CN111487660A - High-precision real-time micro-nano satellite cluster navigation algorithm - Google Patents
High-precision real-time micro-nano satellite cluster navigation algorithm Download PDFInfo
- Publication number
- CN111487660A CN111487660A CN202010329840.8A CN202010329840A CN111487660A CN 111487660 A CN111487660 A CN 111487660A CN 202010329840 A CN202010329840 A CN 202010329840A CN 111487660 A CN111487660 A CN 111487660A
- Authority
- CN
- China
- Prior art keywords
- satellite
- navigation
- satellites
- cluster
- gnss
- 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
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/421—Determining position by combining or switching between position solutions or signals derived from different satellite radio beacon positioning systems; by combining or switching between position solutions or signals derived from different modes of operation in a single system
- G01S19/423—Determining position by combining or switching between position solutions or signals derived from different satellite radio beacon positioning systems; by combining or switching between position solutions or signals derived from different modes of operation in a single system by combining or switching between position solutions derived from different satellite radio beacon positioning systems
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention provides a micro-nano satellite cluster distributed navigation scheme based on a Global Navigation Satellite System (GNSS) single-frequency receiver, a micro-nano satellite cluster absolute/relative orbit dynamics model suitable for high-precision navigation is constructed, a fusion mechanism of single-frequency combined observation data and carrier phase difference data is established, and a high-precision micro-nano satellite cluster navigation algorithm is further designed. The invention adopts the idea of distributed navigation, namely each satellite in the cluster can send own GNSS measurement information to other satellites, and each satellite can utilize the received other satellites and the own GNSS measurement information to navigate all the satellites in the cluster. The specific implementation method of the invention is to utilize the GNSS observed quantity and the relative position vector obtained based on the carrier wave difference to carry out correction based on Kalman filtering on the estimation quantity of state quantities such as satellite position, speed and the like obtained by utilizing the dynamic model calculation, thereby obtaining the high-precision absolute and relative position information of the satellite cluster and realizing the purpose of carrying out high-precision navigation on the satellite cluster. The invention has the advantages of high navigation precision and good real-time performance.
Description
Technical Field
The invention provides a micro-nano Satellite cluster Navigation algorithm with higher precision and real-time performance, and relates to a method for performing high-precision Navigation on a Satellite cluster by utilizing a GNSS (Global Navigation Satellite System) broadcast ephemeris, single-frequency pseudo-range and carrier phase information and inter-Satellite relative position vectors obtained based on carrier differential calculation and utilizing extended Kalman filtering. Belonging to the technical field of navigation.
Background
The research proves that the satellite precision orbit determination technology based on the GNSS can be traced back to the feasibility research of applying the GPS to carry out spacecraft navigation in 1975 by the Lockschid missile company in the 1975, the satellite-borne GPS receiver is completely feasible to carry out navigation below the orbit height of 800km, and the navigation under higher orbits also has huge research and development space, in 1982, the satellite L and Sat-4 of the earth resource in the United states uses the satellite-borne GPS receiver to realize the navigation precision with the position error less than 50m under the condition of no SA interference, and the feasibility of applying the GPS technology in the field of spacecraft navigation is proved.
With the development of decades, the low-earth orbit satellite orbit determination technology based on GNSS has become mature. Currently, the orbit determination method is divided into two methods, one is a reduced kinetics method, and the other is a kinematic method.
The reduced dynamics method was originally proposed by scholars such as T.P.Yunck, S.CWu et al and 1986, and was applied to the actual satellite orbit determination task. The method fuses the orbit dynamics equation of the satellite and the GPS observation equation, and performs optimal solution between the dynamics information and the geometric information, thereby weakening the error influence of the dynamics model, and realizing continuous orbit determination under the condition of poor GPS satellite geometric distribution or data interruption. The method gives full play to the advantages of both satellite orbit dynamics and GPS measurement, and the provided orbit solution is the satellite mass center. The orbit accuracy of the reduced dynamics approach to centimeter level has been verified by orbit determination of the GOCE satellites as early as 2000 by using dual-frequency GPS to receive data from p.n.a.m Visser and j.van den Ijssel.
In 2002, Chiaradia researches a method for performing real-time reduced dynamic orbit determination on a Topex/Poseidon satellite by using single-frequency GPS data, achieves the orbit determination precision of tens of meters, and verifies the feasibility of performing real-time orbit determination on a low-orbit satellite by using a cheap single-frequency GPS receiver.
Nowadays, a post-processing reduced dynamics orbit determination method is very mature, the orbit determination precision is high, the reduced dynamics orbit determination method based on single frequency and real time is also widely researched, but the reduced dynamics method has the most essential problems of complex calculation, large calculation amount, influence by the precision of a dynamics model and the like, so that the reduced dynamics method cannot be well applicable to the situations of small satellites with limited task budget and satellite resources, micro-nano satellite orbit determination tasks, satellite orbit change and the like. Thus, GNSS kinematic orbit determination methods that are more versatile and relatively less studied should receive more extensive attention.
A kinematic orbit determination method based on GNSS is a method for positioning and resolving only depending on GNSS pseudo-range information and carrier phase information received by a receiver. The orbit solution provided by the method is the position of the phase center of the antenna of the satellite-borne GNSS receiver.
In 2003, Montenbruck verifies the precision of the pure kinematics orbit determination method, and the method adopting single-frequency measurement data and GRAPHIC combined ionosphere elimination finally obtains 1-1.5m orbit determination three-dimensional error, thereby demonstrating the high-precision orbit determination feasibility of pure kinematics. However, compared to reduced kinetics methods, the kinematics tracking method is less studied, especially in the single-frequency real-time domain.
In summary, the invention provides a high-precision real-time micro-nano satellite cluster navigation algorithm, which corrects a state quantity predicted value obtained by an absolute/relative dynamics model by using GNSS broadcast ephemeris, single-frequency pseudo range and carrier phase information received by a receiver through an Extended Kalman Filter (EKF) to obtain a high-precision navigation result. And by utilizing a distributed design concept, the final high-precision navigation result of the satellite cluster is obtained by fusing the navigation result of each satellite in the satellite cluster.
Disclosure of Invention
Objects of the invention
The invention provides a distributed high-precision real-time navigation algorithm based on GNSS broadcast ephemeris, single-frequency pseudorange and carrier phase information, aiming at solving the problem of high-precision orbit determination of a micro-nano satellite cluster. The method has the advantages of high precision and good real-time performance, and is suitable for real-time absolute navigation and positioning scenes of small satellites or micro-nano satellite clusters by utilizing a GNSS receiver.
(II) technical scheme
For convenience of description, taking a satellite cluster formed by three stars A, B, C as an example, the implementation steps of the high-precision real-time micro-nano satellite cluster navigation algorithm provided by the invention are as follows:
the method comprises the following steps: computing state predictions for satellites in a cluster
The satellite A calculates to obtain a state prediction value of the position and the speed of the satellite A by using an absolute dynamic model, and calculates to obtain state prediction values of the satellite B and the satellite C in the cluster by using a relative dynamic model.
The resultant force of the acting forces received by the satellite in space can be decomposed into component forces in the x, y and z directions along a coordinate system, and is marked as Fx,Fy,FzThus, the absolute kinetic equation for a satellite can be written as:
expressed in vector form as:
wherein m represents the satellite whole satellite mass, and the ith perturbation force considered in the i-table type dynamic model.
The establishment of a relative kinetic model is discussed below. Taking satellite A, B as an example, the position vector of the two under the inertial system is rA＝(xA,yA,zA)T,rB＝(xB,yB,zB)TThus, the relative positions of the two can be expressed as:
rAB＝rA-rB(3)
each perturbation force can be expressed as a function f of a position vector ri(r) performing first order Taylor expansion on each perturbation force at A, and neglecting high order small quantity to obtain B perturbation force as:
considering the small distance dimension between the formation flying satellites a and B, only a first order taylor expansion can meet the accuracy requirement. Further, a relative kinetic model between the A star and the B star can be obtained as follows:
the space scale of the satellite cluster related by the invention is 100m to 100 km. Under such a spatial scale, the perturbation forces such as sunlight pressure, sun-moon attraction, atmospheric resistance and the like, which each satellite of the satellite cluster receives, can be considered to be equal in magnitude and direction, and can be counteracted in relative motion, so that only the term of earth attraction remains in the relative dynamic model, and further the relative dynamic model can be simplified as follows:
expressed in vector form as:
▽ g (r) thereinA) Is the gravity gradient at the location of satellite a.
Further, for a more general case, assuming that a satellite cluster is composed of a satellite a and N other satellites, a simplified absolute/relative integrated kinetic model can be obtained as follows:
the absolute state predicted value of the satellite A and the predicted values of the relative position vectors from the satellites B and C to the satellite A can be obtained through an absolute/relative dynamics model, and then the predicted values of the absolute positions of the satellites B and C are obtained.
Step two: all satellites receive GNSS measurement information and send self-carrier phase information to other satellites
The single-frequency GRAPHIC combined observation model is utilized to fuse the single-frequency pseudo range and the carrier phase information of the GNSS to the satellite A, so that the GNSS observed quantity after ionospheric delay is removed is obtained, and the single-frequency GRAPHIC combined measurement can be expressed as:
where, P represents a single frequency pseudorange measurement,
for carrier phase information containing ambiguity, ρ represents the true distance between the GNSS satellite and the satellite, t is the satellite clock error, NGThe degree of blur is a GRAPHIC blur,Gthe measurement noise is combined for the GRAPHIC, thereby obtaining absolute position measurement information of satellite A.
Furthermore, satellite a transmits GNSS carrier phase information obtained by itself to satellites B and C, while receiving GNSS carrier phase information transmitted by satellites B and C to satellite a.
Step three: obtaining a relative position vector using received GNSS carrier phase information
And predicting the states of the three satellites by using an absolute/relative dynamic model. And then, obtaining a predicted value of a relative position vector between the satellites by using the predicted value:
rAB0＝rA0-rB0(10)
wherein r isAB0Representing the relative position vector between satellites A and B, rA0，rB0The absolute positions of satellites a and B, respectively, predicted using a kinetic model.
And the constructed precise carrier phase double-difference equation can be used for eliminating or weakening the orbit error of the satellite, the clock error of a receiver and the influence of the refraction errors of an ionosphere and a troposphere so as to improve the positioning precision. According to the principle of integer ambiguity generation, the real value of the integer ambiguity should be a definite integer. After an initial set of real integer ambiguity solutions is obtained, a search region is created around the initial set of real integer ambiguity solutions, candidate integer solutions are selected in the search region by a method, and a final integer ambiguity solution is selected from the candidate solutions according to a certain criterion.
The double difference carrier phase equation is:
namely, it is
Wherein: xkFor relative position correction terms, N is double-difference integer ambiguity
The project uses the L AMBDA method to determine the carrier phase ambiguity N since the L AMDA method is a mature algorithm, the L AMDA algorithm will not be explained in detail here, and the relative position correction vector r is obtained therebyABAnd then obtaining an accurate relative position vector:
rAB＝rAB0+rAB(15)
step four: calculating high-precision navigation result by utilizing absolute/relative integrated navigation algorithm
According to the method, an Extended Kalman Filter (EKF) is utilized, a state equation is constructed by an absolute/relative dynamics model, a measurement equation is constructed by GNSS observed quantity, a predicted value of the state quantity is corrected, and a high-precision navigation result is obtained.
The state quantity X includes the position, velocity, carrier ambiguity and clock offset of each satellite of the satellite constellation, and is expressed as:
X＝(xA,yA,zA,vxA,vyA,vzA,NGA,ctA,xB,yB,zB,vxB,vyB,vzB,NGB,ctB,xC,yC,zC,vxC,vyC,vzC,NGC,ctC)T(16)
firstly, a state equation of a satellite state quantity X is constructed according to the selected dynamic model, and the position speed of the satellite calculated in the step one is used as a state quantity predicted value.
And constructing a measurement equation according to the GNSS measurement principle and the carrier differential measurement relative position vector principle.
Observed quantities are single-frequency GRAPHIC combined measurement:
and relative position vector:
the jacobian matrix H is thus obtained, where the rows corresponding to GNSS measurements can be expressed as:
where the row corresponding to the relative position vector may be represented as:
further, the Kalman filter recursion equation set is as follows:
wherein the content of the first and second substances,
is tk-1To tkThe state transition matrix of the time of day,
after linearization for the system, at tk-1The residual prediction value at a time is calculated,
is a system pair tkAn estimate of the residual of the time of day.
Is t after EKF treatmentkAnd (4) optimal estimation of the system state quantity at the moment, namely a navigation result.
Step five: repeating the steps from one to four
And for any epoch time, repeating the first step to the fourth step, and further obtaining the navigation results of all satellites in the satellite cluster at the time
Step six: high-precision navigation information obtained by fusing navigation results of satellite clusters
And step one to step five are also applied to the satellite B and the satellite C, so that three groups of state quantity estimated values based on the navigation method can be obtained at any epoch moment, and the three groups of state quantity estimated values are fused to obtain the high-precision navigation information of the satellite cluster.
Through the steps, a high-precision real-time navigation result based on the GNSS broadcast ephemeris, the single-frequency pseudo-range information and the carrier phase information can be obtained. Therefore, the requirement of the micro-nano satellite cluster for realizing high-precision real-time navigation by utilizing a GNSS receiver can be met.
(III) advantages
The high-precision real-time micro/nano satellite cluster navigation algorithm provided by the invention has the advantages that:
① the algorithm provided by the invention adopts a distributed concept, that is, each satellite in the satellite cluster can obtain the high precision navigation result of the satellite cluster through the algorithm, the design concept can avoid the situation that the navigation can not be realized due to the fault of a certain satellite, and simultaneously, the precision of the navigation result of the satellite cluster can be further improved by fusing the navigation result of each satellite in the cluster.
② the algorithm provided by the invention uses GNSS single frequency pseudo range information and carrier phase information at the same time, compared with the navigation algorithm only using GNSS pseudo range information, the algorithm has higher navigation precision, and the GNSS receiving antenna used for receiving the single frequency pseudo range information is more suitable for carrying micro-nano satellites from the view of volume and mass or power consumption.
Drawings
FIG. 1 is a flow chart of the steps of the present invention
FIG. 2 is a flow chart of expanding a Kalman filter
Detailed Description
The following will describe in further detail the implementation of the present invention by taking a satellite cluster formed by three stars A, B, C as an example, with reference to fig. 1 and the technical solution.
The method comprises the following steps: computing state predictions for satellites in a cluster
The satellite A calculates to obtain a state prediction value of the position and the speed of the satellite A by using an absolute dynamic model, and calculates to obtain state prediction values of the satellite B and the satellite C in the cluster by using a relative dynamic model.
The resultant force of the acting forces received by the satellite in space can be decomposed into component forces in the x, y and z directions along a coordinate system, and is marked as Fx,Fy,FzThus, the absolute kinetic equation for a satellite is expressed in vector form as:
wherein m represents the satellite whole satellite mass, and the ith perturbation force considered in the i-table type dynamic model.
The space scale of the satellite cluster related by the invention is 100m to 100 km. Under such a spatial scale, the perturbation force such as sunlight pressure, gravity of the sun and moon, atmospheric resistance and the like received by each satellite of the satellite cluster can be considered to be equal in magnitude and direction, and can be counteracted in relative motion, so that only the gravity of the earth remains in the relative dynamic model, and further the vector form of the relative dynamic model can be simplified as follows:
▽ g (r) thereinA) Is the gravity gradient at the location of satellite a.
Further, for a more general case, assuming that a satellite cluster is composed of a satellite a and N other satellites, a simplified absolute/relative integrated kinetic model can be obtained as follows:
the absolute state predicted value of the satellite A and the predicted values of the relative position vectors from the satellites B and C to the satellite A can be obtained through an absolute/relative dynamics model, and then the predicted values of the absolute positions of the satellites B and C are obtained.
This step corresponds to the first block in figure one.
Step two: all satellites receive GNSS measurement information and send self-carrier phase information to other satellites
The GNSS is used for single-frequency GRAPHIC combined measurement of the satellite A, and the satellite A obtains the absolute measurement information of the satellite A.
Furthermore, satellite a transmits GNSS carrier phase information obtained by itself to satellites B and C, while receiving GNSS carrier phase information transmitted by satellites B and C to satellite a.
This step corresponds to the second block in figure one.
Step three: obtaining a relative position vector using received GNSS carrier phase information
Predicting the states of three satellites by using an absolute/relative dynamics model, and obtaining a predicted value of a relative position vector between the satellites by using the predicted value:
rAB0＝rA0-rB0(26)
wherein r isAB0Representing the relative position vector between satellites A and B, rA0，rB0The absolute positions of satellites a and B, respectively, predicted using a kinetic model.
And the constructed precise carrier phase double-difference equation can be used for eliminating or weakening the orbit error of the satellite, the clock error of a receiver and the influence of the refraction errors of an ionosphere and a troposphere so as to improve the positioning precision. According to the principle of integer ambiguity generation, the real value of the integer ambiguity should be a definite integer. After an initial set of real integer ambiguity solutions is obtained, a search region is created around the initial set of real integer ambiguity solutions, candidate integer solutions are selected in the search region by a method, and a final integer ambiguity solution is selected from the candidate solutions according to a certain criterion.
The project uses the L AMBDA method to determine the carrier phase ambiguity N since the L AMDA method is a mature algorithm, the L AMDA algorithm will not be explained in detail here, and the relative position correction vector r is obtained therebyABTo obtain an accurate relative position vector
rAB＝rAB0+rAB(27)
This step corresponds to the third block in figure one.
Step four: calculating high-precision navigation result by utilizing absolute/relative integrated navigation algorithm
According to the method, an Extended Kalman Filter (EKF) is utilized, a state equation is constructed by an absolute/relative dynamics model, a measurement equation is constructed by taking GNSS single-frequency GRAPHIC combined observation and carrier difference-based relative position vector observation as observed quantities, the state quantities are optimally estimated, and a high-precision navigation result is obtained.
The step corresponds to the fourth block in the first attached drawing, and a Kalman filter calculation flow chart is expanded and shown in the second attached drawing.
Step five: repeating the steps from one to four
And for any epoch time, repeating the first step to the fourth step, and further obtaining the navigation results of all satellites in the satellite cluster at the time
Step six: high-precision navigation information obtained by fusing navigation results of satellite clusters
And step one to step five are also applied to the satellite B and the satellite C, so that three groups of state quantity estimated values based on the navigation method can be obtained at any epoch moment, and the three groups of state quantity estimated values are fused to obtain the high-precision navigation information of the satellite cluster.
Through the steps, a high-precision real-time navigation result based on the GNSS broadcast ephemeris, the single-frequency pseudo-range information and the carrier phase information can be obtained. Therefore, the requirement of the micro-nano satellite cluster for realizing high-precision real-time navigation by utilizing a GNSS receiver can be met.
Claims (1)
1. A high-precision real-time micro-nano satellite cluster navigation algorithm takes a satellite cluster formed by three satellites A, B, C as an example, and is characterized in that: the method comprises the following steps:
the method comprises the following steps: computing state predictions for satellites in a cluster
The satellite A calculates to obtain a state prediction value of the position and the speed of the satellite A by using an absolute dynamic model, and calculates to obtain state prediction values of the satellite B and the satellite C in the cluster by using a relative dynamic model.
Step two: all satellites receive GNSS measurement information and send self-carrier phase information to other satellites
Taking the satellite A as an example, a single-frequency GRAPHIC combined observation model is utilized to fuse the single-frequency pseudo range and the carrier phase information of the GNSS to the satellite A, so that the GNSS observed quantity after ionospheric delay is removed is obtained, and the satellite A obtains the absolute measurement information of the satellite A. Furthermore, satellite a transmits GNSS carrier phase information obtained by itself to satellites B and C, while receiving GNSS carrier phase information transmitted by satellites B and C to satellite a.
Step three: all satellites obtain relative position vectors by using received GNSS carrier phase information
The satellite a obtains relative position vectors of the satellite B and the satellite C with respect to the satellite a based on the carrier difference by using the carrier phase information of itself and the received satellites B and C.
Step four: calculating high-precision navigation result by utilizing absolute/relative integrated navigation algorithm
According to the method, an Extended Kalman Filter (EKF) is utilized, a state equation is constructed by an absolute/relative dynamics model, a measurement equation is constructed by GNSS observed quantity, a predicted value of the state quantity is corrected, and a high-precision navigation result is obtained.
Step five: repeating the steps from one to four
And for any epoch moment, repeating the first step to the fourth step to further obtain the navigation results of all satellites in the satellite cluster at the moment
Step six: high-precision navigation information obtained by fusing navigation results of satellite clusters
And step one to step five are also applied to the satellite B and the satellite C, so that three groups of state quantity estimated values based on the navigation method can be obtained at any epoch moment, and the three groups of state quantity estimated values are fused to obtain the high-precision navigation information of the satellite cluster.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010329840.8A CN111487660B (en) | 2020-04-24 | 2020-04-24 | High-precision real-time micro-nano satellite cluster navigation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010329840.8A CN111487660B (en) | 2020-04-24 | 2020-04-24 | High-precision real-time micro-nano satellite cluster navigation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111487660A true CN111487660A (en) | 2020-08-04 |
| CN111487660B CN111487660B (en) | 2022-07-26 |
Family
ID=71812980
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010329840.8A Active CN111487660B (en) | 2020-04-24 | 2020-04-24 | High-precision real-time micro-nano satellite cluster navigation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111487660B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112068176A (en) * | 2020-09-15 | 2020-12-11 | 北京航空航天大学 | Unmanned intelligent Mars detection system and method carrying multi-micro/nano-satellite networking |
| CN114779285A (en) * | 2022-04-18 | 2022-07-22 | 浙江大学 | Precise orbit determination method based on microminiature low-power-consumption satellite-borne dual-mode four-frequency GNSS receiver |
| CN115047505A (en) * | 2022-08-17 | 2022-09-13 | 长沙金维信息技术有限公司 | GNSS positioning method and navigation method based on carrier phase differential assistance |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040027277A1 (en) * | 2000-11-17 | 2004-02-12 | Global Locate, Inc. | Method and apparatus for processing of satellite signals without time of day information |
| CN102981175A (en) * | 2012-12-24 | 2013-03-20 | 北京理工大学 | Method of master-slave object cooperation positioning for cluster space robots |
| JP2013181872A (en) * | 2012-03-02 | 2013-09-12 | Advanced Telecommunication Research Institute International | Relative positioning device, relative positioning method, and program |
| US20130271318A1 (en) * | 2012-04-12 | 2013-10-17 | Trimble Navigation Limited | Advanced global navigation satellite systems (gnss) positioning using precise satellite information |
| US20150145719A1 (en) * | 2013-10-27 | 2015-05-28 | Honeywell International Inc. | Multiple-criterion based global navigation satellite sub-set recursive selection |
| CN106569241A (en) * | 2016-09-27 | 2017-04-19 | 北京航空航天大学 | Single frequency high precision positioning method based on GNSS |
| CN107367744A (en) * | 2017-08-22 | 2017-11-21 | 温州大学 | LEO-based GPS orbit determination method based on adaptive measuring Noise Variance Estimation |
| EP3287811A1 (en) * | 2015-04-23 | 2018-02-28 | Unicore Communications. Inc | Method and device for gnss point positioning |
| CN109917431A (en) * | 2019-04-02 | 2019-06-21 | 中国科学院空间应用工程与技术中心 | A kind of method that space-based realizes GNSS satellite independent navigation |
| CN109991633A (en) * | 2019-03-05 | 2019-07-09 | 上海卫星工程研究所 | A kind of low orbit satellite orbit determination in real time method |
| CN110031881A (en) * | 2019-05-06 | 2019-07-19 | 中国人民解放军61540部队 | The method of laser ranging auxiliary Static Precise Point Positioning between high precision star |
| CN110764127A (en) * | 2019-10-08 | 2020-02-07 | 武汉大学 | Relative orbit determination method for formation satellite easy for satellite-borne on-orbit real-time processing |
-
2020
- 2020-04-24 CN CN202010329840.8A patent/CN111487660B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040027277A1 (en) * | 2000-11-17 | 2004-02-12 | Global Locate, Inc. | Method and apparatus for processing of satellite signals without time of day information |
| JP2013181872A (en) * | 2012-03-02 | 2013-09-12 | Advanced Telecommunication Research Institute International | Relative positioning device, relative positioning method, and program |
| US20130271318A1 (en) * | 2012-04-12 | 2013-10-17 | Trimble Navigation Limited | Advanced global navigation satellite systems (gnss) positioning using precise satellite information |
| CN102981175A (en) * | 2012-12-24 | 2013-03-20 | 北京理工大学 | Method of master-slave object cooperation positioning for cluster space robots |
| US20150145719A1 (en) * | 2013-10-27 | 2015-05-28 | Honeywell International Inc. | Multiple-criterion based global navigation satellite sub-set recursive selection |
| EP3287811A1 (en) * | 2015-04-23 | 2018-02-28 | Unicore Communications. Inc | Method and device for gnss point positioning |
| CN106569241A (en) * | 2016-09-27 | 2017-04-19 | 北京航空航天大学 | Single frequency high precision positioning method based on GNSS |
| CN107367744A (en) * | 2017-08-22 | 2017-11-21 | 温州大学 | LEO-based GPS orbit determination method based on adaptive measuring Noise Variance Estimation |
| CN109991633A (en) * | 2019-03-05 | 2019-07-09 | 上海卫星工程研究所 | A kind of low orbit satellite orbit determination in real time method |
| CN109917431A (en) * | 2019-04-02 | 2019-06-21 | 中国科学院空间应用工程与技术中心 | A kind of method that space-based realizes GNSS satellite independent navigation |
| CN110031881A (en) * | 2019-05-06 | 2019-07-19 | 中国人民解放军61540部队 | The method of laser ranging auxiliary Static Precise Point Positioning between high precision star |
| CN110764127A (en) * | 2019-10-08 | 2020-02-07 | 武汉大学 | Relative orbit determination method for formation satellite easy for satellite-borne on-orbit real-time processing |
Non-Patent Citations (6)
| Title |
|---|
| CHEN ZHIGUO 等: ""An algorithm for orbit determination of navigation satellites based on a ground beacon"", 《PROCEEDINGS OF 2012 INTERNATIONAL CONFERENCE ON MEASUREMENT, INFORMATION AND CONTROL》 * |
| MUHAMMAD ILYAS 等: ""Extended Kalman filter design for multiple satellites formation flying"", 《2008 4TH INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES》 * |
| 仲惟超: ""卫星集群导航与轨道控制方法研究"", 《中国博士学位论文全文数据库 工程科技II辑》 * |
| 李轶 等: ""基于分布式滤波器的编队卫星导航算法研究"", 《第三届中国卫星导航学术年会电子文集-S03精密定轨与精密定位》 * |
| 梁晓朋 等: ""基于X射线脉冲星和星间链路的导航星座自主导航仿真研究"", 《航天控制》 * |
| 陈培 等: ""基于星间测量的卫星星座自主导航算法"", 《北京航空航天大学学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112068176A (en) * | 2020-09-15 | 2020-12-11 | 北京航空航天大学 | Unmanned intelligent Mars detection system and method carrying multi-micro/nano-satellite networking |
| CN114779285A (en) * | 2022-04-18 | 2022-07-22 | 浙江大学 | Precise orbit determination method based on microminiature low-power-consumption satellite-borne dual-mode four-frequency GNSS receiver |
| CN115047505A (en) * | 2022-08-17 | 2022-09-13 | 长沙金维信息技术有限公司 | GNSS positioning method and navigation method based on carrier phase differential assistance |
| CN115047505B (en) * | 2022-08-17 | 2022-11-22 | 长沙金维信息技术有限公司 | GNSS positioning method and navigation method based on carrier phase differential assistance |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111487660B (en) | 2022-07-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111487660B (en) | High-precision real-time micro-nano satellite cluster navigation method | |
| CN111522037B (en) | Autonomous navigation method and navigation system for constellation co-orbital plane satellite | |
| CN103487820B (en) | A kind of vehicle-mounted strapdown/satellite tight integration seamless navigation method | |
| CN103675861A (en) | Satellite autonomous orbit determination method based on satellite-borne GNSS multiple antennas | |
| CN101609140A (en) | A kind of compatible navigation receiver positioning system and localization method thereof | |
| CN110764127B (en) | Relative orbit determination method for formation satellite easy for satellite-borne on-orbit real-time processing | |
| CN103868514A (en) | Autonomous navigation system for on-orbit aircraft | |
| CN104048664A (en) | Autonomous orbit determination method of navigation satellite constellation | |
| CN110554443A (en) | Method for determining earth gravity field based on carrier phase observed value and point acceleration method | |
| Gong et al. | Precise real-time navigation of the small TJU-1 satellite using GPS, GLONASS and BDS | |
| Xu et al. | Orbit determination and thrust force modeling for a maneuvered GEO satellite using two-way adaptive Kalman filtering | |
| CN116878498A (en) | Multi-frequency BDS/INS combined air refueling relative navigation system and method | |
| CN107830856B (en) | Formation-flight-oriented solar TDOA (time difference of arrival) measurement method and integrated navigation method | |
| Zini | Precise orbit determination techniques for a lunar satellite navigation system | |
| Lantto et al. | Precise orbit determination using duty cycled GPS observations | |
| Bisnath et al. | Precise a posteriori geometric tracking of Low Earth Orbiters with GPS | |
| Bagci et al. | Integrated NRM/EKF for LEO satellite GPS based orbit determination | |
| CHENG et al. | The rotation of the gravity potential on the Earth's gravity field recovery | |
| Chen et al. | High orbital spacecraft fast positioning algorithm assisted by inter-satellite links | |
| CN113687394B (en) | Centimeter-level orbit determination system and method for high-orbit satellite | |
| Kumar et al. | Real-time kinematic absolute and relative velocity estimation of geostationary satellites in formation using IRNSS observables | |
| CN115096317B (en) | Earth-moon space DRO spacecraft formation relative navigation method and system | |
| CN112379398B (en) | Earth-moon space satellite navigation positioning method | |
| Capuano et al. | GNSS/INS/Star tracker integration for Real-time on-Board autonomous orbit and Attitude determination in LEO, MEO, GEO and beyond | |
| Li et al. | Precise Orbit Determination of Combined GNSS and LEO Constellations with Regional Ground Stations |
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 |