CN110059292B - Space target posture recognition method - Google Patents
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Abstract
The invention discloses a space target posture identification method, which solves the technical problem of judging the characteristics of an LEO target, takes the influence of space observation geometry on the space target characteristic identification into consideration when identifying the posture of the LEO space target, carries out the rapid dynamic time warping distance calculation on the luminosity data of the space target currently observed and the luminosity data of different posture models in a space target database according to the practical physical and geometric meanings when identifying the posture of the target under the similar space observation geometry change rule, directly carries out the rapid dynamic time warping distance calculation, simultaneously takes the influence of different space target postures on the luminosity data shape and the amplitude of the space target into consideration, further realizes the identification of the LEO target posture by calculating the luminosity data shape and amplitude distance, and provides a minimum value and average value identification model.
Description
Technical Field
The invention relates to a space target posture identification method, and belongs to the field of calculation.
Background
The low-orbit target is close to the ground and is a typical orbit for ground reconnaissance and remote sensing, the task characteristics of the low-orbit target determine that the low-orbit target needs to be subjected to attitude adjustment when the low-orbit target works on the orbit, attitude judgment is a key for predicting the action intention of the target, and the identification of the target attitude can provide reference for the judgment of the action intention of the target.
The method is characterized in that the attitude inversion of a space target is realized based on photometric data, the traditional inversion method only considers the influence of the change of a phase angle on the characteristic inversion of the space target, and the nonlinear filtering with a good estimation effect has the defects of large data computation amount, low computation efficiency, only consideration of simple observation geometry, incomplete analysis on the observation geometry and strict requirements on the observation conditions; a space target feature identification method based on luminosity big data is mainly characterized in that machine learning is directly carried out on luminosity data obtained by observation facing to a GEO target, geometric significance in observation is not considered, the purpose of the machine learning cannot achieve the expected effect, and due to the fact that the low-orbit target is high in movement speed and complex in space geometric change, the feature inversion result of the low-orbit target is worse without considering the condition of the geometric significance.
At present, a large amount of photometric data are observed and accumulated by an optical monitoring system for a foundation space target in China, and an intelligent means is urgently needed to realize characteristic judgment of an LEO target, so that the space situation perception capability is effectively improved.
Disclosure of Invention
Aiming at the defects, the invention provides a space target posture identification method, aiming at LEO space target luminosity big data acquired in space target optical detection, on the basis of fully considering the influence of an observation geometric relation on space target luminosity data, a luminosity data set with a similar space observation geometric change rule is searched, and the posture of the space target is identified by calculating the shape distance and the amplitude distance between the current observation luminosity data and the luminosity data set.
In order to achieve the purpose, the invention is concretely realized by the following technical scheme:
the invention provides a space target posture identification method, which comprises the following steps:
acquiring luminosity data and orbit data of a plurality of groups of LEO space targets observed at different times to serve as a space target database;
analyzing a relative position sequence of the sun, the detector and the space target in the space target observation process through orbit data of the space target, wherein the relative position sequence is unified under a satellite centroid orbit coordinate system;
thirdly, resolving a phase angle sequence, a sun azimuth angle sequence, a sun pitch angle sequence, a detector azimuth angle sequence and a detector pitch angle sequence by the relative position sequence; the change rule of the azimuth angle sequence of the sun, the change rule of the pitch angle sequence of the sun, the change rule of the azimuth angle sequence of the detector and the change rule of the pitch angle sequence of the detector are space observation geometric change rules;
acquiring current observation data, wherein the current observation data comprises current observation luminosity data and current observation orbit data;
step five, searching a first space target database Dom1 which has a similar change rule with the phase angle sequence of the current observation data in the space target database;
step six, searching a second space target database which has similar change rules with the sun azimuth angle sequence, the sun pitch angle sequence, the detector azimuth angle sequence and the detector pitch angle sequence of the current observation data in the first space target database, and taking the second space target database as a luminosity data set which has similar space observation geometric change rules with the luminosity data of the current observation data;
seventhly, based on a distance calculation method, performing shape similarity judgment and amplitude similarity judgment on the current observation luminosity data and luminosity data of different posture models in the second luminosity data set to obtain a calculation result matrix MAG;
and step eight, after the calculation result matrix MAG is input into the minimum value identification model or the mean value identification model, the attitude identification result of the current observed data is output.
In the first step, acquiring photometric data and orbital data of a plurality of groups of space targets observed at different times comprises:
photometric data and orbit data of LEO targets with different platform shapes and different postures in different working states and different orbit types at different observation times are obtained through simulation calculation, actual observation and/or laboratory simulation measurement modes.
In the second step, the method for unifying the relative position sequence under the satellite centroid orbit coordinate system comprises the following steps:
in a Satellite Tool Kit (STK), adding a detector and an observed Satellite, establishing a Satellite centroid orbital coordinate system, and establishing a vector pointing to the detector and a vector pointing to the sun under the Satellite centroid orbital coordinate system;
the illumination limiting conditions of the detector are set as follows: the detector is in a full shadow or a half shadow area;
setting the illumination conditions of the space target as follows: the space target is directly irradiated by the sun;
and outputting the position sequence of the detector under the satellite centroid orbit coordinate system in the observable arc section and the position sequence of the sun through the STK report manager.
In the third step, the relative position sequence is used for resolving the phase angle sequence, the solar azimuth angle sequence, the solar pitch angle sequence, the detector azimuth angle sequence and the detector pitch angle sequence, and the method comprises the following steps:
the method for solving the phase angle sequence from the relative position sequence is as follows:
wherein i is the time within the observable arc segment, and SunInSat is the vector of the sun; facInSat is a vector of the detector;
the coordinates of the vector of the sun are SunInSat i ＝(SX i ,SY i ,SZ i ) (ii) a The coordinates of the vector of the detector are FacInSat i ＝(FX i ,FY i ,FZ i ) (ii) a Wherein S is i '＝(SX i ,SY i ,0)；F i '＝(FX i ,FY i ,0)；
The method for calculating the azimuth angle sequence of the sun, the pitch angle sequence of the sun, the azimuth angle sequence of the detector and the pitch angle sequence of the detector from the relative position sequence comprises the following steps:
wherein, the symbol "+" represents the inner product operation of the vector, and the symbol "|" represents the modulo operation of the vector;
vector Z = (0,0,1), X = (1,0,0); alpha is alpha Si At the i-th time, the azimuth angle of the sun, α Fi At the i-th time, the azimuth angle of the detector, beta Si At the i-th moment, the sun's pitch angle, beta Fi The pitch angle of the probe at the ith time.
In the fourth step, the current observation data includes:
randomly extracting data from a spatial target database; or
Observing the obtained data in real time; or
The observed data for a plurality of arc segments is accumulated.
In the fifth step, the method for searching the first space target database having a similar change rule with the phase angle sequence of the current observation data in the space target database includes:
rapid calculation of phase angle sequence of current observation data
And dynamic time regular distances of all Access phase angle sequences in the spatial target database; wherein the phase angle sequence of the current observation data is rapidly calculated
And the phase angle sequence of the Access time in the spatial target database
The dynamic time warping distance of (a) is:
the limit condition of the space target to the observability of the ground-based optical detector is as follows: the space target is directly irradiated by the sun, the detector is positioned in a global shadow or a penumbra area of the earth, and no shielding exists between the target and the detector; access represents an observable arc segment satisfying the limiting condition;
represents the phase angle sequence of the current observed data, and the subscript letter t represents the current observed data;
for fast calculation
Sequence and
dynamic time warping distance of the sequence; according to
The order of the sizes of the first and second sub-areas is used for sorting the Access in the space target database, and the sorted first o is extracted 1 Taking the Access space target data as a first space target database; o 1 The parameter is used for controlling the number of accesses of the first space target database.
In the sixth step, the method for searching the second spatial target database having the similar change rule with the solar azimuth angle sequence, the solar pitch angle sequence, the detector azimuth angle sequence and the detector pitch angle sequence of the current observation data in the first spatial target database includes:
fast calculation of solar azimuth sequence alpha of current observation data St And the solar azimuth sequence of all Access in the first spatial target database Dom1Dynamic time warping distance; wherein, the solar azimuth sequence alpha of the current observation data is rapidly calculated St And the sun azimuth angle sequence alpha of the Access time in the first space target database Dom1 S [Access]The dynamic time warping distance of (a) is:
α SFDTW [Access]＝FDTW(α St ,α S [Access])；
wherein alpha is St The capital letter S of the middle subscript indicates that the object to which the azimuth sequence belongs is the sun, and the subscript letter t indicates the current observation data;
azimuth angle sequence alpha of detector for quickly calculating current observation data Ft And dynamic time warping distances of azimuth angle sequences of all Access detectors in the first spatial target database Dom1; wherein the azimuth sequence alpha of the detector for fast calculation of the current observation data Ft And the azimuth angle sequence alpha of the detector at the Access time in the first space target database Dom1 F [Access]The dynamic time warping distance of (a) is:
α FFDTW [Access]＝FDTW(α Ft ,α F [Access])；
wherein alpha is Ft The capital letter F of the middle subscript indicates that the object to which the azimuth sequence belongs is a detector, and the subscript letter t indicates the current observation data;
solar pitch angle sequence beta for rapidly calculating current observation data St And dynamic time warping distance of pitch angle sequences of all Access sun in the first space target database Dom1; wherein, the sun pitch angle sequence beta of the current observation data is rapidly calculated St And the pitch angle sequence beta of the sun at the Access time in the first space target database Dom1 S [Access]The dynamic time warping distance of (a) is:
β SFDTW [Access]＝FDTW(β St ,β S [Access])；
wherein, beta St The capital letter S of the middle subscript indicates that the object to which the pitch angle sequence belongs is the sun, and the subscript letter t indicates the current observation data;
quickly calculating pitch angle sequence beta of current observation detector Ft And dynamic time warping distance of pitch angle sequences of all Access detectors in the first space target database Dom1; wherein, the pitch angle sequence beta of the current observation detector is rapidly calculated Ft And the pitch angle sequence beta of the detector at the Access time in the first space target database Dom1 F [Access]The dynamic time warping distance of (a) is:
β FFDTW [Access]＝FDTW(β Ft ,β F [Access])；
wherein, beta Ft The capital letter F of the middle subscript indicates that the object to which the pitch angle sequence belongs is a detector, and the subscript letter t indicates current observation data;
respectively according to alpha SFDTW 、α FFDTW 、β SFDTW 、β FFDTW The size sequence of the first space target database carries out four sorts on the Access in the first space target database to form four space target databases Dom11, dom12, dom13 and Dom14, and front n (n is more than or equal to o) in the Dom11 to Dom14 is extracted simultaneously 1 ) The Access forms a second space target database, wherein the second space target database is required to contain o 2 Access, each occurrence four times, wherein o 2 And the input quantity is used for controlling the number of the accesses of the second space target database.
In the seventh step, the distance calculation method adopts a method for quickly calculating the DTW distance, and the method comprises the following steps:
quickly calculating luminosity data M of current observation t Obtaining a calculation result matrix MAG by the dynamic time warping distance of photometric data of a model with different postures from the space target in the second space target database; wherein the currently observed photometric data M is rapidly calculated t The dynamic time warping distance between the first space target database and the luminosity data of the jth Access of the ith posture model in the second space target database is as follows:
MAG i,j ＝FDTW(M t ,M i,j )；
where M denotes photometric data, subscript t denotes current observation data, j =1,2 2 ，i＝1,2,...T，o 2 The number of Access of the second space target database, and T is the space target in the second space target databaseNumber of types of poses.
In the step eight, inputting the calculation result matrix MAG into the minimum recognition model, including:
selecting an attitude model corresponding to the minimum value of each column of the MAG matrix as a primary attitude identification result of the current observed data, and outputting the primary attitude identification result with the largest occurrence frequency as the attitude identification result of the current observed data;
inputting the calculation result matrix MAG into a mean recognition model, comprising:
and calculating the average value of each row of MAG, and outputting the attitude model with the minimum average value as the attitude recognition result of the current observed data.
The beneficial effects of the invention are:
according to the technical scheme provided by the invention, the influence of space observation geometry on space target feature recognition is considered when the LEO space target gesture is recognized, the LEO target gesture is recognized by calculating the distance between the shape and the amplitude of photometric data under the similar space observation geometry change rule according to the practical physical and geometric meanings, and a minimum value and mean value recognition model is provided.
Drawings
Fig. 1 is a schematic diagram of a first satellite and its attitude according to the present invention.
Fig. 2 is a schematic diagram of a second satellite and its attitude according to the present invention.
Fig. 3 is a schematic diagram of a third satellite and its attitude according to the present invention.
Fig. 4 is a schematic diagram of a fourth satellite and its attitude according to the present invention.
Fig. 5 is a schematic diagram of a fifth satellite and its attitude according to the present invention.
Fig. 6 is a schematic diagram of OCS data of different attitudes of a first satellite under similar observation geometry according to the present invention.
Fig. 7 is a schematic diagram of OCS data of a second satellite in different postures under a similar observation geometry according to the present invention.
Fig. 8 is a schematic diagram of OCS data of a third satellite in different attitudes under similar observation geometries, according to the present invention.
Fig. 9 is a schematic diagram of OCS data of a fourth satellite in different postures under a similar observation geometry according to the present invention.
Fig. 10 is a schematic diagram of OCS data of a fifth satellite in different postures under a similar observation geometry according to the present invention.
FIG. 11 is a flow chart of the FDTW provided by the present invention.
Detailed Description
The technical solutions of the present invention are specifically described below, and it should be noted that the technical solutions of the present invention are not limited to the embodiments described in the examples, and those skilled in the art should refer to and use the contents of the technical solutions of the present invention, and make improvements and designs based on the technical solutions of the present invention, and shall fall into the protection scope of the present invention.
Example one
The embodiment of the invention provides a method for identifying a space target attitude, which comprises the following steps:
acquiring photometric data and orbit data of a plurality of groups of space targets observed at different times to serve as a space target database;
analyzing a relative position sequence of the sun, the detector and the space target in the space target observation process through orbit data of the space target, wherein the relative position sequence is unified under a satellite centroid orbit coordinate system;
thirdly, resolving a phase angle sequence, a solar azimuth angle sequence, a solar pitch angle sequence, a detector azimuth angle sequence and a detector pitch angle sequence by the relative position sequence; the change rule of the azimuth angle sequence of the sun, the change rule of the pitch angle sequence of the sun, the change rule of the azimuth angle sequence of the detector and the change rule of the pitch angle sequence of the detector are space observation geometric change rules;
acquiring current observation data, wherein the current observation data comprises current observation luminosity data and current observation orbit data;
step five, searching a first space target database Dom1 which has a similar change rule with the phase angle sequence of the current observation data in the space target database;
step six, searching a second space target database which has similar change rules with the sun azimuth angle sequence, the sun pitch angle sequence, the detector azimuth angle sequence and the detector pitch angle sequence of the current observation data in the first space target database to serve as a luminosity data set which has similar space observation geometric change rules with the luminosity data of the current observation data;
step seven, based on a distance calculation method, respectively carrying out shape similarity judgment and amplitude similarity judgment on the current observation luminosity data and luminosity data of different posture models in the second luminosity data set to obtain a calculation result matrix MAG;
and step eight, after the calculation result matrix MAG is input into the minimum value identification model or the mean value identification model, the attitude identification result of the current observed data is output.
In the first step, acquiring photometric data and orbital data of a plurality of groups of space targets observed at different times comprises:
photometric data and orbit data of GEO targets with different platform shapes and different postures in different working states and different orbit types in different observation times are obtained through simulation calculation, actual observation and/or laboratory simulation measurement.
In the second step, the method for unifying the relative position sequences under the satellite centroid orbit coordinate system comprises the following steps:
in a Satellite Tool Kit (STK), adding a detector and an observed Satellite, establishing a Satellite centroid orbital coordinate system, and establishing a vector pointing to the detector and a vector pointing to the sun under the Satellite centroid orbital coordinate system;
the illumination limiting conditions of the detector are set as follows: the detector is in a full shadow or a half shadow area;
setting the illumination conditions of the space target as follows: the space target is directly irradiated by the sun;
and outputting a detector position sequence and a sun position sequence under the satellite centroid orbital coordinate system in the observable arc section through the STK report manager.
In the third step, the relative position sequence is used for resolving the phase angle sequence, the solar azimuth angle sequence, the solar pitch angle sequence, the detector azimuth angle sequence and the detector pitch angle sequence, and the method comprises the following steps:
the method for solving the phase angle sequence from the relative position sequence is as follows:
wherein i is the time within the observable arc segment, and SunInSat is the vector of the sun; facInSat is the vector of the detector;
the coordinates of the vector of the sun are SunInSat i ＝(SX i ,SY i ,SZ i ) (ii) a The coordinates of the vector of the detector are FacInSat i ＝(FX i ,FY i ,FZ i ) (ii) a Wherein S is i '＝(SX i ,SY i ,0)；F i '＝(FX i ,FY i ,0)；
The method for calculating the azimuth angle sequence of the sun, the pitch angle sequence of the sun, the azimuth angle sequence of the detector and the pitch angle sequence of the detector from the relative position sequence comprises the following steps:
wherein, the symbol "+" represents the inner product operation of the vector, and the symbol "|" represents the modulo operation of the vector;
vector Z = (0,0,1), X = (1,0,0); alpha is alpha Si At the i-th time, the azimuth angle of the sun, α Fi At the i-th time, the azimuth angle of the detector, beta Si At the i-th time, the sun's pitch angle, beta Fi The pitch angle of the probe at the ith time.
In the fourth step, the current observation data includes:
randomly extracting data from a spatial target database; or
Observing the obtained data in real time; or
The observed data for a plurality of arc segments is accumulated.
The method adopted in the fifth step, the sixth step and the seventh step is realized by adopting a method for rapidly calculating dynamic time warping (Fast DTW, FDTW):
(1) And (4) coarsening. The original sequence is subjected to data abstraction, and the data abstraction can be executed for multiple times, namely 1/1 → 1/2 → 1/4 → 1/8, and the coarse-grained data point is the average value of a plurality of corresponding fine-grained data points.
(2) And (5) projecting. On coarser granularity, the DTW distance is calculated.
(3) And (4) fine granularity. The squares traversed by the regular paths obtained at the coarser granularity are further refined to a finer granularity time series, and the FDTW expands K granularities outward (laterally, vertically, obliquely) in the finer granularity space. The specific execution flow diagram of FDTW is shown in FIG. 11.
The DTW distance of two time sequences needs to construct a dynamic time warping distance matrix D with m multiplied by n units, the complexity in time and space is O (mn), and for the space target photometric sequence with high current sampling rate, the calculation of the DTW distance consumes a large amount of time. FDTW actively reduces the calculation range, and filters the edge elements with the time complexity of O (min (m, n)), thereby greatly shortening the calculation time. When the DTW distance of the two curves is calculated, the DTW distance comprises the shape distance and the amplitude distance of the two curves so as to solve the problems of data expansion and translation on a time axis and unequal data in distance calculation.
In the fifth step, the method for searching the first space target database having a similar change rule with the phase angle sequence of the current observation data in the space target database includes:
fast calculation of phase angle sequence of current observation data
And dynamic time regular distance of phase angle sequences of all Access in the space target database; wherein, the fast calculation is carried outPhase angle sequence of pre-observation data
And the phase angle sequence of the Access time in the spatial target database
The dynamic time warping distance of (a) is:
the limit condition of the space target to the observability of the ground-based optical detector is as follows: the space target is directly irradiated by the sun, the detector is positioned in a global shadow or a penumbra area of the earth, and no shielding exists between the target and the detector; access represents an observable arc segment satisfying the limiting condition;
representing the phase angle sequence of the current observed data, and subscript letter t representing the current observed data;
for fast calculation
Sequence and
dynamic time warping distance of the sequence; according to
The order of the sizes of the first and second sub-areas is used for sorting the Access in the space target database, and the sorted first o is extracted 1 Taking the Access space target data as a first space target database; o 1 The parameter is used for controlling the number of accesses of the first space target database.
In the sixth step, a method for searching a second spatial target database having a similar change rule with the sun azimuth angle sequence, the sun pitch angle sequence, the detector azimuth angle sequence and the detector pitch angle sequence of the current observation data in the first spatial target database includes:
fast calculation of solar azimuth sequence alpha of current observation data St And the dynamic time warping distance of the sun azimuth angle sequences of all the accesses in the first space target database Dom1; wherein, the solar azimuth angle sequence alpha of the current observation data is rapidly calculated St And the sun azimuth angle sequence alpha of the Access time in the first space target database Dom1 S [Access]The dynamic time warping distance of (a) is:
α SFDTW [Access]＝FDTW(α St ,α S [Access])；
wherein alpha is St The capital letter S of the middle subscript indicates that the object to which the azimuth sequence belongs is the sun, and the subscript letter t indicates the current observation data;
azimuth angle sequence alpha of detector for quickly calculating current observation data Ft And dynamic time warping distances of azimuth angle sequences of all Access detectors in the first space target database Dom1; wherein the azimuth sequence alpha of the detector for fast calculation of the current observation data Ft And the azimuth angle sequence alpha of the detector at the Access time in the first space target database Dom1 F [Access]The dynamic time warping distance of (a) is:
α FFDTW [Access]＝FDTW(α Ft ,α F [Access])；
wherein alpha is Ft The capital letter F of the middle subscript indicates that the object to which the azimuth sequence belongs is a detector, and the subscript letter t indicates the current observation data;
quickly calculating sun pitch angle sequence beta of current observation data St And dynamic time warping distance of pitch angle sequences of sun of all Access in the first space target database Dom1; wherein, the sun pitch angle sequence beta of the current observation data is rapidly calculated St And the pitch angle sequence beta of the sun at the Access time in the first space target database Dom1 S [Access]The dynamic time warping distance of (a) is:
β SFDTW [Access]＝FDTW(β St ,β S [Access])；
wherein, beta St The capital letter S of the middle subscript indicates that the object to which the pitch angle sequence belongs is the sun, and the subscript letter t indicates the current observation data;
quickly calculating pitch angle sequence beta of current observation detector Ft And dynamic time warping distance of pitch angle sequences of all Access detectors in the first space target database Dom1; wherein, the pitch angle sequence beta of the current observation detector is rapidly calculated Ft And the pitch angle sequence beta of the detector at the Access time in the first space target database Dom1 F [Access]The dynamic time warping distance of (a) is:
β FFDTW [Access]＝FDTW(β Ft ,β F [Access])；
wherein beta is Ft The capital letter F of the middle subscript indicates that the object to which the pitch angle sequence belongs is a detector, and the subscript letter t indicates current observation data;
respectively according to alpha SFDTW 、α FFDTW 、β SFDTW 、β FFDTW The size sequence of the first space target database carries out four sorts of Access in the first space target database to form four space target databases Dom11, dom12, dom13 and Dom14, and front n (n is more than or equal to o) in the Dom11 to Dom14 is extracted at the same time 1 ) The Access forms a second space target database, wherein the second space target database is required to contain o 2 Access, each occurrence four times, wherein o 2 Is an input quantity for controlling the number of accesses of the second spatial target database.
In the seventh step, the distance calculation method adopts a method for quickly calculating the DTW distance, and the method comprises the following steps:
quickly calculating luminosity data M of current observation t Obtaining a calculation result matrix MAG by the dynamic time warping distance of photometric data of a model with different postures from the space target in the second space target database; wherein the currently observed photometric data M is rapidly calculated t Dynamic time scale distance with the light intensity data of jth Access of ith posture model in the second space target databaseThe separation is as follows:
MAG i,j ＝FDTW(M t ,M i,j )；
where M denotes photometric data, subscript t denotes current observation data, j =1,2 2 ，i＝1,2,...T，o 2 The number of accesses of the second spatial target database, and T is the number of types of the spatial target attitudes in the second spatial target database.
In the eighth step, inputting the calculation result matrix MAG to the minimum recognition model, including:
selecting an attitude model corresponding to the minimum value of each column of the MAG matrix as a primary attitude identification result of the current observed data, and outputting the primary attitude identification result with the largest occurrence frequency as the attitude identification result of the current observed data;
inputting the calculation result matrix MAG into a mean recognition model, comprising:
and calculating the average value of each row of MAG, and outputting the attitude model with the minimum average value as the attitude recognition result of the current observed data.
One embodiment is as follows:
1. creation of data sets
In order to verify the effect of the invention, the Lijiang astronomical phenomena is taken as a ground-based optical observation station, an LEO orbit target is selected, the limit conditions of the observation station and a satellite are set as the limit conditions of a ground-based optical observation space target, the orbit parameter of the low orbit is set through a satellite tool kit STK, and the observable condition is analyzed, as shown in Table 1.
TABLE 1 orbital parameters and observable conditions for low earth orbit satellites
Firstly, calling STK through MATLAB, modifying an STK report manager output time window according to the start and stop time of each Access, outputting time sequence three-dimensional coordinates of SunInSat and FacInSat in all accesses in one year in an O-XYZ system as input of target luminosity calculation, and when the time sequence three-dimensional coordinates are generated, an orbit model is a two-body model, and the time sequence step length is two1s. Solved according to time sequence three-dimensional coordinates
α S 、α F 、β S And beta F The timing data of (2).
Typical satellite models 1-5 shown in fig. 1-5 are constructed through 3DS MAX and are exported as 3DS files, the figure marks the attitude of each satellite in a satellite orbit coordinate system, and the satellite normally operates in orbit in a three-axis stable earth orientation working mode. The surface of the satellite is coated with silver and golden polyimide films, aluminum, sailboard materials, white paint and the like which are commonly used for space targets. And importing the satellite model and the low-orbit three-dimensional time sequence coordinate into an OCS calculation program, and calculating OCS data of different targets and different postures. The target OCS is calculated by adopting an OpenGL-based pickup technology, a Phong model improved by aiming at a Fresnel phenomenon which is a common material of the space target is adopted for description of the material BRDF, model 3ds files and time sequence three-dimensional coordinates are imported into an OCS calculation program to calculate OCS sequences of all models, and then photometric data sets of different postures of the space target can be established. The gesture recognition is performed for the same target.
2. And verifying the target posture characteristic recognition effect.
Randomly extracting Access for 100 times aiming at each satellite, identifying the attitude characteristics of the satellite, and enabling the satellite to be o 1 ＝15，o 2 And (5). Shown in FIGS. 6-10 as Access [491 ]]As OCS data of satellites 1-5 in different postures under a similar observation geometric relationship during Access testing, it can be seen from the figure that the optical scattering characteristics of a target are changed due to different postures aiming at the same satellite, and accordingly, the satellite posture characteristics are identified under the observation geometric change rule of a similar observation space.
The data of different attitudes of each satellite are input into an attitude recognition program, and the attitude recognition results of the mean value and minimum value recognition models are shown in table 2.
TABLE 2 satellite attitude identification results (mean/minimum model)
The recognition results are counted, and the accuracy of gesture recognition is shown in table 3.
TABLE 3 satellite attitude identification accuracy/% (mean model/minimum model)
The table shows that the recognition accuracy can reach 97% when different attitudes are recognized for the same satellite, the average recognition rate of the two recognition models is calculated, the average recognition model is 88.6%, the minimum recognition model is 87.6%, and the recognition accuracy of the average model is higher than that of the minimum model. The mean recognition model is a voting result of a plurality of groups of photometric data of the target characteristics, and is more representative, and the minimum value model is only a voting result of a single characteristic corresponding to the minimum value, so that the mean recognition model is suggested to be used in the application.
For low-orbit targets, on one hand, the structural shape of the target can be acquired through optical telescopes, radars and other means, the material of the surface coating of the target is determined through technical means such as spectrum unmixing and the like, a target model is constructed under the condition that the structural shape and the material of the target are determined, and photometric data of the target in different running postures in orbit are calculated in a simulation mode. By selecting target historical photometric data with a space observation geometric change rule similar to that of the current observation and comparing the target historical photometric data with the current observation, the states of target posture adjustment, abnormal detection, failure and the like can be judged.
On the other hand, under the condition that the target shape and surface material information cannot be obtained, clustering storage is carried out on target historical photometric data obtained by foundation observation according to a similar space observation geometric change rule, whether the current photometric data is consistent with the historical data under the similar space observation geometric change rule or not is judged through comparing the current observation data of the satellite with the satellite historical data, and target abnormity detection is carried out.
The invention has the beneficial effects that:
according to the technical scheme provided by the invention, the influence of space observation geometry on space target feature recognition is considered when the LEO space target gesture is recognized, the LEO target gesture is recognized by calculating the distance between the shape and the amplitude of photometric data under the similar space observation geometry change rule according to the practical physical and geometric meanings, and a minimum value and mean value recognition model is provided.
The above disclosure is only for a few specific embodiments of the present invention, however, the present invention is not limited to the above embodiments, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present invention.
Claims (9)
1. A method for recognizing the attitude of a spatial target is characterized by comprising the following steps:
acquiring luminosity data and orbit data of a plurality of groups of LEO space targets observed at different times to serve as a space target database;
analyzing a relative position sequence of the sun, the detector and the space target in the space target observation process through orbit data of the space target, wherein the relative position sequence is unified under a satellite centroid orbit coordinate system;
thirdly, resolving a phase angle sequence, a solar azimuth angle sequence, a solar pitch angle sequence, a detector azimuth angle sequence and a detector pitch angle sequence by the relative position sequence; the change rule of the solar azimuth angle sequence, the change rule of the solar pitch angle sequence, the change rule of the detector azimuth angle sequence and the change rule of the detector pitch angle sequence are space observation geometric change rules;
acquiring current observation data, wherein the current observation data comprises current observation luminosity data and current observation orbit data;
step five, searching a first space target database Dom1 which has a similar change rule with the phase angle sequence of the current observation data in the space target database;
step six, searching a second space target database which has similar change rules with the sun azimuth angle sequence, the sun pitch angle sequence, the detector azimuth angle sequence and the detector pitch angle sequence of the current observation data in the first space target database, and taking the second space target database as a luminosity data set which has similar space observation geometric change rules with the luminosity data of the current observation data;
seventhly, based on a distance calculation method, performing shape similarity judgment and amplitude similarity judgment on the current observation luminosity data and the luminosity data of the models in different postures in the second luminosity data set to obtain a calculation result matrix MAG;
and step eight, after the calculation result matrix MAG is input into the minimum value identification model or the mean value identification model, the attitude identification result of the current observed data is output.
2. The method of claim 1, wherein in step one, acquiring photometric data and orbital data for a plurality of sets of spatial objects observed at different times comprises:
photometric data and orbit data of LEO targets with different platform shapes and different postures in different working states and different orbit types at different observation times are obtained through simulation calculation, actual observation and/or laboratory simulation measurement modes.
3. The method of claim 1, wherein in step two, the relative position sequence is unified under a satellite centroid orbital coordinate system, comprising:
in a Satellite Tool Kit (STK), adding a detector and an observed Satellite, establishing a Satellite centroid orbital coordinate system, and establishing a vector pointing to the detector and a vector pointing to the sun under the Satellite centroid orbital coordinate system;
the illumination limiting conditions of the detector are set as follows: the detector is in a full shadow or a half shadow area;
setting the illumination conditions of the space target as follows: the space target is directly irradiated by the sun;
and outputting the position sequence of the detector under the satellite centroid orbit coordinate system in the observable arc section and the position sequence of the sun through the STK report manager.
4. The method according to one of claims 1 to 3, wherein in the third step, the resolving the phase angle sequence, the azimuth sequence of the sun, the pitch sequence of the sun, the azimuth sequence of the detector and the pitch sequence of the detector from the relative position sequence comprises:
the method for solving the phase angle sequence from the relative position sequence is as follows:
wherein i is the time within the observable arc segment, and SunInSat is the vector of the sun; facInSat is the vector of the detector;
the coordinates of the vector of the sun are SunInSat i ＝(SX i ,SY i ,SZ i ) (ii) a The coordinates of the vector of the detector are FacInSat i ＝(FX i ,FY i ,FZ i ) (ii) a Wherein S is i '＝(SX i ,SY i ,0)；F i '＝(FX i ,FY i ,0)；
The method for calculating the azimuth angle sequence of the sun, the pitch angle sequence of the sun, the azimuth angle sequence of the detector and the pitch angle sequence of the detector from the relative position sequence comprises the following steps:
wherein, the symbol "+" represents the inner product operation of the vector, and the symbol "|" represents the modulo operation of the vector;
vector Z = (0,0,1), X = (1,0,0); alpha is alpha Si At the i-th time, the azimuth angle of the sun, α Fi At the i-th time, the azimuth angle of the detector, beta Si At the i-th moment, the sun's pitch angle, beta Fi The pitch angle of the probe at the ith time.
5. The method of claim 1, wherein in the fourth step, the current observation data comprises:
randomly extracting data from a spatial target database; or
Observing the obtained data in real time; or
The observed data for a plurality of arc segments is accumulated.
6. The method as claimed in claim 1, wherein in the fifth step, the method for searching the first spatial target database having similar variation law with the phase angle sequence of the current observed data in the spatial target database comprises:
fast calculation of phase angle sequence of current observation data
And dynamic time regular distance of phase angle sequences of all Access in the space target database; wherein the phase angle sequence of the current observation data is rapidly calculated
And the phase angle sequence of the Access time in the spatial target database
The dynamic time warping distance of (a) is:
the limit condition of the space target to the observability of the ground-based optical detector is as follows: the space target is directly irradiated by the sun and the detector is positioned in a global shadow or a penumbra area of the earth, and no shielding exists between the target and the detector; access represents an observable arc segment satisfying the limiting condition;
indicating the phase angle sequence of the current observation, subscript letter t tableDisplaying current observation data;
for fast calculation
Sequence and
dynamic time warping distance of the sequence; according to
The order of the sizes of the first and second sub-areas is used for sorting the Access in the space target database, and the sorted first o is extracted 1 Taking the Access space target data as a first space target database; o 1 The parameter is used for controlling the number of accesses of the first space target database.
7. The method according to claim 1, wherein in step six, the method for searching the first spatial target database for the second spatial target database having similar variation law with the azimuth sequence of the sun, the pitch sequence of the sun, the azimuth sequence of the detector and the pitch sequence of the detector of the current observation data comprises:
fast calculation of solar azimuth sequence alpha of current observation data St And the dynamic time warping distance of the solar azimuth sequence of all Access in the first space target database Dom1; wherein, the solar azimuth sequence alpha of the current observation data is rapidly calculated St And the solar azimuth sequence alpha of the Access time in the first space target database Dom1 S [Access]The dynamic time warping distance of (a) is:
α SFDTW [Access]＝FDTW(α St ,α S [Access])；
wherein alpha is St The capital letter S of the middle subscript indicates that the object to which the azimuth sequence belongs is the sun, and the subscript letter t indicates the current observation data;
azimuth angle sequence alpha of detector for quickly calculating current observation data Ft And dynamic time warping distances of azimuth angle sequences of all Access detectors in the first space target database Dom1; wherein the azimuth sequence alpha of the detector for fast calculation of the current observation data Ft And the azimuth angle sequence alpha of the detector at the Access time in the first space target database Dom1 F [Access]The dynamic time warping distance of (a) is:
α FFDTW [Access]＝FDTW(α Ft ,α F [Access])；
wherein alpha is Ft The capital letter F of the middle subscript indicates that the object to which the azimuth sequence belongs is a detector, and the subscript letter t indicates the current observation data;
solar pitch angle sequence beta for rapidly calculating current observation data St And dynamic time warping distance of pitch angle sequences of all Access sun in the first space target database Dom1; wherein, the sun pitch angle sequence beta of the current observation data is rapidly calculated St And the pitch angle sequence beta of the sun at the Access time in the first space target database Dom1 S [Access]The dynamic time warping distance of (a) is:
β SFDTW [Access]＝FDTW(β St ,β S [Access])；
wherein beta is St The capital letter S of the middle subscript indicates that the object to which the pitch angle sequence belongs is the sun, and the subscript letter t indicates the current observation data;
quickly calculating pitch angle sequence beta of current observation detector Ft And dynamic time warping distance of pitch angle sequences of all Access detectors in the first space target database Dom1; wherein, the pitch angle sequence beta of the current observation detector is rapidly calculated Ft And the pitch angle sequence beta of the detector at the Access time in the first space target database Dom1 F [Access]The dynamic time warping distance of (a) is:
β FFDTW [Access]＝FDTW(β Ft ,β F [Access])；
wherein, beta Ft The capital letter F of the middle subscript indicates that the object to which the pitch angle sequence belongs is probeA measuring device, wherein a subscript letter t represents current observation data;
respectively according to alpha SFDTW 、α FFDTW 、β SFDTW 、β FFDTW The size sequence of the first space target database carries out four sorts on the Access in the first space target database to form four space target databases Dom11, dom12, dom13 and Dom14, and front n (n is more than or equal to o) in the Dom11 to Dom14 is extracted simultaneously 1 ) The Access forms a second space target database, wherein the second space target database is required to contain o 2 Access, each occurrence four times, wherein o 2 And the input quantity is used for controlling the number of the accesses of the second space target database.
8. The method of claim 1, wherein in the seventh step, the distance calculation method uses a method of fast calculating the DTW distance, the method comprising:
quickly calculating luminosity data M of current observation t Obtaining a calculation result matrix MAG by the dynamic time warping distance of photometric data of a model with different postures from the space target in the second space target database; wherein the currently observed photometric data M is rapidly calculated t The dynamic time warping distance between the first space target database and the luminosity data of the jth Access of the ith posture model in the second space target database is as follows:
MAG i,j ＝FDTW(M t ,M i,j )；
where M denotes photometric data, subscript t denotes current observation data, j =1,2 2 ，i＝1,2,...T，o 2 The number of accesses of the second spatial target database, and T is the number of types of the spatial target attitudes in the second spatial target database.
9. The method according to claim 1, wherein in step eight,
inputting the calculation result matrix MAG into a minimum value identification model, comprising:
selecting an attitude model corresponding to the minimum value of each column of the MAG matrix as a primary attitude identification result of the current observed data, and outputting the primary attitude identification result with the largest occurrence frequency as the attitude identification result of the current observed data;
inputting the calculation result matrix MAG into a mean recognition model, including:
and calculating the average value of each row of MAG, and outputting the attitude model with the minimum average value as the attitude recognition result of the current observed data.
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