CN112926237A - Luminosity signal-based space target key feature identification method - Google Patents
Luminosity signal-based space target key feature identification method Download PDFInfo
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Abstract
The invention discloses a luminosity signal-based space target key feature identification method, which comprises the following steps of: step 1: setting an initial state of a space target, predicting the position of the space target based on a finite element model and a solar radiation compression model, and estimating the posture of the space target according to a posture control mode; step 2: analyzing the surface element visibility of the space target surface; and step 3: predicting a space target photometric signal based on a bidirectional reflectivity distribution function, and correcting the space target state predicted in the step 1 according to an actual observation value; and 4, step 4: estimating an observed value of the corrected parameter based on the space target state in the step 3, and correcting an overfitting phenomenon possibly existing in the step 3 by using an actual observed value; and 5: and (4) repeating the steps 1 to 4 based on the space target parameters estimated in the step 4, updating and obtaining the position and the posture of the space target, and realizing the identification of the key features of the space target.
Description
Technical Field
The invention relates to the technical field of spatial information detection, in particular to the technical field of identification of key features of a spatial target based on photometric signals.
Background
The difference between the orbit period and the earth rotation period of medium and high orbit satellites such as geosynchronous orbit satellites is small, the earth surface targets can be stared at for a long time under the condition that background image motion is not generated, the working types and the working states of space targets such as the medium and high orbit satellites can be accurately identified, and the method has important significance for space information detection.
The ground-based optical system is not restricted by space platform resources, has better optical information acquisition and processing capability than a space-based optical system, is lower in use cost, and is a main means for monitoring a space target. The distance of ground observation is long, the imaging resolution of an optical system is low, and the optical system is easily influenced by uncontrollable factors such as illumination, attitude and orbit change of non-cooperative targets and the like; in this case, it is generally difficult to effectively acquire the spatial target feature, so the optical image-based research method has certain limitations. The photometric signal is an energy signal, contains key feature information such as the position, the posture, the orbit and the like of the space target, has higher sensitivity to the change of the information, and is suitable for identifying the key features of the space target.
Disclosure of Invention
In order to solve the technical problem, the invention provides a method for identifying key features of a spatial target based on photometric signals, which comprises the following steps:
step 1: setting an initial state of a space target, predicting the position of the space target based on a finite element model and a solar radiation compression model, and estimating the posture of the space target according to a posture control mode;
step 2: analyzing the surface element visibility of the space target surface;
and step 3: predicting a space target photometric signal based on a bidirectional reflectivity distribution function, and correcting the space target state predicted in the step 1 according to an actual observation value;
and 4, step 4: estimating an observed value of the corrected parameter based on the space target state in the step 3, and correcting an overfitting phenomenon possibly existing in the step 3 by using an actual observed value;
and 5: and (4) repeating the steps 1 to 4 based on the space target parameters estimated in the step 4, updating and obtaining the position and the posture of the space target, and realizing the identification of the key features of the space target.
The invention has the beneficial effects that: according to the phase angle and luminosity curves of the medium and high orbit space targets observed by the foundation photoelectric telescope, the phase angle and luminosity data are fused by a nonlinear filtering method, and synchronous estimation of motion parameters such as the position, the posture and the speed of the space target and characteristic parameters such as the quality, the shape and the albedo is realized.
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FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The technical solution of the present invention will now be fully described with reference to fig. 1. The following description is merely exemplary of some, but not all, embodiments of the present invention. All other embodiments obtained by those skilled in the art without any inventive step are within the scope of the present invention.
The method for identifying the key features of the space target based on the luminosity signal comprises the following steps:
step 1: setting an initial state of a space target, predicting the position of the space target based on a finite element model and a solar radiation compression model, and estimating the posture of the space target according to a posture control mode;
step 2: analyzing the surface element visibility of the space target surface;
and step 3: predicting a space target photometric signal based on a bidirectional reflectivity distribution function, and correcting the space target state predicted in the step 1 according to an actual observation value;
and 4, step 4: estimating an observed value of the corrected parameter based on the space target state in the step 3, and correcting an overfitting phenomenon possibly existing in the step 3 by using an actual observed value;
and 5: and (4) repeating the steps 1 to 4 based on the space target parameters estimated in the step 4, updating and obtaining the position and the posture of the space target, and realizing the identification of the key features of the space target.
Preferably, in step 1, the spatial target position and attitude prediction and estimation steps are as follows:
(1) predicting nonlinear system state parameters and observed values through lossless transformation;
(2) the numerical value transmission and the updating are completed by fitting the mean value and the covariance of the state parameters of the nonlinear system;
(3) constructing a motion updating model of the attitude quaternion;
(4) and updating the space target attitude according to the track position and the track speed.
Preferably, in step 2, the bin visibility analysis step of the spatial target surface is as follows:
(1) finite element division is carried out on a geometric model of the space target, and surface element parameters are derived;
(2) determining a shielding relation between surface elements based on the positions of corner points of the surface elements and the directions of the light clusters;
(3) and determining the visible surface elements and the visibility thereof based on the shielding relation among the surface elements.
Preferably, in step 3, the step of correcting the photometric signal and the predicted state value of the spatial target includes the following steps:
(1) based on the solar radiation illumination, calculating the radiation illumination generated by the visible surface element at the entrance pupil of the ground-based detector to obtain a single-frame luminosity signal;
(2) predicting a single-frame photometric signal of the space target based on the motion parameters of the space target and by combining the position parameters of the sun and the optical detector, the position of the space target and the attitude parameters;
(3) and (3) correcting the space target state based on the photometric signal predicted in the step (2) and the actual observation value.
Preferably, in step 4, the overfitting phenomenon correction step of estimating and correcting the observation value is as follows:
(1) determining a quantitative expression of an overfitting correction scale based on a linear transformation hypothesis aiming at a position correction process;
(2) and determining a quantitative expression of the overfitting correction scale based on a linear transformation hypothesis aiming at the posture correction process.
Example 1
1. Prediction of spatial target position and attitude
If the influence of the gravitation of other celestial bodies is ignored and only the perturbation influence of the solar radiation pressure on the space target track is considered, the acceleration expression of the space target is
In the formula, aperIs the acceleration caused by the solar radiation pressure, μ is the earth's gravitational constant, and r is the distance from the spatial target to the geocenter.
Quaternion is defined as
q＝[q0 μT]T
Wherein q is0And μ is defined as
Wherein ν is an Euler rotation angle,
is the euler axis of rotation.
Most space targets do not have posture adjustment capability and belong to a spin stabilization system. The spin-stable space object rotates around a fixed rotating shaft at a constant speed, and the motion of the attitude quaternion of the object is updated into a model
For any one 3 × 1 vector a, the expression of [ ax ] is
The three-axis stable spatial target attitude is related to its position and instantaneous velocity. Taking the satellite staring at the intersatellite point as an example, the object specimen body coordinate system is enabled to point to the movement direction along the x axis, point to the intersatellite point direction along the z axis, and form a right-hand coordinate system along the y axis, the x axis and the z axis to form a body coordinate system of a space object, so that the attitude model of the three-axis stabilized satellite pointing to the intersatellite point is as follows:
β＝γ×α
R＝[α β γ]T
wherein r iskAnd vkThe position and instantaneous velocity of the spatial object.
The spatial target trajectory position and velocity are then updated as follows:
according to the calculation process, the space target state parameters at each moment are updated, iteration is completed by combining a single-frame simulation process, and continuous frame photometric signal curve simulation is realized.
The quality and position of the spatial target at the time k correspond to the observed value at the time k, and the existence of the orbit dynamics and attitude control system can influence the observed value at the next time. Therefore, the target state parameter at the time k +1 can be estimated through the target state parameter at the time k, and then the predicted target state parameter at the time k +1 is corrected based on the actual observed value at the time k +1 until the residual error between the predicted observed value and the actual observed value is smaller than the set threshold value.
The method for inverting the space target shape characteristic information by utilizing photometric data mainly comprises a Gaussian surface density method, a geometric model matching method, a vector method based on nonlinear filtering and a multi-model self-adaptive estimation method. The shape inversion method based on the nonlinear filtering technology is wide in application range, small in error and good in robustness.
The nonlinear filtering method takes the target posture isoparametric inversion as the filtering estimation problem of a nonlinear dynamic system, estimates the state of the target by the observed values such as photometric data and the like, can accurately solve the posture inversion problem of the space target, and is a research hotspot of the current motion information inversion method.
By using the nonlinear filtering method, the problem of posture inversion of the space target can be solved more accurately. The prediction of the nonlinear system state parameters and the observed values used by the method can be completed through lossless transformation according to the mean value of the state parameters x
And covariance mean P, using lossless transform to obtain { χi}. If symmetric sampling is adopted, the state parameter formula is
In the point set obtained by sampling in the method, each point has a weight corresponding to the point and is used for re-fitting to a new state parameter and covariance distribution, and the calculation formula of the weight is
Where the parameter alpha is used for regulating miningThe larger the value of the relation between the sampling point and the average value point is, the smaller the influence of the average value point on the sampling point is; the parameter β is used to fit the high order error of the taylor expansion, and for gaussian distribution, the value of this parameter is taken to be 2. For the above point set, y is obtained after passing through a nonlinear systemiThereafter, the mean value thereof can be fitted by the following formula
Sum covariance PyyAnd completing the transmission and updating of the state parameter mean value and the distribution covariance of the nonlinear system:
for attitude estimation, the quaternion has a constraint of modulo length 1. To satisfy the above constraints and not destroy the physical significance of the sampling point, an intermediate variable, the rogowski parameter δ P, is introduced. And sampling the attitude by using the parameter to obtain a sampling quaternion, and representing the distance between the sampling point and the quaternion mean value. The interconversion between quaternion and Rodrigues parameters is formulated as
Where a is a parameter ranging from 0 to 1, and f is 2(a + 1).
The state parameters of the space target are divided into global parameters and local parameters, and the global parameters are quaternion xkCharacterizing attitude, local parameter δ xkPose was characterized with the rodgers parameter:
xk＝[q0 q1 q2 q3 ωx ωy ωz]T
δxk＝[p1 p2 p3 ωx ωy ωz]T
the local error Rodrigue parameter is set to mean [ 000 ] per frame estimation]Can be obtained by lossless conversion
The local error quaternion can be calculated by
δμ＝f-1(a+δq0)δp
The local error quaternion represents the sampling distance between the sampling point and the mean value, and the calculation formula of the global parameter is
After the calculation process, the state updating and the state observation are carried out by the global quaternion, and the updated prediction local error quaternion calculation formula is
2. Bin visibility analysis of spatial target surfaces
Certain shielding relation exists between the geometric surface elements, and the shielding relation can be obtained by calculating through a triangular ray method. The method comprises the steps of firstly calculating an intersection point of a space ray and a plane where a triangular surface element is located, and then judging whether the intersection point is located inside the surface element. When the intersection point position is judged, any one edge of the triangular surface element is selected, and whether the intersection point and the surface element vertex opposite to the edge are both positioned on the same side of the edge is verified; each side of the triangular surface element is selected in turn, and if the intersection point and the surface element vertex are always located on the same side, the intersection point is known to be located inside the surface element.
The solar radiation is far away from a space target and can be regarded as parallel light, so that the light ray cluster is simplified into a plurality of light rays with different point sources and the same direction, and the parallel light rays have the following mathematical relationship:
Om＝Cm-tDm
Dm＝uSun
wherein, OmIs a source of light m, DmIs the unit direction vector of the light ray m,
is the intersection of the ray m and the bin n, CmIs the center coordinate of the bin in the through coordinate system, uSunIs the unit direction vector of the sun pointing to the center of the target.
3. Photometric signal and state prediction correction for spatial target
The brightness of the radiation reflected by the spatial target may be calculated by:
wherein k isaBeing ambient light, RdIn order to have a diffuse reflectance ratio,
is a vector pointing to the m-th light source, N is the normal vector of the bin, RsIn order to be a specular reflection index,
the unit vector of the center of the surface element pointing to the detector, and alpha is the specular reflection coefficient.
Is composed of
The specular reflection direction vector of (1) is calculated as
And simulating the space target photometric signal by adopting a finite element idea. Firstly, establishing a space target geometric model as a basis for finite element division; then carrying out finite element division on the model, and deriving parameters of each surface element of the target; and finally, describing a finite element expression model of the space target in a J2000 coordinate system through quaternion.
The quaternion and rotation matrix may be interconverted. And obtaining a rotation matrix from the quaternion, converting the target position coordinate from the body coordinate system to a J2000 coordinate system, and then, left-multiplying the inverse matrix of the rotation matrix. The conversion formula of the corner point coordinates of the finite element model is
Then, for each visible surface element, the energy of the sun in the visible light wave band is integrated to obtain the radiance E at the surface of the sun0Then, the radiation illuminance E of the sun at the space target can be obtained according to the light energy transmission formula
Wherein r is0Distance of space target to sun, R0The solar radius. The irradiance of each visible surface element generated at the entrance pupil of the ground-based detector is calculated as
Where n is the bin normal vector, k1Unit vector, k, for the center of the bin pointing towards the light source2Unit vector of bin center pointing to detector, rObs,iAs a vector from the object in space to the detector, AiIs the area of bin i, parameter ρTotal,iIs calculated as
In which h is k1And k2U and v are unit vectors orthogonal to each other in the plane of the surface element, nuAnd nvAnd respectively used for representing the strength of the reflection action in the u direction and the v direction.
Based on the orbital motion model, phase angle observation data can be calculated from the position coordinates of the spatial target. The phase angle observation data includes an altitude and an azimuth of the target to the ground based observation station coordinate system. Firstly, converting a Cartesian coordinate of a target under a ground inertial coordinate system into a coordinate under a coordinate system of a foundation observation station; and then calculating phase angle observation data of the target according to the converted coordinate parameters. The conversion relation between the target position coordinate under the coordinate system of the ground observation station and the target position coordinate under the earth inertia coordinate system is as follows:
wherein rho is a vector from the ground observation station to the space target in the ground inertial coordinate system, and theta and lambda are longitude and latitude angles of the ground observation station relative to the ground inertial coordinate system.
By actual observation
To correct the estimated parameter mean muk+1Sum distribution covariance
Is calculated as follows:
wherein K is Kalman gain and the expression is
Meanwhile, the calculation formula of the prediction state and the observation covariance is
The estimation result of the observation may have a large influence on the attitude estimation due to the shielding relationship of the geometric model or the noise of the detector, and therefore, it is necessary to determine whether the estimation result belongs to the jump. Two cases exist for hopping: the method comprises the steps of firstly, jumping caused by external factors such as a detector and the like, wherein the jumping can influence the stability of an estimation algorithm, and secondly, sudden change caused by mirror reflection of a large surface of a space target can not influence the stability of the algorithm, so that whether the jumping affects a system or not can be judged through two thresholds.
First, the point jump coefficient is defined as follows
Wherein m iskIf jump suppression is enabled for the view stars and the like observed at time k, the estimated state parameters are one frame later than the actual observation.
If the jump coefficient is larger than the threshold value, the system is considered to have jump, whether the jump belongs to reasonable jump is further judged, the observed value at the k +1 moment is predicted, if the residual error between the observed value predicted at the k +1 moment and the actual observed value is larger than the threshold value, the jump is considered to be an unreasonable threshold value, namely
4. Overfitting phenomenon correction
If the lossless transform parameters, particularly the observation model error covariance, are not properly chosen, an overfitting of the estimates of the state parameters will occur. Quaternions can represent simpler state parameter differences, but it is difficult to quantitatively express the scale that needs to be corrected, so for the over-fitting phenomenon, the corrected scale is quantitatively characterized using the rodgers parameter in its correction process.
The above-mentioned correction is done by a linear transformation, so that the overfitting can be regarded as a linear process, the scale of the correction being
Wherein,
a representation of the observed value of the prediction,
representing the corresponding observed value, m, after correction of the state parameterk+1Representing the observed values that were actually observed.
Based on the linear transformation assumption, the Kalman gain is corrected according to the scale required to be corrected, and the corrected Kalman gain is
Wherein, K-For Kalman gain, K, calculated from the sampling points+Is the corrected kalman gain.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (5)
1. A method for identifying key features of a spatial target based on photometric signals is characterized by comprising the following steps:
step 1: setting an initial state of a space target, predicting the position of the space target based on a finite element model and a solar radiation compression model, inverting target attitude parameters by adopting a nonlinear filtering method to be regarded as a filtering estimation problem of a nonlinear dynamic system, and estimating the attitude of the space target according to an attitude control mode;
step 2: analyzing the surface element visibility of the space target surface;
and step 3: predicting a space target photometric signal based on a bidirectional reflectivity distribution function, and correcting the space target state predicted in the step 1 according to an actual observation value;
and 4, step 4: estimating an observed value of the corrected parameter based on the space target state in the step 3, and correcting an overfitting phenomenon possibly existing in the step 3 by using an actual observed value;
and 5: and (4) repeating the steps 1 to 4 based on the space target parameters estimated in the step 4, updating and obtaining the position and the posture of the space target, and realizing the identification of the key features of the space target.
2. The photometric signal-based method for identifying key features of spatial objects as defined in claim 1, wherein the prediction and estimation of the spatial object position and orientation in step 1 comprises the steps of:
step 1.1: predicting nonlinear system state parameters and observed values through lossless transformation;
step 1.2: the numerical value transmission and the updating are completed by fitting the mean value and the covariance of the state parameters of the nonlinear system;
step 1.3: constructing a motion updating model of the attitude quaternion of the space target;
step 1.4: and updating the space target attitude according to the track position and the track speed.
3. The method for identifying key features of a spatial target based on photometric signals as claimed in claim 1, wherein in step 2, the bin visibility analysis of the surface of the spatial target comprises the following steps:
step 2.1: carrying out finite element division on a geometric model of the space target, and extracting surface element parameters;
step 2.2: determining a shielding relation between surface elements based on the positions of corner points of the surface elements and the directions of the light clusters;
step 2.3: and determining the visible surface elements and the visibility thereof based on the shielding relation among the surface elements.
4. The method as claimed in claim 1, wherein the step 3 of correcting the photometric signal and the state prediction value of the spatial target comprises the following steps:
step 3.1: based on the solar radiation illumination, calculating the radiation illumination generated by the visible surface element at the entrance pupil of the ground-based detector to obtain a single-frame luminosity signal;
step 3.2: predicting a single-frame photometric signal of the space target based on the motion parameters of the space target and by combining the position parameters of the sun and the optical detector, the position of the space target and the attitude parameters;
step 3.3: and (3) correcting the space target state based on the photometric signal predicted in the step 3.2 and the actual observation value.
5. The method for identifying key features of a spatial target based on photometric signals as defined in claim 1, wherein the overfitting phenomena correction for observation estimation and correction in step 4 comprises the following steps:
step 4.1: determining a quantitative expression of an overfitting correction scale based on a linear transformation hypothesis aiming at a position correction process;
step 4.2: and determining a quantitative expression of the overfitting correction scale based on a linear transformation hypothesis aiming at the posture correction process.
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