned with the boresight axis based on the determined three-dimensional angular misalignment.
Wu (27) The location of the target in the image from the IR receiver 102 is determined by the location determination system 108. The output of the location determination system 108 is the measured target AZ/EL angles in the IR receiver coordinate frame. The LOS vector computation 110 determines the computed location of the target based on the sensor LOS attitude, sensor geolocation, and target range information. The target pixel location compute system 114 determines the computed target AZ/EL angles in IR receiver coordinates. The measured target AZ/EL angles and the computed target AZ/EL angles are then applied to a summation device 116 to determine the residuals between these angles. The measurement residuals are the azimuth angle differences and the elevation angle differences. (Col. 8, ll. 35-50, FIG 3, 6, 9)
Wu (28) Kalman filtering is performed by a Kalman filter gain matrix computation system 118 based on the sensitivity matrices from the system 112. The Kalman filtering technique uses covariance matrix updates calculated by a covariance matrix update system 120 and covariance matrix propagation calculated by a covariance matrix propagation system 122, as shown. The output from the Kalman computations from the system 118 is then applied to a geolocation correction system 124 that computes the target geolocation correction based on the Kalman filtering computation and the measurement residuals. The target geolocation estimate is determined by an initial target geolocation estimate system 126 and a current target geolocation estimate system 128. The target geolocation estimate and the computed target geolocation correction are both applied to a summation device 130 to provide the target geolocation update. (Col. 8, ll. 50-65)
See also: Wu (6) According to the invention, two transfer alignment approaches are utilized to align the pod LOS to the navigation reference frame. The pod INS system, according to the invention, located in the pod 20, can be transfer aligned to the aircraft INS system coordinates and/or to the aircraft GPS system coordinates. This transfer alignment occurs by "matching" the position and/or the velocity outputs of the pod INS against comparable outputs of a "reference INS" (the aircraft INS or GPS), in the presence of aircraft maneuvers. Attitude misalignments are estimated during this process as they create observable errors in the pod INS output, relative to the reference output. This estimation process is carried out using a navigational Kalman filter. In the case of transfer alignment to the aircraft INS, matching is done using position and/or velocity outputs and/or attitude output of the aircraft INS. Transfer alignment to GPS coordinates can similarly use matching to position/velocity GPS receiver outputs, which are referred to as a "cascaded" mechanism. This approach is referred to as the cascaded Kalman filter (CKF) approach. (Col. 4-5, ll. 55 – 10); Wu (7) Alternately, transfer alignment to GPS can use GPS pseudo range and delta-pseudo range outputs, which is referred to as a "direct" mechanism (Col. 5, ll. 10-21)
Regarding claim 2, Wu discloses the limitations of claim 1, and further discloses:
wherein the reference orientation is saved in a memory, wherein the alignment controller is configured to adjust the reference orientation in the memory to provide the adjusted reference axis aligned with the boresight axis.
Wu (10) FIG. 3 depicts a navigation reference frame that defines the orientation of the aircraft 10 in cartesian coordinates for the aircraft mission control computer. The orientation of the navigation reference frame relative to a navigation frame is determined by an aircraft inertial navigation system (INS) (not shown). An aircraft INS is a well known device for giving aircraft position, velocity and attitude. The INS gives the aircraft reference position to the mission control computer. In order for the weapons 12 to be accurately guided to the target, its location must be determined by the FLIR system; precision alignment between the pod LOS and the navigation reference frame is necessary. For current state of the art systems, this alignment must be within 1 milli-radian per axis, 1 sigma (Col. 2, ll. 17-30, Fig. 3)
Wu (27) The location of the target in the image from the IR receiver 102 is determined by the location determination system 108. The output of the location determination system 108 is the measured target AZ/EL angles in the IR receiver coordinate frame. The LOS vector computation 110 determines the computed location of the target based on the sensor LOS attitude, sensor geolocation, and target range information. The target pixel location compute system 114 determines the computed target AZ/EL angles in IR receiver coordinates. The measured target AZ/EL angles and the computed target AZ/EL angles are then applied to a summation device 116 to determine the residuals between these angles. The measurement residuals are the azimuth angle differences and the elevation angle differences. (Col. 8, ll. 35-50)
Wu (28) Kalman filtering is performed by a Kalman filter gain matrix computation system 118 based on the sensitivity matrices from the system 112. The Kalman filtering technique uses covariance matrix updates calculated by a covariance matrix update system 120 and covariance matrix propagation calculated by a covariance matrix propagation system 122, as shown. The output from the Kalman computations from the system 118 is then applied to a geolocation correction system 124 that computes the target geolocation correction based on the Kalman filtering computation and the measurement residuals. The target geolocation estimate is determined by an initial target geolocation estimate system 126 and a current target geolocation estimate system 128. The target geolocation estimate and the computed target geolocation correction are both applied to a summation device 130 to provide the target geolocation update. (Col. 8, ll. 50-65)
Examiner note: See loop-based correction structure of Fig. 6/9, where corrections are utilized in the formation of subsequent updates
See also: Wu (3) The present invention proposes modifying the currently existing FLIR boresight alignment technique, as outlined above, to increase its accuracy and response time, and reduce its cost and hardware implementation. To accomplish this goal, the present invention proposes, in one embodiment, to replace the autocollimator and the active structure alignment system in the existing pod 20 with a pod INS, and by using a Kalman filter algorithm to maintain its alignment to a set of navigation coordinates. (Col. 4, ll. 17-25); Wu, (16) The orientation of the sensor coordinates with respect to the navigation coordinates is computed in software. This type of mechanization saves the weight and complexity of the gimbal system and its supporting controllers.(column 6, lines 45-48).
Regarding claim 3, Wu discloses the limitations of claim 1, and further discloses:
wherein the alignment controller is configured to implement an alignment algorithm to calculate a three-dimensional misalignment angle between the reference axis and the boresight axis, wherein the alignment controller is configured to adjust the reference orientation by the three-dimensional misalignment angle to provide the adjusted reference axis.
Wu (6) According to the invention, two transfer alignment approaches are utilized to align the pod LOS to the navigation reference frame. The pod INS system, according to the invention, located in the pod 20, can be transfer aligned to the aircraft INS system coordinates and/or to the aircraft GPS system coordinates. This transfer alignment occurs by "matching" the position and/or the velocity outputs of the pod INS against comparable outputs of a "reference INS" (the aircraft INS or GPS), in the presence of aircraft maneuvers. Attitude misalignments are estimated during this process as they create observable errors in the pod INS output, relative to the reference output. This estimation process is carried out using a navigational Kalman filter. In the case of transfer alignment to the aircraft INS, matching is done using position and/or velocity outputs and/or attitude output of the aircraft INS. Transfer alignment to GPS coordinates can similarly use matching to position/velocity GPS receiver outputs, which are referred to as a "cascaded" mechanism. This approach is referred to as the cascaded Kalman filter (CKF) approach. (Col. 4-5, ll. 55 – 10)
Wu (27) The location of the target in the image from the IR receiver 102 is determined by the location determination system 108. The output of the location determination system 108 is the measured target AZ/EL angles in the IR receiver coordinate frame. The LOS vector computation 110 determines the computed location of the target based on the sensor LOS attitude, sensor geolocation, and target range information. The target pixel location compute system 114 determines the computed target AZ/EL angles in IR receiver coordinates. The measured target AZ/EL angles and the computed target AZ/EL angles are then applied to a summation device 116 to determine the residuals between these angles. (col. 8, ll. 35-50, FIG 3, 6, 9)
Wu (28) Kalman filtering is performed by a Kalman filter gain matrix computation system 118 based on the sensitivity matrices from the system 112. The Kalman filtering technique uses covariance matrix updates calculated by a covariance matrix update system 120 and covariance matrix propagation calculated by a covariance matrix propagation system 122, as shown. The output from the Kalman computations from the system 118 is then applied to a geolocation correction system 124 that computes the target geolocation correction based on the Kalman filtering computation and the measurement residuals. The target geolocation estimate is determined by an initial target geolocation estimate system 126 and a current target geolocation estimate system 128. The target geolocation estimate and the computed target geolocation correction are both applied to a summation device 130 to provide the target geolocation update. (Col. 8, ll. 50-65, FIG 9)
Regarding claim 7, Wu discloses the limitations of claim 3, and further discloses: wherein the alignment algorithm further comprises converting the three-dimensional misalignment angle to the coordinate frame through vector matrix calculations.
Wu (25) At each observation, the target geolocation determination accuracy is a function of target range error and target LOS angle errors. However, if a multiple look at the target is possible, then a Kalman filter including target position as states can be implemented to improve the target CEP performance, since the random noise or high frequency components of range error and target LOS angle errors can be averaged out during the multi-look operation. Some components of IR sensor boresight to /MU sensor misalignment are observable from changes in the LOS angles to the target, column 8, lines 6-14).
Wu (27) The location of the target in the image from the IR receiver 102 is determined by the location determination system 108. The output of the location determination system 108 is the measured target AZ/EL angles in the IR receiver coordinate frame. The LOS vector computation 110 determines the computed location of the target based on the sensor LOS attitude, sensor geolocation, and target range information. The target pixel location compute system 114 determines the computed target AZ/EL angles in IR receiver coordinates. The measured target AZ/EL angles and the computed target AZ/EL angles are then applied to a summation device 116 to determine the residuals between these angles. (col. 8, ll. 35-50, FIG 3, 6, 9).
Wu (28) Kalman filtering is performed by a Kalman filter gain matrix computation system 118 based on the sensitivity matrices from the system 112. The Kalman filtering technique uses covariance matrix updates calculated by a covariance matrix update system 120 and covariance matrix propagation calculated by a covariance matrix propagation system 122, as shown. The output from the Kalman computations from the system 118 is then applied to a geolocation correction system 124 that computes the target geolocation correction based on the Kalman filtering computation and the measurement residuals. The target geolocation estimate is determined by an initial target geolocation estimate system 126 and a current target geolocation estimate system 128. The target geolocation estimate and the computed target geolocation correction are both applied to a summation device 130 to provide the target geolocation update. (Col. 8, ll. 50-65, FIG 9)
Regarding claim 9, Wu discloses the limitations of claim 1, and further discloses: A movable platform comprising the navigation system of claim 1
Wu, Fighter Aircraft, Weapons Pod, see abstract, Fig. 1/2
Regarding claim 10, see the above rejection of claims 1-2, which recite analogous subject matter.
Regarding claim 15, Wu discloses the limitations of claim 10 and further discloses: storing the reference orientation in the memory; and accessing the memory to obtain the reference orientation to provide the adjusted reference axis aligned with the boresight axis at initialization of the INS.
Wu (10) FIG. 3 depicts a navigation reference frame that defines the orientation of the aircraft 10 in cartesian coordinates for the aircraft mission control computer. The orientation of the navigation reference frame relative to a navigation frame is determined by an aircraft inertial navigation system (INS) (not shown). An aircraft INS is a well known device for giving aircraft position, velocity and attitude. The INS gives the aircraft reference position to the mission control computer. In order for the weapons 12 to be accurately guided to the target, its location must be determined by the FLIR system; precision alignment between the pod LOS and the navigation reference frame is necessary. For current state of the art systems, this alignment must be within 1 milli-radian per axis, 1 sigma (Col. 2, ll. 17-30, Fig. 3)
Wu (27) The location of the target in the image from the IR receiver 102 is determined by the location determination system 108. The output of the location determination system 108 is the measured target AZ/EL angles in the IR receiver coordinate frame. The LOS vector computation 110 determines the computed location of the target based on the sensor LOS attitude, sensor geolocation, and target range information. The target pixel location compute system 114 determines the computed target AZ/EL angles in IR receiver coordinates. The measured target AZ/EL angles and the computed target AZ/EL angles are then applied to a summation device 116 to determine the residuals between these angles. The measurement residuals are the azimuth angle differences and the elevation angle differences. (Col. 8, ll. 35-50)
Wu (28) Kalman filtering is performed by a Kalman filter gain matrix computation system 118 based on the sensitivity matrices from the system 112. The Kalman filtering technique uses covariance matrix updates calculated by a covariance matrix update system 120 and covariance matrix propagation calculated by a covariance matrix propagation system 122, as shown. The output from the Kalman computations from the system 118 is then applied to a geolocation correction system 124 that computes the target geolocation correction based on the Kalman filtering computation and the measurement residuals. The target geolocation estimate is determined by an initial target geolocation estimate system 126 and a current target geolocation estimate system 128. The target geolocation estimate and the computed target geolocation correction are both applied to a summation device 130 to provide the target geolocation update. (Col. 8, ll. 50-65)
Examiner note: See loop-based correction structure of Fig. 6/9, where corrections are saved for use in the formation of subsequent updates
See also: Wu (3) The present invention proposes modifying the currently existing FLIR boresight alignment technique, as outlined above, to increase its accuracy and response time, and reduce its cost and hardware implementation. To accomplish this goal, the present invention proposes, in one embodiment, to replace the autocollimator and the active structure alignment system in the existing pod 20 with a pod INS, and by using a Kalman filter algorithm to maintain its alignment to a set of navigation coordinates. (Col. 4, ll. 17-25); Wu, (16) The orientation of the sensor coordinates with respect to the navigation coordinates is computed in software. This type of mechanization saves the weight and complexity of the gimbal system and its supporting controllers.(column 6, lines 45-48).
Regarding claim 16, see the above rejections of claims 1 and 7, which recite analogous subject matter.
Regarding claim 17, see the above rejection of claim 2, which recites analogous subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 8 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wu as applied above, and further in view of Jones (US 2013/0139566 A 1)
Regarding claim 8, Wu discloses the limitations of claim 1, and further discloses: wherein the optical tracking device is configured to obtain a plurality of reference images to determine the orientation of the boresight axis based on an aggregation of (see disclosure from Jones below) the plurality of reference images.
Wu (25) At each observation, the target geolocation determination accuracy is a function of target range error and target LOS angle errors. However, if a multiple look at the target is possible, then a Kalman filter including target position as states can be implemented to improve the target CEP performance, since the random noise or high frequency components of range error and target LOS angle errors can be averaged out during the multi-look operation. Some components of IR sensor boresight to IMU sensor misalignment are observable from changes in the LOS angles to the target….(Col. 8, ll. 5-15)
Wu (26) FIG. 8 depicts a block diagram of a multiple look operation system 100 using an "averaging out" where measurement residuals, i.e., the difference between the current measured target azimuth/elevation (AZ/EL) angles and the computed target AZ/EL angles based on the previous target location estimate, are computed at every successive observation
Wu (27) The location of the target in the image from the IR receiver 102 is determined by the location determination system 108. The output of the location determination system 108 is the measured target AZ/EL angles in the IR receiver coordinate frame. The LOS vector computation 110 determines the computed location of the target based on the sensor LOS attitude, sensor geolocation, and target range information. The target pixel location compute system 114 determines the computed target AZ/EL angles in IR receiver coordinates. The measured target AZ/EL angles and the computed target AZ/EL angles are then applied to a summation device 116 to determine the residuals between these angles. The measurement residuals are the azimuth angle differences and the elevation angle differences. (Col. 8, ll. 35-50)
Wu (28) Kalman filtering is performed by a Kalman filter gain matrix computation system 118 based on the sensitivity matrices from the system 112. The Kalman filtering technique uses covariance matrix updates calculated by a covariance matrix update system 120 and covariance matrix propagation calculated by a covariance matrix propagation system 122, as shown. The output from the Kalman computations from the system 118 is then applied to a geolocation correction system 124 that computes the target geolocation correction based on the Kalman filtering computation and the measurement residuals. The target geolocation estimate is determined by an initial target geolocation estimate system 126 and a current target geolocation estimate system 128. The target geolocation estimate and the computed target geolocation correction are both applied to a summation device 130 to provide the target geolocation update. (Col. 8, ll. 50-65)
Wu, as shown above, discloses the determination of orientation of the boresight axis using a plurality of reference images, which strongly suggests aggregation of said plurality of images. This concept is more explicitly disclosed by Jones.
JONES, [0036] Example 2 includes the method of Example 1, and further comprising moving the alignment measurement sensor back to the first position at least once from the second position; determining an average of data measurements taken by the alignment measurement sensor and whether a current position of the alignment measurement sensor is within repeatability bounds; if a resulting variation of the current position is within repeatability bounds, the average of the measurements is used as the misalignment factor; and if the resulting variation is outside of acceptable repeatability bounds, the measurements are rejected.
One of ordinary skill in the art would have recognized that applying the known technique of Jones to the orientation analysis of Wu would have yielded predictable results and resulted in improved system alignment.
Regarding claim 14, see the above rejection of claim 8, which recites analogous subject matter.
Potentially Allowable Subject Matter
Claims 4-6, 11-13, and 18-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABBY J FLYNN whose telephone number is (571)272-9855. The examiner can normally be reached Monday - Friday 8:30-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James Trammell can be reached at 571-272-6712. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ABBY J FLYNN/ Examiner, Art Unit 3663
