Optoelectronic image high-maneuvering target adaptive tracking method

Abstract

The application discloses a photoelectric image high-maneuvering target adaptive tracking method, which comprises the following steps: step 1, initializing an optical field index table; step 2, establishing an optical field adaptive adjustment model; step 3, acquiring point or track data of the high-maneuvering target and photoelectric images, and calculating the deviation of the target position azimuth angle in the kth frame of the photoelectric image from the center of the photoelectric image; step 4, calculating the gray scale, size and corner point features of the high-maneuvering target, and tracking through template matching; step 5, calculating the deviation of the target position azimuth angle in the k+1th frame of the photoelectric image from the center of the photoelectric image; and step 6, outputting the k+1th frame of the optical field image according to the optical field adaptive adjustment model, and completing the photoelectric image high-maneuvering target adaptive tracking.

Description

Technical Field

[0001] This invention relates to a target tracking method, and more particularly to an adaptive tracking method for highly maneuverable targets based on photoelectric images. Background Technology

[0002] Ensuring safe aircraft landings at airports is a crucial mission for tower and approach controllers. Currently, airports primarily utilize Category I I instrument landing systems (ILS), with a few large civil aviation airports equipped with Category II ILS, which mainly include beacon systems, landing radar, instrument landing systems, microwave landing systems, and TACAN systems. Existing airport approach and landing surveillance methods and systems have the following shortcomings:

[0003] (1) Civil aviation airports rely on ADS-B equipment and other airports rely on landing radar to monitor landing routes. The monitoring methods are simple and the level of informatization and automation is insufficient.

[0004] (2) Target surveillance means are reflected in the points and tracks of the flight path, and the appearance and flight attitude of the aircraft cannot be seen directly;

[0005] (3) Due to the limitations of optical field of view and operating distance, the photoelectric system is difficult to quickly and effectively capture and track highly maneuverable targets, which can easily cause the target to leave the optical field of view and result in target tracking loss. Summary of the Invention

[0006] Purpose of the invention: The technical problem to be solved by the present invention is to provide an adaptive tracking method for highly maneuverable targets based on photoelectric images, addressing the shortcomings of the existing technology.

[0007] To address the aforementioned technical problems, this invention discloses an adaptive tracking method for highly maneuverable targets based on photoelectric images, comprising the following steps:

[0008] Step 1: Initialize the optical field of view (FOV) index table;

[0009] Step 2: Establish an adaptive adjustment model for the optical field of view;

[0010] Step 3: Acquire point or track data of highly maneuverable targets and obtain electro-optical images. Calculate the target's azimuth angle θ in the k-th frame of the electro-optical image. k Deviation Δθ from the center of the photoelectric image k ;

[0011] Step 4: Calculate the grayscale, size, and corner features of the highly maneuverable target, and track it using template matching;

[0012] Step 5: Calculate the azimuth angle θ of the target position in the (k+1)th frame of the photoelectric image. k+1 Deviation Δθ from the center of the photoelectric image k+1 ;

[0013] Step 6: Output the (k+1)th frame of the optical field of view image based on the optical field of view adaptive adjustment model to complete the adaptive tracking of highly maneuverable targets in photoelectric images.

[0014] Furthermore, the initialization of the optical field of view (FOV) index table described in step 1 specifically includes:

[0015] Let the optical field of view index table be:

[0016] FOV = {FOV1, FOV2, ..., FOV} m ,…,FOV M}

[0017] The optical field of view (FOV) index table is arranged from left to right, with the optical field of view increasing progressively: {FOV1, FOV2, ..., FOV}. m ,…,FOV M} represents different sizes of optical field of view, FOV m Let M represent the m-th optical field of view, where M is the independent variable representing the number of different sizes of optical fields of view, and is a positive integer.

[0018] Furthermore, the optical field-of-view adaptive adjustment model described in step 2 specifically includes:

[0019] The optical field-of-view adaptive adjustment model outputs a quantity FOV. m The expression is as follows:

[0020]

[0021] Where, Δθ k+1 Δθ represents the deviation between the azimuth angle of the target position in the (k+1)th frame of the photoelectric image and the center of the photoelectric image. k This represents the deviation between the azimuth angle of the target position in the k-th frame of the photoelectric image and the center of the photoelectric image.

[0022] Furthermore, the point or track data of the highly maneuverable target mentioned in step 3, namely the azimuth, elevation, and range information provided by radar data or ADS-B data.

[0023] Furthermore, the photoelectric images mentioned in step 3 refer to the infrared and visible light images acquired by the photoelectric tracking turntable.

[0024] Furthermore, step 3 involves calculating the target position azimuth angle θ in the k-th frame of the photoelectric image. k Deviation Δθ from the center of the photoelectric image k The specific method is as follows:

[0025]

[0026] Where, θk This represents the azimuth angle of the target position in the k-th frame of the photoelectric image. This represents the azimuth angle of the center of the photoelectric image in the k-th frame.

[0027] Furthermore, the tracking described in step 4 specifically includes the following steps:

[0028] Step 4-1, Target Locking: Based on the grayscale, size, and corner features of the highly maneuverable target, establish a target priority model, calculate the priority of all target points, and select the target with the highest priority for locking;

[0029] Step 4-2: Construct an image feature matching tracking model to continuously track the target through template matching.

[0030] Furthermore, the target locking described in step 4-1 specifically includes:

[0031] Step 4-1-1: Establish a target priority model based on the target's grayscale, size, and corner features, as detailed below:

[0032] Step 4-1-1-1, calculate the target grayscale feature coefficients:

[0033]

[0034] Among them, w GrayScale G(x,y) represents the grayscale feature coefficient of the target to be locked, G(x,y) represents the grayscale value of the pixel, (x,y) represents the image coordinate position, (x0,y0) represents the starting coordinate of the target region, and (x1,y1) represents the ending coordinate of the target region.

[0035] Step 4-1-1-2, calculate the target size characteristic coefficients:

[0036]

[0037] Among them, w size R represents the size characteristic coefficient of the target to be locked. ratio The default target has the best aspect ratio;

[0038] Step 4-1-1-3: Calculate the target corner feature coefficients and find ORB corners based on the target region. The calculation method is as follows:

[0039]

[0040] Among them, w CornerPoint Pn represents the corner feature coefficients of the target to be locked, Pn is the number of orb corners, and Pmax is the maximum number of effective corners of the target.

[0041] Step 4-1-1-4: Calculate the priority weight of the target to be locked, as follows:

[0042] w = a1·w GrayScale +a2·w Size +a3·w CornerPoint

[0043] Where w is the priority weight of the target to be locked, and a1, a2, a3 are the weight ratios. The priority of all target points to be locked is calculated, and the target with the highest priority, i.e. the largest w, is selected for locking.

[0044] Step 4-1-2: Use the Canny segmentation operator to calculate the bounding rectangle of the target to be locked, and complete the locking.

[0045] Furthermore, the continuous tracking of the target described in step 4-2 specifically includes:

[0046] By establishing a tracking template using the bounding rectangle of the locked target, an image feature matching tracking model is constructed, that is, the locked target is continuously tracked by matching frame by frame;

[0047] During matching, the similarity between the tracking template and the matching region is calculated, and the most similar position is taken as the matching point. The mean difference and SSD are used as similarity measures for the two pixels T(i,j) and f(i,j) to be matched, as follows:

[0048]

[0049] Where T(i,j) is the pixel value in row i and column j of the tracking template, f(i,j) is the pixel value of the image to be matched, (x c ,y c ) represents the offset value of the tracking template in the image to be matched, I represents the window height, and J represents the window width. The frame-by-frame tracking deviation of the locked target is minimized by d(x). c ,y c x in ) c ,y c The value is used to obtain the target position (x) for each frame in sequence. p ,y p ), where p represents the p-th frame.

[0050] Furthermore, the deviation Δθ mentioned in step 5 k+1 The calculation methods include:

[0051]

[0052] Where, θ k+1 This represents the azimuth angle of the target position in the (k+1)th frame of the photoelectric image. It represents the azimuth angle of the center of the (k+1)th frame of the photoelectric image.

[0053] Beneficial effects:

[0054] 1. This invention introduces a photoelectric tracking turntable into aircraft landing monitoring, integrating multi-source information to perform full-process automatic real-time monitoring of the aircraft landing process, effectively solving the problems of single monitoring methods and insufficient informatization and automation levels.

[0055] 2. In addition to providing flight point and track information through traditional surveillance methods, this invention adds image information such as aircraft appearance and flight attitude, which can more intuitively assist tower controllers and approach controllers in target surveillance and control command.

[0056] 3. This invention can effectively solve the problem of highly maneuverable targets leaving the optical field of view through adaptive adjustment of the optical field of view, and ensure high-precision and stable tracking of the target. Attached Figure Description

[0057] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0058] Figure 1 A schematic diagram of the workflow of the method of the present invention.

[0059] Figure 2a This is a schematic diagram of the tracking result of a highly maneuverable target in one embodiment of the present invention.

[0060] Figure 2b This is a schematic diagram of the tracking result of a highly maneuverable target in one embodiment of the present invention.

[0061] Figure 2c This is a schematic diagram of the tracking result of a highly maneuverable target in one embodiment of the present invention. Detailed Implementation

[0062] This invention provides an adaptive tracking method for highly maneuverable targets based on photoelectric images, which provides tower controllers and approach controllers with accurate aircraft landing information and maximizes aircraft landing safety.

[0063] The technical solution mainly includes: acquiring the target's point / track and photoelectric image, automatically locking the target, performing real-time tracking through template matching, and calculating the current optical field image based on the optical field adjustment model.

[0064] Step 1: Initialize the optical field of view (FOV) index table;

[0065] Optical Field of View (FOV) Index Table: {FOV1, FOV2, ..., FOV} m}, where the field of view (FOV) increases progressively from left to right. mThis represents different optical field of view sizes, where m is the independent variable and its value is a positive integer. The maximum and minimum optical field of view are defined as FOV. max FOV min ,but

[0066]

[0067] Where M represents the averaging parameter.

[0068] Step 2: Establish an adaptive adjustment model for the optical field of view

[0069] Optical field of view adaptive adjustment model output FOV m The expression is as follows:

[0070]

[0071] Among them, FOV m Δθ represents different sizes of optical field of view. k+1 Δθ represents the deviation between the azimuth angle of the target position in the (k+1)th frame and the center of the image. k This represents the deviation between the azimuth angle of the target position in the k-th frame and the center of the image.

[0072] Step 3: Acquire the point / track and electro-optical images of the highly maneuverable target, and calculate the target's azimuth angle θ in the k-th frame. k Deviation Δθ from the image center k ;

[0073] The point / track data of radar targets specifically includes azimuth, elevation, range, and secondary codes;

[0074] ADS-B devices output longitude, latitude, altitude, secondary codes, and target address codes. By converting geographic coordinates to spherical coordinates, they obtain the target's point / track data, specifically including azimuth, elevation, distance information, secondary codes, and target address codes.

[0075] Photoelectric images include infrared and visible light image information.

[0076] The deviation Δθ between the target position azimuth angle and the image center in the kth frame k The expression is as follows:

[0077]

[0078] Where, θ k This represents the azimuth angle of the target position in the k-th frame. This represents the azimuth angle of the center of the k-th frame image.

[0079] Step 4: Calculate the grayscale, size, and corner features of the highly maneuverable target, and perform high-precision tracking through template matching;

[0080] Step 4-1: Automatically lock onto the target

[0081] The automatic target locking strategy uses the Canny segmentation operator to calculate the bounding rectangle of potential targets and establishes a target priority model based on features such as target grayscale, size, and corner points, as shown in the following formula:

[0082] Target grayscale features:

[0083]

[0084] Among them, w GrayScale Let G(x,y) be the grayscale feature coefficient of a certain target, where G(x,y) is the grayscale value of a pixel, x and y are the image coordinates, x0 and y0 are the starting coordinates of the target region, and x1 and y1 are the ending coordinates of the target region.

[0085] Target size characteristics:

[0086]

[0087] Among them, w size R is the dimensional characteristic coefficient. ratio The default optimal aspect ratio is defined by x0 and y0, which are the starting coordinates of the target area and x1 and y1, which are the ending coordinates of the target area.

[0088] Target corner features: Based on the target region, ORB corners are found, and the corner feature coefficient values ​​are calculated using the following formula:

[0089]

[0090] Where Pn is the number of orb corners, and Pmax is the maximum number of effective corners of the target.

[0091] The priority model formula is as follows:

[0092] w = a1·w GrayScale +a2·w Size +a3·w CornerPoint

[0093] Where w is the locking priority weight, and a1, a2, and a3 are the weight ratios. The priority of all target points is calculated, and the target with the highest priority, i.e., the largest w, is automatically locked.

[0094] Step 4-2: Image Feature Matching and Tracking

[0095] After selecting the target based on priority, a tracking template is established by using the target's bounding rectangle. An image feature matching tracking model is constructed, and the target is tracked with high precision and stability by matching multiple feature templates frame by frame.

[0096] During template matching, the similarity between the template and the matching region is calculated, and the most similar position is taken as the matching point. The following matching criteria are used as the similarity measure of T(i,j) and f(i,j): Sum of Square Difference (SSD).

[0097]

[0098] Where T(i,j) is the pixel value in row i and column j of the template, and f(i,j) is the pixel value of the image under examination, (x c ,y c ) represents the offset of the template image in the image to be matched. All three measures are within the range of d(x). c ,y c The optimal matching position is obtained when the minimum value is reached, and the target tracking deviation frame by frame is the minimum d(x). c ,y c x in ) c ,y c The value is obtained sequentially from the target position (x) of each frame. p ,y p ), where p represents the p-th frame.

[0099] Step 5: Calculate the azimuth angle θ of the target position in the (k+1)th frame. k+1 Deviation Δθ from the image center k+1 ;

[0100] The deviation Δθ between the target position azimuth angle and the image center in the (k+1)th frame k+1 The expression is as follows:

[0101]

[0102] Where, θ k+1 This represents the azimuth angle of the target position in the (k+1)th frame. It represents the azimuth angle of the center of the (k+1)th frame image.

[0103] Step 6: Output the (k+1)th frame of the optical field of view image based on the optical field of view adaptive adjustment model.

[0104] Based on the optical field-of-view adaptive adjustment model, the output FOV is calculated. m .

[0105] Example:

[0106] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0107] like Figure 1 As shown, this invention discloses an adaptive tracking method for highly maneuverable targets based on photoelectric images, comprising the following steps:

[0108] (1) Initialize the optical field of view (FOV) index table;

[0109] Optical Field of View (FOV) Index Table: {FOV1, FOV2, ..., FOV} m From left to right, the field of view (FOV) increases progressively. m This represents different optical field of view sizes, where m is the independent variable and its value is a positive integer. The maximum and minimum optical field of view are defined as FOV. max =35.1°, FOV min =1.8°, then

[0110]

[0111] Where M represents the equal distribution parameter, and its value is 24.

[0112] Therefore, the optical field of view index table FOV = {1.8°, 3.2°, ..., 35.1°}.

[0113] (2) Establish an adaptive adjustment model for the optical field of view.

[0114] Optical field of view adaptive adjustment model output FOV m The expression is as follows:

[0115]

[0116] Among them, FOV m Δθ represents different sizes of optical field of view. k+1 Δθ represents the deviation between the azimuth angle of the target position in the (k+1)th frame and the center of the image. k This represents the deviation between the azimuth angle of the target position in the k-th frame and the center of the image.

[0117] (3) Acquire the point / track and electro-optical images of the highly maneuverable target, and calculate the target position azimuth angle θ in the k-th frame. k Deviation Δθ from the image center k ;

[0118] The point / track data of radar targets specifically includes azimuth, elevation, range, and secondary codes;

[0119] ADS-B devices output longitude, latitude, altitude, secondary code (5707), and target address code information (FF085D91). By converting geographic coordinates to spherical coordinates, point / track data of the target can be obtained, specifically including bearing, elevation, distance information, and secondary code 5707.

[0120] Photoelectric images include infrared and visible light image information.

[0121] The deviation Δθ between the target position azimuth angle and the image center in the kth framek The expression is as follows:

[0122]

[0123] Where, θ k This represents the azimuth angle of the target position in the k-th frame. This represents the azimuth angle of the center of the k-th frame image.

[0124] (4) Calculate the grayscale, size and corner features of highly maneuverable targets, and perform high-precision tracking through template matching;

[0125] (a) Automatic target locking

[0126] The bounding rectangles (240, 125, 80, 35) of potential target 1 and (350, 279, 28, 134) of potential target 2 in the image are calculated using the Canny operator. A target priority model is established based on grayscale, size and corner features, and the target with higher priority is automatically locked.

[0127] Target grayscale features:

[0128] Target 1 grayscale features Target 2 grayscale features

[0129] Target size feature, R ratio The optimal aspect ratio for the target is set to 2.5.

[0130]

[0131] Target corner feature: Find ORB corners based on the target region. Pmax is set to 20, the number of target 1 corners is 14, the number of target 2 corners is 3, and the corner feature coefficient values ​​are:

[0132]

[0133] a1, a2, and a3 are fixed values ​​set to 1, 0.5, and 1.5 respectively. The priority is calculated as follows:

[0134] w 1 =1×0.57+0.5×5+1.5×0.75=1.94

[0135] w 2 =1×0.22+0.5×1.4+1.5×0.15=1.145

[0136] Target 1 has a higher weight than Target 2, therefore, Target 1 is automatically locked.

[0137] (b) Image feature matching and tracking

[0138] After selecting the target based on priority, a tracking template is established by using the target's bounding rectangle. An image feature matching tracking model is constructed, and the target is tracked with high precision and stability by matching multiple feature templates frame by frame.

[0139] (5) Calculate the azimuth angle θ of the target position in the (k+1)th frame. k+1 Deviation Δθ from the image center k+1 ;

[0140] The deviation Δθ between the target position azimuth angle and the image center in the (k+1)th frame k+1 The expression is as follows:

[0141]

[0142] Where, θ k+1 This represents the azimuth angle of the target position in the (k+1)th frame. It represents the azimuth angle of the center of the (k+1)th frame image.

[0143] (6) Output the (k+1)th frame of the optical field of view image according to the adaptive adjustment model of the optical field of view.

[0144] Based on the optical field-of-view adaptive adjustment model, the output FOV is calculated. m .

[0145] like Figure 2a , Figure 2b and Figure 2c The image shows the practical application results of the method in this embodiment for tracking an aircraft in flight.

[0146] In its specific implementation, this application provides a computer storage medium and a corresponding data processing unit. The computer storage medium is capable of storing a computer program, which, when executed by the data processing unit, can run the invention's content regarding an adaptive tracking method for highly maneuverable targets in photoelectric images, as well as some or all of the steps in various embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0147] Those skilled in the art will clearly understand that the technical solutions in the embodiments of the present invention can be implemented using computer programs and their corresponding general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of computer programs, i.e., software products. These computer program software products can be stored in a storage medium and include several instructions to cause a device containing a data processing unit (which may be a personal computer, server, microcontroller, MUU, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0148] This invention provides a concept and method for adaptive tracking of highly maneuverable targets using photoelectric images. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. An adaptive tracking method for highly maneuverable targets based on photoelectric images, characterized in that, Includes the following steps: Step 1: Initialize the optical field of view index table ; Step 2: Establish an adaptive adjustment model for the optical field of view, which specifically includes: The optical field-of-view adaptive adjustment model outputs... The expression is as follows: ； in, Represents the photoelectric image of the first The deviation between the azimuth angle of the target position in the frame and the center of the photoelectric image. Represents the photoelectric image of the first The deviation between the azimuth angle of the target position in the frame and the center of the photoelectric image; Step 3: Acquire point or track data of highly maneuverable targets, and acquire electro-optical images. Calculate the first... Target position azimuth angle in the frame Deviation from the center of the photoelectric image ; Step 4: Calculate the grayscale, size, and corner features of the highly maneuverable target, and track it using template matching; Step 5: Calculate the photoelectric image. Target position azimuth angle in the frame Deviation from the center of the photoelectric image ; Step 6: Output the first step based on the optical field-of-view adaptive adjustment model. Frame optical field-of-view images are used to complete adaptive tracking of highly maneuverable targets using photoelectric images.

2. The photoelectric image-based adaptive tracking method for highly maneuverable targets according to claim 1, characterized in that, The initialization of the optical field index table described in step 1 Specifically, it includes: Let the optical field of view index table be: ； Among them, the optical field of view index table From left to right, the optical field of view increases progressively. Represents different sizes of optical field of view. Indicates the first Level optical field of view, It is the independent variable, representing the number of optical fields of view of different sizes, and is a positive integer.

3. The photoelectric image-based adaptive tracking method for highly maneuverable targets according to claim 2, characterized in that, The point or track data of the highly maneuverable target mentioned in step 3 refers to the azimuth, elevation, and range information provided by radar data or ADS-B data.

4. The photoelectric image-based adaptive tracking method for highly maneuverable targets according to claim 3, characterized in that, The photoelectric images mentioned in step 3 refer to the infrared and visible light images acquired by the photoelectric tracking turntable.

5. The photoelectric image-based adaptive tracking method for highly maneuverable targets according to claim 4, characterized in that, The calculation of the photoelectric image in step 3 Target position azimuth angle in the frame Deviation from the center of the photoelectric image The specific method is as follows: ； in, Represents the photoelectric image number 1 The azimuth angle of the target position in the frame. Represents the photoelectric image number 1 The azimuth angle of the center of the photoelectric image of the frame.

6. The photoelectric image-based adaptive tracking method for highly maneuverable targets according to claim 5, characterized in that, The tracking described in step 4 specifically includes the following steps: Step 4-1, Target Locking: Based on the grayscale, size, and corner features of the highly maneuverable target, establish a target priority model, calculate the priority of all target points, and select the target with the highest priority for locking; Step 4-2: Construct an image feature matching tracking model to continuously track the target through template matching.

7. The photoelectric image-based adaptive tracking method for highly maneuverable targets according to claim 6, characterized in that, The target locking described in step 4-1 specifically includes: Step 4-1-1: Establish a target priority model based on the target's grayscale, size, and corner features, as detailed below: Step 4-1-1-1, calculate the target grayscale feature coefficients: ； in, The grayscale feature coefficients of the target to be locked. The grayscale value of a pixel. Image coordinates The starting coordinates of the target area. The end coordinates of the target area; Step 4-1-1-2, calculate the target size characteristic coefficients: ； in, The size characteristic coefficients of the target to be locked. The default target has the best aspect ratio; Step 4-1-1-3: Calculate the target corner feature coefficients and find ORB corners based on the target region. The calculation method is as follows: )； in, The corner feature coefficients of the target to be locked. The number of orb corner points. The target is the maximum number of effective corner points; Step 4-1-1-4: Calculate the priority weight of the target to be locked, as follows: ； in, The priority weights of the targets to be locked. , , Assuming weighted proportions, calculate the priority of all target points to be locked, and select the one with the highest priority. Lock onto the largest target; Step 4-1-2: Use the Canny segmentation operator to calculate the bounding rectangle of the target to be locked, and complete the locking.

8. The photoelectric image-based adaptive tracking method for highly maneuverable targets according to claim 7, characterized in that, The continuous tracking of the target described in step 4-2 specifically includes: By establishing a tracking template using the bounding rectangle of the locked target, an image feature matching tracking model is constructed, that is, the locked target is continuously tracked by matching frame by frame; During the matching process, the similarity between the tracking template and the matching region is calculated, and the most similar position is taken as the matching point. The average difference and SSD are used as the two pixels to be matched. and The similarity measure is as follows: ； in, In the tracking template OK Column pixel values, The pixel values ​​of the image to be matched. To track the offset value of the template in the image to be matched, Indicates the window height. This indicates that the window width minimizes the frame-by-frame tracking deviation of the locked target. In The value is used to obtain the target position for each frame in turn. , where p represents the p-th frame.

9. The photoelectric image-based adaptive tracking method for highly maneuverable targets according to claim 1, characterized in that, The deviation amount mentioned in step 5 The calculation methods include: ； in, Represents the photoelectric image of the first The azimuth angle of the target position in the frame. Represents the photoelectric image number 1 The azimuth angle of the image center of the frame.

Images