CN108871347A - A kind of navigation sensor mounting configuration based on sight probability density determines method - Google Patents
A kind of navigation sensor mounting configuration based on sight probability density determines method Download PDFInfo
- Publication number
- CN108871347A CN108871347A CN201810195417.6A CN201810195417A CN108871347A CN 108871347 A CN108871347 A CN 108871347A CN 201810195417 A CN201810195417 A CN 201810195417A CN 108871347 A CN108871347 A CN 108871347A
- Authority
- CN
- China
- Prior art keywords
- probability density
- sensor
- navigation sensor
- sight
- mounting configuration
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/24—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for cosmonautical navigation
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Astronomy & Astrophysics (AREA)
- Automation & Control Theory (AREA)
- General Physics & Mathematics (AREA)
- Navigation (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention discloses a kind of navigation sensor mounting configurations based on sight probability density to determine method, belongs to field of flight vehicle design.Celestial body be will be responsible in deep space exploration task each stage relative to projecting under detector body system after the position discretization of detector；The concept of sight probability density is introduced, the probability that each constraint celestial body appears in detector all directions in the task complete period is calculated；High probability and low probability region are obtained after obtained sight probability density image is carried out expansion process；Using the high/low probability region of corresponding celestial body as Search Range, final optimization pass is to obtain the optimal mounting configuration of navigation sensor.The present invention compensates for the optimal mounting configuration of navigation sensor in existing complicated deep space exploration task and determines difficulty, needs the disadvantages of iterating, Searching efficiency is greatly improved, the quick determination of the optimal installation position of navigation sensor suitable for complicated deep space exploration task.
Description
Technical field
The present invention relates to field of flight vehicle design more particularly to a kind of quick determining complicated deep space exploration task navigation are sensitive
The method that the optimal mounting configuration of device quickly determines.
Background technique
It is reported that deep space exploration task often exists, duty cycle is long, task object is more, apart from the earth far and communication control
The features such as difficult, this is but also detector greatly increases the dependence of autonomous navigation technology.Navigation sensor is to detector week
The perception in collarette border is basis and the premise of autonomous navigation technology, and therefore, deep space probe often carries many kinds of, quantity
Biggish all types of navigation sensors.Due to every kind of navigation sensor direction target and evade constraint and be all not quite similar, in addition
The complexity of detection mission itself, so that the determination of navigation sensor mounting configuration is at a urgent need to solve the problem.With
The starting of China's deep space exploration task, the requirement for Spacecraft Autonomous Navigation are continuously improved.The present invention from this demand,
Propose a kind of method that can rapidly and accurately determine the optimal mounting configuration of navigation sensor.
The design and manufacture of deep space probe are an extremely complex multidisciplinary optimization processes.Wherein, navigation subsystem
Design be the core work for being related to entire detection mission win or lose.Navigation sensor is most important group of navigation subsystem
At part, therefore, the selection of sensor and mount scheme require to carry out strict research and demonstration.Due to detector ruler itself
Very little and quality limitation, in addition the position and attitude variation of detector is acutely and frequent during detection mission, so that navigation is sensitive
The determination of the optimal installation site of device and direction is at a technical problem urgently to be solved.Domestic and foreign scholars ask for this at present
The processing mode of topic is often using the thinking for combining mathematical modeling and traditional optimizing algorithm.For example, appoint in BepiColombo
In business, the position vector that scholars just use by the target for needing to observe relative to spacecraft is projected as the elevation angle and azimuth,
And determine that the installation of scientific load is directed toward by the situation of change of tracking angle.By phased mission system piecemeal, carry out modularization
Modeling is also that by the further analysis to task phase it is lower that impact factor in each stage is isolated in a desirable thinking
Celestial body and detector parts so that the searching process of multidisciplinary optimization, which is controlled in a lesser range, finds part most
Excellent solution, also the exploitation for related simulation engine and software is provided convenience.There are also scholars to propose the research think of in knowledge based library
The direction of search of optimizing algorithm is guided by the data experience of previous task in road, thus play improve Searching efficiency and
The purpose of accuracy.Scheme is followed for the first angle curve, due to there is no the form using fitting and analytic solutions, so that this
The simulation accuracy of kind scheme is higher, is well suited for the determination being directed toward with single goal single-sensor.But it is more for multiple types multiple target
The coupling of sensor optimizes, and this method often haves the shortcomings that calculation amount is excessive, efficiency is lower.For segmentation blocking modeling
Thought, due to " impact factor " concept of each celestial body of each stage and detector subsystem be it is artificially determining, lead to this
The foundation of kind isolation processing will appear the situation improperly worked as unavoidably, in addition modularization optimizing is only the part for seeking each stage
Optimal solution causes the detection efficient of sensor relatively low.Finally for the thought of knowledge base, since the deep space exploration in China has just risen
Step, knowledge base is still incomplete, and knowledge base itself is only to play search direction impulse, and what is substantially played is still to reduce
The effect of region of search, therefore modeling strategy for core and the integrated of optimizing algorithm have no direct help.
Summary of the invention
The present invention is proposed and a kind of is based on sight for the engineering duty demand and the deficiency of technology at this stage in current China
The optimal mounting configuration fast determination method of the complicated deep space exploration task navigation sensor of probability density and graphics expansion, by drawing
Enter sight probability density concept, fitting building probability density function, to greatly improve the search efficiency of optimizing algorithm and accurate
Property, solve problems of the prior art.
The invention is realized in this way：
A kind of navigation sensor mounting configuration based on sight probability density determines method, and steps are as follows：
Step 1：The relative motion of related celestial body and detector in the deep space exploration task complete period is subjected to discretization,
And unit sight vector obtained is all projected into detector body coordinate system；To own in the deep space exploration task complete period
Related celestial body constructs relative motion data library relative under the position projective transformation to detector body coordinate system of detector,
And by analyzing the sight probability density in all directions, the rough solution for providing navigation sensor installation is calculated as accurate optimizing
The initial solution of method.
Step 2：Whole unit sight vector is carried out data to be normalized to obtain sight probability density letter
Number, is considered as image for probability density function, and carry out graphics expansion processing；Since probability density is one about azimuth and faces upward
Probability density function can be considered as image, and carry out graphics expansion processing by the binary function of two parameters in angle.By means of probability
The thought of density, correlation celestial bodies all in the deep space exploration task complete period are compressed relative to the position data of detector,
And sight probability density function is fitted, probability density image is subjected to expansion process and by the high/low Probability Region of gained image
Searching efficiency and mesh that is accurate, quickly determining the optimal mounting configuration of navigation sensor are improved as Search Range, to play in domain
's.
Step 3：The minimum and highest pixel of probability is individually listed in probability density matrix, and passes through the pixel and its week
The mode for enclosing pixel difference fitting, estimates the accurate coordinates of very big/minimum of probability density function；
Step 4：Using coordinate obtained in step 3 as the initial guess solution of optimization algorithm, to reach diminution region of search
Purpose, and use genetic algorithm (GA) carry out final optimization pass to obtain the optimal mounting configuration of navigation sensor.
Further, the step two is specific as follows：
2.1, initialization, by step 1 all sight vector azimuth for being converted under detector body system of projection and
Elevation information；
2.2, by 0-360 ° of azimuth, detector body system projection localization is 360x180 pixel by -90-90 ° of the elevation angle
Image, and the number of the appearance of each celestial body sight vector in each pixel is counted, and with the celestial body in detector body system
Under total frequency of occurrence do normalized, gained matrix is denoted as n；
2.3, iteration initialization, number k=1；
2.4, using 3x3 filter to matrix convolution, to play the role of compressing information.To the matrix square after convolution
Battle array maximum value is normalized, and the number of iterations k++, 3x3 filter φ is：
Wherein, k is constant, value 1, and operation max (M) refers to taking the maximum value of matrix M, and i, j respectively represent matrix M
Current line number and columns, a are the maximum value of current matrix, and err is that current matrix makes the difference with its maximum value, from filter formula
It can be seen that, filter uses the situation of a saturation function.For an element of matrix M, poor err is bigger, then filter is herein
The weight of element is lower.It follows that gray scale map values are higher, then the influence after filter processing to ambient enviroment is bigger, thus
Expanding image is achieved the effect that；
2.5, if the number of iterations reaches maximum value, algorithm stops, and exports result；Otherwise the 2.4th step is returned to, until repeatedly
Generation number reaches maximum value.
Further, the step three is specially：The accurate coordinates of very big/minimum of probability density function will be estimated
As optimizing algorithm initial solution；And the point centered on pixel where this coordinate, 20 pixel of transverse and longitudinal is chosen corresponding to inner region
Angle value is as region of search.Specific area size can be determined by pixel quantity, can be adjusted according to mission requirements.
Further, the step four is specially：By the initial solution and region of search input genetic algorithm in step 3, and make
Accurate optimizing is carried out with algorithm, optimizing model is defined as follows：
Wherein, a, b are the region of search of optimizing algorithm, N (Υ (Θi)) be i-th of sensor the light disturbance function of time；
O(Υ(Θi)) be i-th of sensor normal working hours function；The two functions collectively form the work of navigation sensor
Efficiency index function.The definition of sensor i is hereinafter by formula (7) explanation.k1And k2Respectively maximum index and minimum refer to
Target weighted value.ag(Υ(Θi),Υ(Θj)) it is the operator for calculating angle between two vectors.A (Υ (Θ)) represents constraint day
Body enters the time of respective sensor visual field, and Υ (Θ) is converted setting angle Θ as the operator of unit sight vector.Index
Function is to need normal observation time longest and light disturbance time most short, is specifically defined as：
Wherein, O, N are respectively the normal observation time and light disturbance time of navigation sensor；
θiWithThe field angle and veiling glare for respectively representing sensor i avoid angle；fiWithRespectively represent the direction of sensor i
The direction of visual lines of direction and celestial body j under detector body system；τiThe biography is represented for the running parameter of different moments sensor, 0
This moment of sensor does not work normally, and 1 represents this moment of sensor normal work.Coefficient aiIt is quick to represent different types of navigation
The target function of sensor, every kind of sensor is defined according to the working principle and demand of sensor, and coefficient index is as optimizing
Basis of characterization when algorithm calls, such as：
The optimizing result of final output is the mounting configuration of each navigation sensor.In view of navigation sensor type
It with the diversity of quantity, establishes and the database of demand is directed toward and evaded comprising different type sensor, and by related celestial body
Feasible zone of the probability function as final optimizing carries out quick optimizing using genetic algorithm.
The beneficial effect of the present invention compared with the existing technology is：
1) thought for utilizing sight probability density, by correlation celestial bodies all in the deep space exploration task complete period relative to detection
The position data of device is compressed, and fits probability density function, and the field of very big/minimum of probability density function is made
For Search Range, to quickly determine navigation sensor mounting configuration；
2) present invention by correlation celestial bodies all in complicated deep space exploration duty cycle and is detected by kinematics projective transformation
The relative motion of device is projected under detector body coordinate system and is studied；Sight probability density concept is introduced, fitting building is general
Rate density function.By the method for probability density function graphics expansion, high probability and the low probability area of correlation celestial body sight are determined
Domain, and using this region as algorithm search domain, to greatly improve the search efficiency and accuracy of optimizing algorithm；
3) present invention introduces sight probability density concepts, by constructing sight probability density function and pattern swelling method,
Direction of visual lines high probability and low probability region have been obtained, the database of all positions of heavenly body in the task complete period is not needed, thus
Greatly reduce calculation amount；The region of search for no longer needing manually to determine optimizing algorithm by way of experience or target practice, improves
The efficiency and accuracy of searching process；
4) searching process is modeled by way of probability density weighted score, no longer using traditional based on minimum
The optimizing index of angle and maximum observation time, improves the compatibility of model and optimizing algorithm.
Detailed description of the invention
Fig. 1 is projection of the relative position of all celestial bodies and detector under detector body system in task overall process；
Fig. 2 is distribution of the probability density of each celestial body in the present invention under detector body system；
Fig. 3 is throwing of mounting configuration of the various navigation sensors after optimizing under detector body system in the present invention
Shadow；
Fig. 4 be in the present invention mounting configuration of the various navigation sensors after optimizing in the effect picture of three-dimensional space；
Fig. 5 is that various navigation sensors are escaped after optimizing in the present invention and cruise section constrains celestial body and navigation sensor
The variable angle of sight；
Fig. 6 is that various navigation sensors constrain celestial body and navigation sensor sight around section after optimizing in the present invention
Variable angle.
Specific embodiment
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the principle of the present invention, several improvement can also be made, these improvement also should be regarded as of the invention
Protection scope.
The method of the present invention is largely divided into three steps：
Step 1：By the sun in the deep space exploration task complete period, the relative motion of the correlation celestial body such as earth and detector into
Row discretization, and unit sight vector obtained is all projected into detector body coordinate system；It is complete for task as shown in Figure 1
Projective distribution figure of the position sight vector of all correlation celestial body opposing detectors under detector body system in period.
Step 2：Whole sight vector is carried out data to be normalized to obtain sight probability density function, by
It is a binary function about two parameters in azimuth and the elevation angle in probability density, probability density function can be considered as figure
Picture, and carry out graphics expansion processing.
As shown in Fig. 2, Fig. 2 is constructed after carrying out expansion process to celestial body sight vector obtained in step 1
Probability density figure.Wherein the detailed process of graphics expansion is as follows：
1) it initializes, all sight vector in step 1 are projected into the azimuth being converted under detector body system and faces upward
Angle information.
2) 0-360 ° of azimuth is pressed, detector body system projection localization is the figure of 360x180 pixel by -90-90 ° of the elevation angle
Picture, and the number of the appearance of each celestial body sight vector in each pixel is counted, and with the celestial body under detector body system
Total frequency of occurrence do normalized, gained matrix is denoted as n.
3) iteration initialization, number k=1
4) using 3x3 filter to matrix convolution, to play the role of compressing information.To the matrix matrix after convolution
Maximum value is normalized, and the number of iterations k++, 3x3 filter φ is：
Wherein, k is constant, value 1, and operation max (M) refers to taking the maximum value of matrix M.
If 5) the number of iterations reaches maximum value, algorithm stops, and exports as a result, otherwise returning to step 4.
Step 3：The minimum and highest pixel of probability in probability density matrix will be obtained in step 2 individually to list, and is led to
The mode for crossing the pixel and the fitting of its surrounding pixel difference, estimates the accurate coordinates of very big/minimum of probability density function,
Using this coordinate as optimizing algorithm initial solution；And the point centered on pixel where this coordinate, 20 pixel of transverse and longitudinal is chosen with inner region
Corresponding angle value is as region of search.Above-mentioned initial solution and region of search are inputted into genetic algorithm, and carried out accurately using algorithm
Optimizing, optimizing model are defined as follows：
Wherein, a, b are the region of search of optimizing algorithm, and N (Υ (Θ)) and O (Υ (Θ)) are variety classes navigation sensor
Working efficiency target function, is specifically defined as：
Wherein, O, N are respectively the normal observation time and light disturbance time of navigation sensor.Target function in formula (2)
Meaning is to need normal observation time longest and light disturbance time most short.θiWithRespectively represent the field angle of sensor i and miscellaneous
Light avoids angle；fiWithRespectively represent the direction of visual lines of the pointing direction and celestial body j of sensor i under detector body system.τi
For the running parameter of different moments sensor, 0, which represents this moment of the sensor, is not worked normally, and 1 represents the sensor at this time
It carves and works normally.Coefficient aiRepresent different types of navigation sensor, the target function of every kind of sensor is according to the work of sensor
Make principle and demand is defined, basis of characterization when coefficient index is called as optimizing algorithm, such as：
The optimizing result of final output is the mounting configuration of each navigation sensor.
Illustrate the process for using of algorithm below by way of specific example；
Set following design conditions and technical parameter：
1) task scene is defined as mars exploration task；Optimization goal is to improve including earth escape, cruise, Mars capture
Work gross efficiency with the navigation sensor to berth, Mars is surround under four-stage；
2) in task process each stage, detector flight track and posture direction have determined and cannot change, it is assumed that
All navigation sensors are switched on work in task overall process.Working efficiency definition rule is：
A) when the target that navigation sensor alignment needs to observe, and any work constraint is not violated, then it is assumed that this moment
Sensor journey work, otherwise it is assumed that this moment sensor does not obtain valid data.
B) within the task complete period, the normal work total time of all navigation sensors is longer, then it is assumed that sensor system
Working efficiency is higher.
3) navigation sensor is divided into 3 kinds：Sun sensor 1, star sensor 4, it is seen that light camera and infrared senstive device
Each 1.The direction demand and constraint definition of every kind of sensor are as shown in table 1；
1. navigation sensor work requirements of table and constraint
4) all navigation sensors are connected with detector body, and lack of competence controls the posture of detector, because
This is merely able to carry out the extend as far as possible sensor as far as possible in full duty cycle by the mounting configuration for optimizing navigation sensor
Operable time；
5) other than directing constraint, star sensor and navigation camera have veiling glare that angle is avoided to constrain, if constraining celestial body
Angle is avoided into veiling glare, corresponding sensor can work but measurement accuracy can be made to reduce, therefore avoid constraint day as far as possible
While body enters navigation sensor visual field, it is also necessary to which reduction constraint celestial body rests on sensor veiling glare and avoids in angle as far as possible
Time.This demand is embodied in formula (2) target function with the mode of weighting, the field range of each navigation sensor and miscellaneous
Light avoids angle value as shown in table 2；
2. navigation sensor visual field of table, that is, veiling glare avoids angle
6) it for star sensor and navigation camera, since number is more than 1, also needs to increase constraint：Every two star sensors
Field range must not be overlapped, the field range of navigate camera and infrared sensor must not be overlapped, and be seen with this to further increase
The precision of survey；
7) task orbit parameter uses for reference the parameter that NASA Mars Rover 2020 is planned, and initial time epoch is set to 2020
Year, Mars capture time 2021, around 3 years mission durations after capture；
8) preliminary orbit samples point step size 1s；
9) it is based on 1~8, data compression and graphics expansion calculate maximum number of iterations 50；I.e. 50 iteration are greatest iteration
The parameter value of number stops iteration.
Design conditions and technical parameter based on optimization method of the invention and above-mentioned setting, it is soft using Matlab and STK
Part carries out simulating, verifying.By iteration and expansion, it can be clearly seen that the position of different celestial body opposing detectors exists from Fig. 2
Probability distribution during entire task.Color more levels off to white in figure, then it represents that the probability that celestial body occurs in this region
It is bigger, more level off to black, then it represents that celestial body is smaller in the probability that this region occurs.It, can according to the demand of different sensors
Know that sun sensor should be mounted on the white area of sun probability density as far as possible；Camera should be mounted on the white of planet probability density
Color region；And star sensor then needs to avoid all white areas, selects black region.On this basis, optimizing can be calculated
The region of search of method：
Wherein, ΘiFor the setting angle of i, ground sensor；Each setting angle is a bivector, respectively indicates peace
Fill inclination angle and azimuth.minΘiWith max ΘiThe min/max at inclination angle and azimuth region of search is then stated, in corresponding (2)
A, b.After genetic algorithm optimizing, the solution of Fig. 3 and Fig. 4 are obtained.1 is star sensor mounting configuration in Fig. 4, and 2 be the sun
Sensor mounting configuration, 3 and 4 be respectively the mounting configuration of Visible Light Camera and infrared senstive device.From figs. 3 and 4 it can be seen that
The optimizing position of all sensors and the probability density distribution in Fig. 2 coincide：Sun sensor and camera are distributed in the top of Fig. 2
With the two of bottom at the higher region of probability density；And star sensitive periods, is then distributed in the lower region of probability density.From Fig. 5's -6
Verifying analysis can be seen that, celestial body is constrained during full task and does not enter into sensor visual field.Only around the of short duration entrance of section mid-term
Star sensor veiling glare avoids angle, therefore it is concluded that：Optimizing result meets mission requirements, it is ensured that all navigation sensors
There can be preferable working efficiency.
Claims (5)
1. a kind of navigation sensor mounting configuration based on sight probability density determines method, which is characterized in that steps are as follows：
Step 1：The relative motion of related celestial body and detector in the deep space exploration task complete period is subjected to discretization, and will
Unit sight vector obtained is all projected into detector body coordinate system；
Step 2：Whole unit sight vector is carried out data to be normalized to obtain sight probability density function, it will
Probability density function is considered as image, and carries out graphics expansion processing；
Step 3：The minimum and highest pixel of probability in sight probability density function probability density matrix is individually listed, and is led to
The mode for crossing the pixel and the fitting of its surrounding pixel difference, estimates the accurate coordinates of very big/minimum of probability density function；
Step 4：Using coordinate obtained in step 3 as the initial guess solution of optimization algorithm, and using genetic algorithm (GA) into
Row final optimization pass is to obtain the optimal mounting configuration of navigation sensor.
2. a kind of navigation sensor mounting configuration based on sight probability density according to claim 1 determines method,
It is characterized in that, the step two is specific as follows：
2.1, all sight vector in step 1 are projected the azimuth and the elevation angle being converted under detector body system by initialization
Information；
2.2, by 0-360 ° of azimuth, detector body system projection localization is the image of 360x180 pixel by -90-90 ° of the elevation angle,
And the number of the appearance of each celestial body sight vector in each pixel is counted, and total under detector body system with the celestial body
Frequency of occurrence does normalized, and gained matrix is denoted as n；
2.3, iteration initialization, number k=1；
2.4, using 3x3 filter to matrix convolution, the matrix after convolution is normalized with matrix maximum value, the number of iterations
K++, 3x3 filter φ is：
Wherein, k is constant, value 1, and operation max (M) refers to taking the maximum value of matrix M, and it is current that i, j respectively represent matrix M
Line number and columns, a be current matrix maximum value, err be current matrix made the difference with its maximum value；
2.5, if the number of iterations reaches maximum value, algorithm stops, and exports result；Otherwise the 2.4th step is returned to, until iteration time
Number reaches maximum value.
3. a kind of navigation sensor mounting configuration based on sight probability density according to claim 1 determines method,
It is characterized in that, the step three is specially：Using the accurate coordinates for estimating very big/minimum of probability density function as seeking
Excellent algorithm initial solution；And the point centered on pixel where this coordinate, 20 pixel of transverse and longitudinal is chosen with angle value corresponding to inner region
As region of search.
4. a kind of navigation sensor mounting configuration based on sight probability density according to claim 1 determines method,
It is characterized in that, the step four is specially：By the initial solution and region of search input genetic algorithm in step 3, and use algorithm
Accurate optimizing is carried out, optimizing model is defined as follows：
Wherein, a, b are the region of search of optimizing algorithm, N (γ (Θi)) be i-th of sensor the light disturbance function of time；O(γ
(Θi)) be i-th of sensor normal working hours function；The two functions collectively form the working efficiency of navigation sensor
Target function；k1And k2The respectively weighted value of maximum index and minimum index；ag(γ(Θi),γ(Θj)) it is to calculate
The operator of angle between two vectors, A (γ (Θ)) represent the time that constraint celestial body enters respective sensor visual field, and γ (Θ) is
Setting angle Θ is converted as the operator of unit sight vector.
5. a kind of navigation sensor mounting configuration based on sight probability density according to claim 4 determines method,
It is characterized in that, the target function is to need normal observation time longest and light disturbance time most short, is specifically defined as：
Wherein, O, N are respectively the normal observation time and light disturbance time of navigation sensor；θiWithRespectively represent sensor i
Field angle and veiling glare avoid angle；fiWithThe pointing direction and celestial body j for respectively representing sensor i are under detector body system
Direction of visual lines；τiFor the running parameter of different moments sensor, 0, which represents this moment of the sensor, is not worked normally, and 1 represent should
This moment of sensor works normally；Coefficient aiDifferent types of navigation sensor is represented, coefficient index is called as optimizing algorithm
When basis of characterization, the optimizing result of final output is the mounting configuration of each navigation sensor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810195417.6A CN108871347B (en) | 2018-03-09 | 2018-03-09 | Navigation sensor installation configuration determination method based on sight probability density |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810195417.6A CN108871347B (en) | 2018-03-09 | 2018-03-09 | Navigation sensor installation configuration determination method based on sight probability density |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108871347A true CN108871347A (en) | 2018-11-23 |
| CN108871347B CN108871347B (en) | 2022-04-05 |
Family
ID=64326057
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810195417.6A Active CN108871347B (en) | 2018-03-09 | 2018-03-09 | Navigation sensor installation configuration determination method based on sight probability density |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108871347B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101462597A (en) * | 2009-01-15 | 2009-06-24 | 哈尔滨工业大学 | Medication maneuvering time selecting method for deep space detector approaching orbit |
| CN103954287A (en) * | 2014-03-31 | 2014-07-30 | 北京控制工程研究所 | Route sign planning method of deep space detection autonomous navigation |
| CN104252548A (en) * | 2013-06-27 | 2014-12-31 | 上海新跃仪表厂 | Method of designing injection target point of Mars probe with optimal fuel |
| CN105865459A (en) * | 2016-03-31 | 2016-08-17 | 北京理工大学 | Visual angle constraint-considered small heavenly body approaching section guidance method |
| CN106549396A (en) * | 2016-12-08 | 2017-03-29 | 中国电力科学研究院 | A kind of power distribution network multiple target probability idle work optimization method |
-
2018
- 2018-03-09 CN CN201810195417.6A patent/CN108871347B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101462597A (en) * | 2009-01-15 | 2009-06-24 | 哈尔滨工业大学 | Medication maneuvering time selecting method for deep space detector approaching orbit |
| CN104252548A (en) * | 2013-06-27 | 2014-12-31 | 上海新跃仪表厂 | Method of designing injection target point of Mars probe with optimal fuel |
| CN103954287A (en) * | 2014-03-31 | 2014-07-30 | 北京控制工程研究所 | Route sign planning method of deep space detection autonomous navigation |
| CN105865459A (en) * | 2016-03-31 | 2016-08-17 | 北京理工大学 | Visual angle constraint-considered small heavenly body approaching section guidance method |
| CN106549396A (en) * | 2016-12-08 | 2017-03-29 | 中国电力科学研究院 | A kind of power distribution network multiple target probability idle work optimization method |
Non-Patent Citations (2)
| Title |
|---|
| 李捷: "红外地球敏感器两种安装构型的性能比较", 《中国空间科学技术》 * |
| 马红亮等: "多星敏感器测量最优姿态估计算法", 《北京航空航天大学学报》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108871347B (en) | 2022-04-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101598556B (en) | Unmanned aerial vehicle vision/inertia integrated navigation method in unknown environment | |
| CN102426019B (en) | Unmanned aerial vehicle scene matching auxiliary navigation method and system | |
| US8260734B2 (en) | Method and a system for estimating the impact area of a military load launched from an aircraft | |
| CN106094569A (en) | Multi-sensor Fusion unmanned plane perception with evade analogue system and emulation mode thereof | |
| CN104406589B (en) | Flight method of aircraft passing through radar area | |
| CN114332385A (en) | Monocular camera target detection and spatial positioning method based on three-dimensional virtual geographic scene | |
| CN110223351A (en) | A kind of depth camera localization method based on convolutional neural networks | |
| CN111829964A (en) | Distributed remote sensing satellite system | |
| Mejias et al. | Forced landing technologies for unmanned aerial vehicles: towards safer operations | |
| CN108364279A (en) | Determine the method that stationary orbit remote sensing satellite is directed toward deviation | |
| Delaune et al. | Visual–inertial navigation for pinpoint planetary landing using scale-based landmark matching | |
| CN105023281B (en) | Asterism based on point spread function wavefront modification is as centroid computing method | |
| CN103743488B (en) | Infrared imaging simulation method for globe limb background characteristics of remote sensing satellite | |
| CN112857306B (en) | Method for determining continuous solar altitude angle of video satellite at any view direction point | |
| Ruel et al. | 3DLASSO: Real-time pose estimation from 3D data for autonomous satellite servicing | |
| CN109708643A (en) | Asteroid surface optical navigation road sign evaluates choosing method | |
| CN105547286A (en) | Composite three-view-field star sensor star map simulation method | |
| CN105911614B (en) | A kind of sunshine time computational methods based on data buffer storage | |
| CN109657679B (en) | Application satellite function type identification method | |
| CN107727102A (en) | Astronomy test the speed combined with terrestrial radio Mars capture section air navigation aid | |
| CN108871347A (en) | A kind of navigation sensor mounting configuration based on sight probability density determines method | |
| Humphreys et al. | Open-world virtual reality headset tracking | |
| CN103148840A (en) | Extraction method of barycentric coordinate of earth ultraviolet image | |
| CN113916217A (en) | Star positioning method based on partitioned stratosphere atmospheric refraction model | |
| CN113686361A (en) | Mars detection sky-ground collaborative navigation ground verification system and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |