CN112927294B - Satellite orbit and attitude determination method based on single sensor - Google Patents
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Abstract
The invention discloses a satellite orbit and attitude determination method based on a single sensor, which comprises the steps of firstly establishing an earth panoramic infrared image model, obtaining an earth panoramic infrared reference image, obtaining a ground infrared fisheye image shot by an infrared earth sensor, obtaining a candidate subregion feature matching image by applying a Harris corner detection algorithm, a weighted Voronoi image and a K-means algorithm, and establishing a subregion matching database by using a multi-scale matching region screening method; and extracting the feature points of the satellite image by using an SIFT algorithm, and matching the feature points with the database to obtain corresponding matching pairs. And after fisheye correction processing is carried out on the matched image point coordinates, a satellite orbit and attitude determination observation model equation is established, and satellite position and attitude information is obtained by resolving through a least square method and an RANSAC algorithm. The invention is based on a single sensor, can independently acquire the position and attitude information of the satellite in real time in all weather, and meets the current development and development trend of the microsatellite.
Description
Technical Field
The invention relates to the technical field of satellite control, in particular to a satellite orbit and attitude determination method based on a single sensor.
Background
With the deepening of the research of aerospace science and technology, the satellite develops towards the direction of smaller volume, more intellectualization and lower energy consumption. The microsatellite has the advantages of smaller volume, lighter weight, low development cost, high emission frequency and the like, and has good development prospect and application prospect. With the increasing of space missions and the increasing of difficulty, in order to better realize the space missions, the mobility of the microsatellite needs to be improved, and a large factor influencing the mobility of the microsatellite is an attitude sensor.
The attitude sensors are in various types, such as sun sensors, earth sensors, star sensors, gyroscopes and the like. When the satellite is covered by the earth shadow, the sun sensor and the visible light earth sensor cannot work; the star sensor is easily interfered by light sources such as a solar light source and the like due to factors such as the characteristics of the device, and the like, and the star sensor is high in development cost and shorter in service life than the earth sensor; the static infrared earth sensor has light weight, high detection precision, low energy consumption and long service life, can continuously observe the earth all weather, and is an attitude sensor capable of reliably working on microsatellites.
In the traditional satellite attitude determination, a plurality of attitude sensors are combined to work together, so that the attitude information of the satellite is obtained. The method has the defects of large volume, heavy weight and higher power consumption of equipment, and the image obtained by the sensor has abundant and various information which is not fully utilized.
The method for acquiring the pose information of the carrier based on the image information is initially applied to the field of robots. With the improvement of the real-time processing capability of the spaceborne computer and the shooting capability of the optical carrier, the application of the image information in the aerospace field is wider.
The method for acquiring the pose information of the satellite by utilizing the earth infrared panoramic image shot by the panoramic infrared earth sensor and carrying out technical processing on the infrared image has important scientific research significance, practical application value and future potential development prospect.
Disclosure of Invention
The invention aims to provide an orbit and attitude determination method based on an infrared earth sensor, which replaces the traditional attitude determination mode combining a plurality of sensors, fully utilizes image information, utilizes an established subgraph database and a fisheye image correction method, reduces the influence of infrared fisheye image distortion, low resolution and edge compression image information, realizes the aim of independently obtaining the relative attitude information of a satellite in real time in all weather, ensures the successful completion of a space mission and ensures the normal work of the satellite.
The technical scheme of the invention is as follows:
the invention discloses a satellite orbit and attitude determination method based on a single sensor, which comprises the following steps:
1) establishing an earth infrared image model to obtain an earth panoramic infrared radiation reference image;
2) determining internal parameters of a camera of an imaging system of the infrared earth single sensor, and acquiring a ground infrared fisheye image shot by the single infrared earth sensor;
3) based on an earth infrared image model, obtaining candidate subregion characteristic images by using a Harris corner algorithm, a weighted Voronoi diagram and a K-means clustering algorithm; screening by using a multi-scale matching area selection method to obtain a sub-area characteristic database;
4) extracting characteristic points of the shot ground infrared fisheye image, and matching the characteristic points with a subregion characteristic database to obtain corresponding characteristic point matching pairs; the method comprises the steps that a ground infrared fisheye image with small information amount and basically ocean in a central area is corrected and matched with a subregion feature database to obtain feature point matching pairs;
5) firstly, correcting coordinates of fisheye image points of the obtained feature point matching pairs, establishing a satellite orbit and attitude determination observation model equation, and obtaining an observation equation between a ground target point and image points of the corrected feature point matching pairs;
6) based on an observation equation, external parameter calculation is carried out by utilizing corrected feature point matching pairs and combining a least square method and a RANSAC algorithm, and the three-dimensional position and attitude angle information of the satellite is estimated.
Preferably, the earth infrared image model established in step 1) specifically includes: and acquiring a global infrared radiation value in a waveband range of 8-14 mu m, and generating an earth panoramic infrared radiation reference image with a spatial resolution of less than 1 km.
Preferably, the determining of internal parameters of the camera in step 2) is specifically: the focal length, the field size, the image principal point, the resolution of the infrared camera and the physical dimensions of a single image element of the camera in the x and y directions respectively.
Preferably, the acquiring of the infrared fisheye image shot by the single infrared earth sensor in the step 2) specifically includes: and when the satellite is at a certain position and posture, the infrared earth sensor shoots a fisheye infrared radiation image of a certain area.
Preferably, the step 3 specifically comprises: obtaining global candidate corners by using a Harris corner algorithm, clustering the candidate corners by using a K-means algorithm, taking the maximum response value in each category as the weight of a weighted Voronoi picture, and determining the number of sub-regions by using the weighted Voronoi picture; and finally, screening the images of the sub-regions by using a multi-scale matching region selection method, calculating the variance, the information entropy and the edge density of the images of the sub-regions, reserving the sub-regions meeting the set threshold value, and generating a sub-region characteristic database.
Preferably, the step 4) specifically comprises: and describing image feature points by adopting an SIFT algorithm, matching the feature points with the sub-region feature database, and extracting matching pairs.
Preferably, in the step 4), the ground infrared fisheye image with a small information amount and a substantially ocean central area is corrected by the fisheye image, and then matched with the sub-area feature database to obtain the feature point matching pair, specifically:
after the internal parameters of the camera are known, the ground infrared fisheye image with less information content and basically ocean in the central area is corrected, a cubic spline interpolation method is used for obtaining the corrected image, and then characteristic point matching is carried out.
Preferably, the step 5) is specifically: after the internal parameters of the camera are known, the coordinates of the fisheye image points of the feature point matching pairs are corrected, and an observation equation of the corrected imaging ground target point, the image points of the feature point matching pairs and the light points, namely a relation equation of the feature point matching pairs is established.
Preferably, the step 6) is specifically: setting iteration times and confidence coefficients, randomly selecting a plurality of pairs of feature point matching pairs, substituting the pairs into an observation equation, solving a transformation matrix of the satellite by using a least square method, testing other points in the feature point matching pairs by using the transformation matrix, and calling the points meeting the transformation matrix as inner points and the points not meeting the transformation matrix as outer points; selecting a transformation matrix with the largest number of interior points, and re-estimating the transformation matrix by using all the interior points; and extracting the attitude and position matrix of the satellite from the re-estimated transformation matrix, and solving to obtain the three-dimensional coordinate and attitude angle of the satellite so as to realize orbit and attitude determination of the satellite.
Compared with the prior art, the method for determining the orbit and the attitude based on the infrared earth sensor has the advantages that:
(1) the invention is different from the traditional method for fixing the orbit by combining a plurality of sensors, can overcome the defects of large volume, heavier mass and high power consumption in the prior art, fully utilizes rich information in the image, realizes the orbit and attitude fixing of the satellite by only utilizing a single sensor, and meets the development trend of the microsatellite.
(2) The invention adopts the static earth infrared sensor, has light weight, high detection precision, low energy consumption and long service life, is not influenced by seasons and day and night, and can carry out all-weather uninterrupted observation on the earth.
(3) The method for establishing the sub-area database to be matched can be suitable for infrared image matching with low resolution, and the number of successfully matched feature points is doubled, so that the precision is improved.
(4) The invention can overcome the defects of fisheye distortion, low resolution and edge information compression of the earth infrared fisheye image and realize the acquisition of satellite pose information.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 illustrates an isometric imaging model of a fisheye lens;
FIG. 3 is a weighted Voronoi diagram;
Detailed Description
The invention will be further illustrated and described with reference to specific embodiments. The technical features of the embodiments of the present invention can be combined correspondingly without mutual conflict.
The invention provides an infrared earth sensor-based orbit and attitude determination method, namely a method for determining the position and attitude of a satellite by carrying out technical processing on an infrared radiation image shot by the satellite and extracting a characteristic vector based on an earth infrared radiation panoramic model. As shown in fig. 1, the method comprises the steps of:
(1) establishing an earth infrared image model, namely an earth panoramic infrared radiation model; and acquiring a global infrared radiation value in a waveband range of 8-14 mu m, and generating an earth panoramic infrared radiation reference image with a spatial resolution of less than 1 km.
(2) The method comprises the steps of determining internal parameters such as focal length, resolution, pixel size, image principal point and the like of a camera of an imaging system of the infrared earth single sensor, and acquiring a panoramic infrared image of the earth shot by the single infrared earth sensor at a certain position and posture. The fish-eye isometric projection model is as shown in fig. 2, and an observation model between a ground target point and an image point is established:
rd＝fθ
the conversion relation between the target point P and the corresponding image point P is the conversion between the coordinate systems, and not only involves the translation, rotation and scaling of the coordinate systems. Let longitude of satellite be alpha, latitude be delta, and pitch angle be
The rolling angle is theta, the course angle is gamma, and then the equatorial earth center inertial coordinate system Ow-XwYwZwAnd the pixel coordinate system O-UV, namely the relation between the image point coordinates and the ground target point coordinates:
θ＝arctan(r)
Rt＝RAR,
(xw,yw,zw) Is the coordinate of the earth center inertia equator coordinate system with the ground target point (x)0,y0,z0) Is the three-dimensional elevation coordinate of the satellite, (u, v) is the coordinate of the target point in the pixel coordinate system, RtIs a transformation matrix of a camera coordinate system and an equatorial inertial geocentric coordinate system, RAIs the mounting matrix of the camera relative to the carrier, and R is the conversion matrix of the equatorial inertial geocentric coordinate system and the carrier platform.
f is the focal length of the camera, dx and dy are the unit pixel sizes of the CMOS in the x-axis and y-axis directions respectively, (c)x,cy) The coordinates of the origin of the image coordinate system in the pixel coordinate system, i.e. the principal point coordinates, are indicated. In the present invention, it is considered that the mounting matrix of the camera is known.
(3) And based on the global infrared radiation image, obtaining global candidate angular points by using a Harris angular point detection algorithm. The core of the Harris corner detection algorithm is that if the gray scale change value in a certain fixed window is large in any direction, a corner exists in the window. The gray scale change before and after sliding (u, v) in the window is described as follows
Simplifying Taylor expansion to obtain
Wherein IxAnd IyThe gradients of the (x, y) image points in the x-direction and the y-direction, respectively. The eigenvalues of the matrix M represent the principal curvatures of the autocorrelation function, with which the response value R of a certain corner can be measured,
R＝det(M)-k(traceM)2
wherein k is a constant value of experience, and is generally 0.04-0.06. Summarizing the Harris corner detection steps as follows:
a) calculating the gray gradients of the image in the x direction and the y direction by using a Sobel operator with a good expression effect;
b) filtering by using a Gaussian function (sigma is 2) to obtain a matrix M, and calculating a Harris response value R of each pixel point;
c) carrying out non-maximum suppression on the candidate points by using a 3X3 mask;
d) and extracting pixel points with Harris response values larger than a certain threshold value, and finally obtaining a candidate corner point set.
(4) In order to distribute the database uniformly around the world, the number of sub-images is determined using a weighted Voronoi diagram. The Voronoi diagram is widely applied to the fields of computer graphics, geophysics, epidemiology, meteorology and the like, and the weighted Voronoi diagram can reflect the influence range of each main point more truly.
At a cluster set s ═ p1,p2,…,pnIn (b) }, p to piDefined as the weight Euclidean distance
λiIs piD (p, p) ofi) Is p to piThe euclidean distance. p is a radical ofiIs defined as V (p)i)＝∩j≠i{p|D(p,pi)＜D(p,pj) 1 … n, the resulting weighted Voronoi diagram is shown in fig. 3.
In order to ensure the relevance of the feature points in the sub-regions, such as lakes which are not segmented, the corner clustering is carried out by adopting K-means. Setting an initial value N of the number of sub-region images, and dividing the extracted corner points into N types by using a K-means clustering mode. And taking the maximum response value in each category as a weighting factor of the region to obtain a weighted Voronoi diagram. The corner points of the infrared image are generally positioned at sea-land boundaries or lake-land boundaries, and the influence area of the ocean needs to be excluded when calculating the influence area. And calculating the minimum ratio of the influence area of each type to the influence area, increasing the number of clusters when the maximum ratio is larger, stopping when the maximum ratio is near 1, and finally generating the sub-images by taking each type of corner point as a whole.
(5) The matching subimages are characterized by obvious features, large information amount and good adaptability. In order to select stable and effective subimages, the subimages generated by the method are screened by using a multi-scale matching area selection method.
a) Image variance Var: the image variance refers to the variance of the gray value of the image, the variance reflects the change condition of the whole image gray value, the information content of the image can be reflected visually, the large variance indicates that the terrain feature of the area is obvious, the terrain fluctuation changes violently, and the small variance indicates that the image has poor adaptability.
b) Information entropy H: entropy is a measure of uncertainty in an image, reflecting the amount of information averaged over the image. Is calculated by the formula
Wherein P isijIs the probability that different gray values appear in the image, M, N is the image size.
c) Edge density EDV: the edge density is used to determine whether the edge features of the image are concentrated. The edge density of the smooth area is small, and therefore the edge density reflects the amount of image information. The calculation formula is as follows:
where edge refers to the number of edges in the image.
Through a plurality of experiments, the threshold values of the indexes are set as follows:
Var>20
H>2
EDV>0.12
(6) the SIFT algorithm has good stability and invariance, and is not interfered by visual angle change, affine change and noise to a certain extent. And identifying the characteristic points of the infrared fisheye image and the reference image by using a Gaussian differential function, performing fine model fitting on each characteristic point, and determining the position and the scale of the characteristic points. And (4) allocating a direction to each feature point to ensure that the feature points have invariance. By calculating the local gradient of each feature point, a feature vector describing the feature point is obtained. And calculating the similarity between the infrared image and the characteristic points of the matched images based on the Euclidean distance, and finding out the characteristic point matching pairs corresponding to the two images.
(7) For an image with a small amount of information and a central area which is basically ocean, since image information of edges is compressed, the image needs to be subjected to distortion removal processing, namely correction processing, so as to expand the edge information. With known camera intrinsic parameters, the relationship between the image point (u, v) and the corrected point (x, y) is:
since the image is discrete and the corrected coordinates are non-integer coordinates, it is necessary to map the non-integer coordinates to integer coordinates. The mapping method includes a forward mapping and a backward mapping, and the backward mapping is usually adopted. The corrected images obtained by different mapping methods have different effects, and a cubic spline interpolation algorithm with higher complexity and optimal interpolation effect is adopted. And matching the corrected image with a reference image database to obtain a feature point matching pair.
(8) By using the extracted feature point matching pairs, unknown external parameters are resolved by using a least square method and a RANSAC algorithm, and the position and attitude information of the satellite is estimated, which specifically comprises the following steps:
firstly, correcting the characteristic points extracted from the infrared fisheye image, wherein the correction formula is shown as the step.
P is the three-dimensional coordinate vector of the ground target point, P0Is the three-dimensional coordinate vector of the satellite, p is the coordinate obtained after correction, K is the internal parameter matrix of the camera, M is the rotation matrix, let K1,k2,k3Is K-1A row vector of h1,h2,h3Respectively row vectors of H. The relationship between the corrected image point vector and the ground target point vector is as follows:
the above formula is rewritten as:
h has 12 unknown parameters, and because the R matrix is an orthogonal matrix and the equation set is a homogeneous equation set, only 8 degrees of freedom exist, at least four pairs of characteristic points form at least 8 equations, and the value of H is solved. Is provided withKnowing that the n pairs of matching points are (P)1,p1(u1,v1))，...(Pn,pn(un,vn) P' h is 0)
Wherein
Calculating P′TThe minimum eigenvalue of the P' matrix and its corresponding eigenvector, then the solution h of the equation can be obtained, where h satisfies ρ | h |F1, the rotation matrix R and the translation vector t can be obtained, and the three-dimensional vector t of the satellite0Then is t0＝-R-1×t。
(9) And after the transformation matrix is obtained through calculation, optimizing by adopting an RANSAC algorithm. And the elimination by using the RANSAC algorithm when the satellite pose information is solved is higher than the elimination solving precision when the satellite pose information is matched. The RANSAC algorithm uses a model to test other points in the data set by iteratively selecting a random set of subsets in the data set, fitting the model to satisfy the subsets. Points that satisfy the model are called interior points and those that do not satisfy are called exterior points. Repeating the process, selecting the model with the most interior points, and re-estimating the model by using all the interior points. And finally, evaluating the model by estimating the error rate of the local interior point and the model. The method for solving the model is as described in step (8).
(10) The longitude and latitude of the satellite can be defined by t0＝(t1,t2,t3)TIs calculated to obtain
Let the attitude angle matrix be R1Then, then
Let R1Is composed of
Then
The working principle of the invention is as follows:
based on the established earth infrared model with the spatial resolution of 1km, a method suitable for matching images with medium and low resolutions and generating a reference subimage with a certain size is provided, and a reference subimage matching database is established. And (3) carrying out image processing on the infrared fisheye image by using an SIFT algorithm, extracting feature points, carrying out feature point matching with an earth infrared panoramic reference image library, and extracting feature point matching pairs for orbit and attitude determination. After correction processing is carried out on the feature point coordinate image points of the infrared fisheye image, based on an observation equation between a ground target point and the corrected image points, a least square method is used for solving the observation equation, an RANSAC algorithm is adopted to improve observation precision, and longitude and latitude, elevation three-dimensional position information and pitching, rolling and yawing three-dimensional attitude information of the satellite are finally obtained, so that instantaneous orbit and attitude determination of the satellite are realized.
By adopting the method, the relative pose information of the satellite can be acquired in real time in all weather, and the satellite can independently work when some attitude sensors fail, thereby ensuring that the space mission is successfully completed and the satellite can normally work.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (6)
1. A satellite orbit and attitude determination method based on a single sensor is characterized by comprising the following steps:
1) establishing an earth infrared image model to obtain an earth panoramic infrared radiation reference image;
2) determining internal parameters of a camera of an imaging system of the infrared earth single sensor, and acquiring a ground infrared fisheye image shot by the single infrared earth sensor;
3) based on an earth infrared image model, obtaining candidate subregion characteristic images by applying a Harris corner algorithm, a weighted Voronoi image and a K-means clustering algorithm; screening by using a multi-scale matching area selection method to obtain a sub-area characteristic database;
the step 3 specifically comprises the following steps: obtaining global candidate corners by using a Harris corner algorithm, clustering the candidate corners by using a K-means algorithm, taking the maximum response value in each category as the weight of a weighted Voronoi picture, and determining the number of sub-regions by using the weighted Voronoi picture; finally, screening the images of the sub-regions by using a multi-scale matching region selection method, calculating the variance, the information entropy and the edge density of the images of the sub-regions, reserving the sub-regions meeting a set threshold value, and generating a sub-region feature database;
4) extracting characteristic points of the shot ground infrared fisheye image, and matching the characteristic points with a subregion characteristic database to obtain corresponding characteristic point matching pairs; the method comprises the steps that a ground infrared fisheye image with small information amount and basically ocean in a central area is corrected and matched with a subregion feature database to obtain feature point matching pairs;
5) firstly, correcting coordinates of fisheye image points of the obtained feature point matching pairs, establishing a satellite orbit and attitude determination observation model equation, and obtaining an observation equation between a ground target point and image points of the corrected feature point matching pairs;
the step 5) is specifically as follows: after the internal parameters of the camera are known, correcting the coordinates of the fisheye image points of the feature point matching pairs, and establishing an observation equation of the corrected imaging ground target point, the image points of the feature point matching pairs and the light points, namely a relation equation of the feature point matching pairs;
the observation equation is:
p is the three-dimensional coordinate vector of the ground target point, P0The three-dimensional coordinate vector of the satellite, p is a coordinate obtained after correction, K is a camera internal parameter matrix, and R is a rotation matrix;
6) based on an observation equation, utilizing corrected feature point matching pairs, combining a least square method and a RANSAC algorithm to carry out external parameter calculation, and estimating three-dimensional position and attitude angle information of the satellite;
the step 6) is specifically as follows: setting iteration times and confidence coefficients, randomly selecting a plurality of pairs of feature point matching pairs, substituting the pairs into an observation equation, solving a transformation matrix of the satellite by using a least square method, testing other points in the feature point matching pairs by using the transformation matrix, and calling the points meeting the transformation matrix as inner points and the points not meeting the transformation matrix as outer points; selecting a transformation matrix with the largest number of interior points, and re-estimating the transformation matrix by using all the interior points; and extracting the attitude and position matrix of the satellite from the re-estimated transformation matrix, and solving to obtain the three-dimensional coordinate and attitude angle of the satellite so as to realize orbit and attitude determination of the satellite.
2. The single-sensor-based satellite orbit and attitude determination method according to claim 1, wherein the earth infrared image model established in the step 1) is specifically: and acquiring a global infrared radiation value in a waveband range of 8-14 mu m, and generating an earth panoramic infrared radiation reference image with a spatial resolution of less than 1 km.
3. The single-sensor-based satellite orbit and attitude determination method according to claim 1, wherein the internal parameters of the phase determination engine of step 2) are specifically: the focal length, the field size, the image principal point, the resolution of the infrared camera and the physical dimensions of a single image element of the camera in the x and y directions respectively.
4. The single sensor-based satellite orbit and attitude determination method according to claim 1, wherein the acquiring of the infrared fisheye image shot by the single infrared earth sensor in the step 2) specifically comprises: and acquiring a fisheye infrared radiation image of a certain area shot by the infrared earth sensor when the satellite is at a certain position and posture.
5. The single-sensor-based satellite orbit and attitude determination method according to claim 1, wherein the step 4) is specifically: and describing image feature points by adopting an SIFT algorithm, matching the feature points with the sub-region feature database, and extracting matching pairs.
6. The single-sensor-based satellite orbit and attitude determination method according to claim 1, wherein in the step 4), the fisheye image correction is performed on the ground infrared fisheye image with small information amount and basically ocean central area, and then the ground infrared fisheye image is matched with the sub-area feature database to obtain the feature point matching pairs, specifically:
after the internal parameters of the camera are known, the ground infrared fisheye image with less information content and basically ocean in the central area is corrected, a cubic spline interpolation method is used for obtaining the corrected image, and then characteristic point matching is carried out.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2016149006A (en) * | 2015-02-12 | 2016-08-18 | アジア航測株式会社 | Change detection system using aerial mobile acquisition information, change detection method using aerial mobile acquisition information, and program of the same |
| CN106504286A (en) * | 2016-08-20 | 2017-03-15 | 航天恒星科技有限公司 | Satellite image localization method and device |
| WO2017114581A1 (en) * | 2015-12-30 | 2017-07-06 | Telecom Italia S.P.A. | System for generating 3d images for image recognition based positioning |
| CN108981721A (en) * | 2018-07-24 | 2018-12-11 | 浙江大学 | A kind of static infrared earth sensor target angle that determining appearance for micro-nano satellite determines method |
| CN109784189A (en) * | 2018-12-19 | 2019-05-21 | 中国人民解放军战略支援部队航天工程大学 | Video satellite remote sensing images scape based on deep learning matches method and device thereof |
| CN111045068A (en) * | 2019-12-27 | 2020-04-21 | 武汉大学 | Low-orbit satellite autonomous orbit and attitude determination method based on non-navigation satellite signals |
| CN111121789A (en) * | 2019-12-12 | 2020-05-08 | 上海航天控制技术研究所 | Image-based remote sensing satellite multi-mode autonomous orbit determination method |
| CN112066879A (en) * | 2020-09-11 | 2020-12-11 | 哈尔滨工业大学 | Air floatation motion simulator pose measuring device and method based on computer vision |
-
2021
- 2021-01-27 CN CN202110110261.9A patent/CN112927294B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2016149006A (en) * | 2015-02-12 | 2016-08-18 | アジア航測株式会社 | Change detection system using aerial mobile acquisition information, change detection method using aerial mobile acquisition information, and program of the same |
| WO2017114581A1 (en) * | 2015-12-30 | 2017-07-06 | Telecom Italia S.P.A. | System for generating 3d images for image recognition based positioning |
| CN106504286A (en) * | 2016-08-20 | 2017-03-15 | 航天恒星科技有限公司 | Satellite image localization method and device |
| CN108981721A (en) * | 2018-07-24 | 2018-12-11 | 浙江大学 | A kind of static infrared earth sensor target angle that determining appearance for micro-nano satellite determines method |
| CN109784189A (en) * | 2018-12-19 | 2019-05-21 | 中国人民解放军战略支援部队航天工程大学 | Video satellite remote sensing images scape based on deep learning matches method and device thereof |
| CN111121789A (en) * | 2019-12-12 | 2020-05-08 | 上海航天控制技术研究所 | Image-based remote sensing satellite multi-mode autonomous orbit determination method |
| CN111045068A (en) * | 2019-12-27 | 2020-04-21 | 武汉大学 | Low-orbit satellite autonomous orbit and attitude determination method based on non-navigation satellite signals |
| CN112066879A (en) * | 2020-09-11 | 2020-12-11 | 哈尔滨工业大学 | Air floatation motion simulator pose measuring device and method based on computer vision |
Non-Patent Citations (4)
| Title |
|---|
| Design, analysis and optimization of random access inter-satellite ranging system;XU Xiaoyi 等;《Systems Engineering and Electronics》;20201031;第31卷(第5期);全文 * |
| Infrared Earth sensor with a large field of view for low-Earth-orbiting micro-satellites;Hao WANG 等;《Technology & Electronic Engineering》;20200720;第22卷(第2期);全文 * |
| Satellite Attitude Determination and Map Projection Based on Robust Image Matching;Toru Kouyama 等;《Remote Sensing》;20170120;第9卷(第1期);全文 * |
| 测绘一号卫星相机的光谱和辐射定标;万志 等;《光学精密工程》;20151231;第23卷(第7期);全文 * |
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