A fast identification method for faint targets of space debris based on daytime guide star images

Abstract

The present invention discloses a method for quickly identifying dim targets of space debris based on daytime guide star images, comprising the following steps: tracking the target according to the predicted value; collecting the target area image and recording it as the image at time T0; adjusting the telescope pointing azimuth angle ΔA and pitch angle ΔE; collecting the target area image and recording it as the image at time T1; comparing the pixels of the image P0 at time T0 and the image P1 at time T1, taking the pixel difference as the image ΔP, searching ΔP, and obtaining the target centroid coordinates; judging the change in the centroid coordinates and determining the target; calculating the target centroid miss amount and adjusting the pointing. The present application can quickly identify dim targets under strong background noise during the day, and after identifying the target, the deviation of the target angle change from the theoretical value can be quickly obtained, which is used for target pointing and distance deviation correction in future daytime space debris laser ranging.

Description

Technical Field

[0001] The present invention relates to the field of laser detection technology, and in particular to a method for quickly identifying dim targets of space debris based on daytime guide star images. Background Art

[0002] Since the first artificial satellite was put into orbit in 1957, human space activities have generated a large amount of space debris, mainly concentrated in low earth orbit (LEO) below 1000km from the ground, including abandoned satellites, rocket final stage, pyrotechnic operational debris, and disintegration fragments formed by the collision of spacecraft or rocket (missile) bodies due to their own explosions. With the continuous development of the aerospace industry, more and more failed and non-recoverable artificial satellites, rocket bodies, and spacecraft are left in space, causing the number of space debris to grow rapidly. Debris Laser Ranging (DLR), as the most accurate ranging technology at present, plays an important role in the precise determination of space debris orbits, precise cataloging, monitoring and early warning. At present, debris laser ranging is mainly carried out in the dawn and dusk phases when the target is visible and the sky background is dark. In order to obtain more observation data, it is necessary to carry out daytime space debris laser ranging research. However, due to the low accuracy of space debris prediction, in the process of space debris laser ranging, it is necessary to image the target through optical telescopes and cameras to obtain the target offset and then correct the pointing position. However, during the daytime debris laser ranging process, the sky light background noise is strong, the target imaging signal-to-noise ratio is low, and it is susceptible to other interferences such as star and camera noise.

[0003] At present, there are two methods for identifying dim targets: single-frame recognition method and continuous multi-frame recognition method. However, under strong daylight, due to the vignetting of the optical system caused by the atmosphere or weather at the observation site and the ambient stray light, the single-frame image of the target shot may not accurately identify the target or noise by image recognition alone; continuous superposition of several frames of images can enhance the target. However, the vignetting non-uniformity in the image is also enhanced as the image background during the superposition process, which limits the target enhancement effect, reduces the recognition efficiency and is prone to misjudgment, resulting in reduced observation efficiency. Therefore, an accurate and fast target recognition method should be designed, which can be used for target pointing correction in future daytime space debris laser ranging. Summary of the invention

[0004] The main purpose of the embodiment of the present invention is to propose a method for quickly identifying dim targets of space debris based on daytime guide star images, aiming to design a method for identifying dim targets under strong sky light background noise.

[0005] The technical solution of the present invention to solve the above technical problem is to provide a method for quickly identifying dim targets of space debris based on daytime guide star images, comprising the following steps:

[0006] Track the target according to the forecast value;

[0007] The collected target area image is recorded as the image at time T0;

[0008] Adjust the telescope pointing azimuth angle ΔA and elevation angle ΔE;

[0009] The target area image is collected and recorded as the T1 time image;

[0010] Compare the pixels of the image P0 at time T0 with the image P1 at time T1, take the pixel difference as image ΔP, search ΔP, and obtain the coordinates of the target centroid;

[0011] Determine the change in the center of mass coordinates and determine the target;

[0012] Calculate the target center of mass miss distance and adjust the pointing direction.

[0013] Furthermore, the step of adjusting the telescope pointing azimuth angle ΔA and elevation angle ΔE comprises:

[0014] The target moving orbit is an approximate circular arc orbit. The relationship between the camera field angle γA, γE and the telescope pointing angle change ΔA, ΔE, the image pixel NA, NE, and the pixel change ΔNA, ΔNE can be calculated by the following formula:

[0015]

[0016]

[0017] Furthermore, the step of comparing the pixels of the image P0 at time T0 with the pixels of the image P1 at time T1, taking the pixel difference as image ΔP, searching ΔP, and obtaining the coordinates of the target centroid includes:

[0018] Subtract the grayscale values ​​of pixels at the same position to obtain motion information or changes;

[0019] Suppose the first frame image is G1(x,y), and the second frame image is G2(x,y), where x and y represent the horizontal and vertical coordinates of the image respectively. Compare the pixel-by-pixel difference results of the two frames D(x,y). The difference formula is:

[0020] D(x,y)=G2(x,y)-G1(x,y)

[0021] D_pos(x,y)=max(D(x,y),0)

[0022] Obtain the grayscale difference image D between the two frames;

[0023] Get the target in the image.

[0024] Furthermore, the step of acquiring the target in the image includes:

[0025] Calculate image brightness: calculate the brightness value of each pixel;

[0026] Find the brightest area: find the local maximum, or segment the area with higher brightness through the threshold method, and then combine morphological operations such as corrosion and expansion to separate adjacent bright areas;

[0027] Select the first two brightest areas: sort the connected areas according to the calculated brightness statistics and select the first two areas with the highest brightness;

[0028] Marking and displaying results: Mark the selected area, obtain the area attributes, and overlay the original image with the marked brightest area;

[0029] Calculate the coordinates of the center of mass of the region N1(x, y) and N2(x, y) to obtain the change in the horizontal and vertical coordinates of the center of mass ΔN A ′、ΔN E ′.

[0030] Furthermore, the step of determining the change in the center of mass coordinates and determining the target includes:

[0031] The obtained ΔN A ′、ΔN E ′ and ΔN A , ΔN E After comparison, if the difference is less than c, the obtained area is judged to be a valid target.

[0032] The technical solution of the present invention designs a new target recognition method based on daytime guide star images, which can quickly identify dim targets under strong background noise during the day. After identifying the target, the deviation between the target angle change and the theoretical value can be quickly obtained, which can be used for target pointing and distance deviation correction in future daytime space debris laser ranging. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0034] Figure 1 This is a flowchart of the steps of the method for rapid identification of space debris laser ranging targets under strong background noise during the daytime according to the present invention;

[0035] Figure 2 It is the image P0 at time T0 during the detection and experimental verification process of the present invention;

[0036] Figure 3 It is the image P1 at time T1 during the detection and experimental verification process of the present invention;

[0037] Figure 4 It is a ΔP diagram comparing T0 and T01 images during the detection and experimental verification process of the present invention;

[0038] Figure 5 It is a target recognition diagram during the detection and experimental verification process of the present invention;

[0039] Figure 6 The target area extraction map is used for detection and experimental verification of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the accompanying drawings of the present invention specification to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0042] In addition, in the present invention, descriptions such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "several" or "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0043] In the present invention, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0044] In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0045] The present invention proposes a method for quickly identifying dim targets of space debris based on daytime guide star images, aiming to design a method for identifying dim targets under strong sky light background noise.

[0046] The following will describe the daytime guide star image proposed by the present invention in a specific embodiment:

[0047] In the technical solution of this embodiment, Figure 1 As shown, a method based on daytime guide star images includes the following steps:

[0048] S10: Track the target according to the predicted value;

[0049] Specifically, a conventional laser ranging device is used, which includes a telescope and a small-field-of-view guide camera coaxial with the telescope. The target transit information is calculated according to the target prediction, and the telescope is pointed to the target theoretical position.

[0050] S20: collecting the target area image and recording it as the image at time T0;

[0051] Specifically, the optical system includes a small field of view camera that is coaxial and pointing in the same direction as the telescope, which can obtain an image of the target position area. After obtaining the image, the image at time T0 can be obtained, and the theoretical position of the target at time T0 can be known according to the forecast.

[0052] S30: Adjust the telescope pointing azimuth angle ΔA and elevation angle ΔE;

[0053] Specifically, the target moving track is an approximate circular arc track, but due to the small field of view of the camera, the target moving track can be approximated as a linear motion within a small range. Then, the relationship between the camera field of view angle γA, γE and the telescope pointing angle change ΔA, ΔE, the image pixels NA, NE, and the pixel change ΔNA, ΔNE can be calculated by the following formula:

[0054]

[0055]

[0056] S40: collecting an image of the target area and recording it as an image at time T1;

[0057] Specifically, after obtaining the image, the image at time T1 can be obtained, and the theoretical position of the target at time T1 can be known according to the prediction.

[0058] S50: Compare the pixels of the image P0 at time T0 with the pixels of the image P1 at time T1, take the pixel difference as image ΔP, search ΔP, and obtain the coordinates of the target centroid;

[0059] Specifically, the motion information or change condition is obtained by performing a subtraction operation on the grayscale values ​​of pixels at the same position.

[0060] S60: Determine the change in the center of mass coordinates and determine the target;

[0061] S70: Calculate the target center of mass miss distance and adjust the direction.

[0062] Understandably, in strong daylight, due to the uneven distribution of skylight background noise caused by the atmosphere or weather at the observation site, in the target shot image, the target or noise cannot be accurately identified by image recognition alone, and manual recognition is required, which reduces the recognition efficiency and is prone to misjudgment, thereby reducing the observation efficiency. The present application designs a new target recognition method that can quickly identify dim targets under strong background noise during the day. After identifying the target, the deviation of the target angle change from the theoretical value can be quickly obtained, which is used for target pointing and distance deviation correction in future daytime space debris laser ranging.

[0063] Furthermore, the step of adjusting the time deviation includes:

[0064] By manually adjusting the time deviation, or setting a fixed time deviation adjustment for automatic adjustment, the position of the target in the guide camera will change.

[0065] Furthermore, the step of comparing the pixels of the image P0 at time T0 with the pixels of the image P1 at time T1, taking the pixel difference as image ΔP, searching ΔP, and obtaining the coordinates of the target centroid includes:

[0066] Subtract the grayscale values ​​of pixels at the same position to obtain motion information or changes;

[0067] Suppose the first frame image is G1(x,y), and the second frame image is G2(x,y), where x and y represent the horizontal and vertical coordinates of the image respectively. Compare the pixel-by-pixel difference results of the two frames D(x,y). The difference formula is:

[0068] D(x,y)=G2(x,y)-G1(x,y)

[0069] D_pos(x,y)=max(D(x,y),0)

[0070] Obtain the grayscale difference image D between the two frames;

[0071] Get the target in the image.

[0072] Specifically, the new image D obtained in this way is the grayscale difference image between the two frames, in which each pixel value reflects the change of the grayscale value at the corresponding position, and the target in the image is found.

[0073] Furthermore, the step of acquiring the target in the image includes:

[0074] Calculate image brightness: calculate the brightness value of each pixel;

[0075] Find the brightest area: find the local maximum, or segment the area with higher brightness through the threshold method, and then combine morphological operations such as corrosion and expansion to separate adjacent bright areas;

[0076] Select the first two brightest areas: sort the connected areas according to the calculated brightness statistics and select the first two areas with the highest brightness;

[0077] Marking and displaying results: Mark the selected area, then obtain the area attributes (such as area, centroid position, etc.), and superimpose the original image and the marked brightest area;

[0078] Calculate the coordinates of the center of mass of the region N1(x, y) and N2(x, y) to obtain the change in the horizontal and vertical coordinates of the center of mass ΔN A ′、ΔN E ′.

[0079] Furthermore, the step of determining the change in the center of mass coordinates and determining the target includes:

[0080] The obtained ΔN A ′、ΔN E ′ and ΔN A , ΔN E After comparison, if the difference is less than c, the obtained area is judged to be a valid target.

[0081] Testing and experimental verification:

[0082] 1. Experimental Verification

[0083] 1) The actual viewing angle of the camera used in this experiment is 20 arc minutes, and the pixel size of the image is 1080*1920;

[0084] 2) Track the target and collect image P0 at time T0 (such as Figure 2 shown);

[0085] 3) The azimuth increases by 2 arc minutes, and the elevation decreases by 2 arc minutes, corresponding to the pixel changes ΔNA=108 and ΔNE=192, which is recorded as time T1;

[0086] 4) Collect image P1 at time T1 (such as Figure 3 shown);

[0087] 5) Compare the difference between P0 and P1 to get ΔP (such as Figure 4 (shown)

[0088] 6) Extract the centroids, which are N1 (354, 518) and N2 (500, 404) (such as Figure 5 (shown)

[0089] 7) Get the pixel change ΔN A ′=146、ΔN E ′=114 Considering that the target changes from T0 to T1, the detection threshold is set to 100, and the detected target is judged to be a real target.

[0090] 2. Signal-to-noise ratio calculation

[0091] In terms of quantitative comparison, the local signal-to-noise ratio (LSNR) of the target is mainly used to measure the impact of contrast processing on target recognition. Obtain a set of P0, P1 and image ΔP, and extract the target area, such as Figure 6 As shown,

[0092] Calculate the signal-to-noise ratio LSNR. The LSNR calculation formula is:

[0093]

[0094] Among them, Ps represents the power of the signal, and Pn represents the power of the noise. Since the unit of SNR is decibel, the calculated result is in decibel.

[0095] Table 1 Local signal-to-noise ratio LSNR calculation results

[0096] image P0 P1 ΔP(1) ΔP(2) LSNR (dB) -3.0240 -5.0091 3.1015 3.8218

[0097] As shown in Table 1, it can be seen that the local signal-to-noise ratio of a single-frame image is below -3dB, and it is difficult to extract target information using existing methods. After image comparison processing, the local signal-to-noise ratio of the two frames of images is improved by more than 6dB.

[0098] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for quickly identifying dim targets of space debris based on daytime guide star images, characterized in that: The following steps are involved: Track the target according to the forecast value; The image of the target area is recorded as the image at time T0; Adjust the telescope pointing azimuth angle ΔA and elevation angle ΔE; The target area image is collected and recorded as the T1 time image; Compare the pixels of the image P0 at time T0 with the image P1 at time T1, take the pixel difference as image ΔP, search ΔP, and obtain the coordinates of the target centroid; Determine the change in the center of mass coordinates and determine the target; Calculate the target center of mass miss distance and adjust the direction; The step of adjusting the telescope pointing azimuth angle ΔA and elevation angle ΔE comprises: The target moving orbit is an approximate circular orbit. The camera field angle γA, γE and the telescope pointing angle change ΔA, ΔE. The image pixel N A 、N E , pixel change ΔN A , ΔN E The relationship between can be calculated by the following formula: ， ； Calculate the target center of mass coordinates , , get the change of the horizontal and vertical coordinates of the center of mass , ; Will get , Respectively , After comparison, if the difference is less than c, the obtained area is judged to be a valid target.

2. The method for rapid identification of space debris dim targets based on daytime guide star images according to claim 1 is characterized in that: The steps of comparing the pixels of the image P0 at time T0 with the pixels of the image P1 at time T1, taking the pixel difference as image ΔP, searching ΔP, and obtaining the target centroid coordinates include: Subtract the grayscale values ​​of pixels at the same position to obtain motion information or changes; Suppose the first frame image is G1(x, y) and the second frame image is G2(x, y), where x and y represent the horizontal and vertical coordinates of the image respectively. Compare the two frames of images pixel by pixel and find the difference result D(x, y). The comparison and difference formula is: D(x, y) = G2(x, y) - G1(x, y) D_pos(x, y) = max(D(x, y), 0) Obtain the grayscale difference image D between the two frames; Get the target in the image.

3. The method for rapid identification of space debris dim targets based on daytime guide star images according to claim 2 is characterized in that: The step of acquiring the target in the image comprises: Calculate image brightness: calculate the brightness value of each pixel; Find the brightest area: find the local maximum, or segment the area with higher brightness through the threshold method, and then combine morphological operations such as corrosion and expansion to separate adjacent bright areas; Select the first two brightest areas: Sort the connected areas according to the calculated brightness statistics and select the first two areas with the highest brightness; Marking and displaying results: Mark the selected area, obtain the area attributes, and overlay the original image with the marked brightest area.

Images