CN108051834A - A kind of temporal frequency comprising GNSS common-view time alignment algorithms transfers receiver - Google Patents
A kind of temporal frequency comprising GNSS common-view time alignment algorithms transfers receiver Download PDFInfo
- Publication number
- CN108051834A CN108051834A CN201711272912.4A CN201711272912A CN108051834A CN 108051834 A CN108051834 A CN 108051834A CN 201711272912 A CN201711272912 A CN 201711272912A CN 108051834 A CN108051834 A CN 108051834A
- Authority
- CN
- China
- Prior art keywords
- mrow
- msub
- msubsup
- receiver
- time
- 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.)
- Pending
Links
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
-
- 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
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/32—Multimode operation in a single same satellite system, e.g. GPS L1/L2
Abstract
The present invention provides a kind of temporal frequencies comprising GNSS common-view time alignment algorithms to transfer receiver, belongs to receiver of satellite navigation system Time transfer receiver research field, and in particular to receiver hardware design：Time frequency unit, radio frequency unit, signal processing unit, monitoring unit；Satellite Orbit Determination；Signal propagation delays amendment and the post-processing of data.Common-view time alignment algorithm includes establishing pseudorange observation equation, troposphere time delay correction value, the modeling of earth rotation effects correction value, and real-time time comparison is filtered with regard to result and post-processing.Advantages of the present invention mainly has at 2 points：First, it is easy to accomplish, altogether regarding method Time transfer receiver, it is only necessary to allow one satellite of measured receiver while observation that Time transfer receiver can be realized.Second, the Time transfer receiver of Kalman filter and RTS post-processings is added in compared to simple common-view time alignment algorithm precision higher.
Description
Technical field
The invention belongs to receiver of satellite navigation system Time transfer receiver research fields, and in particular to Satellite Orbit Determination, signal
Propagation delay amendment and the post-processing of data.
Background technology
Under the background being continuously improved in location navigation precision, high-precision time synchronization has become each GNSS satellite and determines
Key technology in the navigation system of position is the important guarantee of location navigation precision.Time transfer receiver algorithm is to realize time synchronization skill
The key of art is to determine the important method of Different Ground receiver time difference.Current existing Time transfer receiver algorithm includes regarding altogether
Method Time transfer receiver regards method Time transfer receiver, carrier phase method Time transfer receiver scheduling algorithm entirely.Allan and Weiss et al. propose apparent time altogether
Between alignment algorithm；Jung and Petit proposes alignment algorithm between full apparent time.Regarding method Time transfer receiver entirely needs multiple receivers simultaneously
Multi-satellite is observed, this equally has higher requirements to receiver and alignment algorithm；Although carrier phase method precision is higher, observation
The cost of equipment is also relatively high, and algorithm is complicated, is not easy to realize.
The content of the invention
The present invention provides a kind of temporal frequencies comprising GNSS common-view time alignment algorithms to transfer receiver, it is therefore intended that
It realizes that receiver time compares using method is regarded altogether, and post-processing is carried out to comparison result to improve comparison accuracy.The present invention's
What purpose was realized in：
Common-view time alignment algorithm includes：
(1) pseudorange observation equation is established：Different position ground receiver is calculated by receiving the navigation signal that satellite broadcasts
The Time transfer receiver algorithm of the time difference of machine needs to establish pseudorange observation equation, determines the actual distance of satellite and receiver, calculates
Time delay in signal communication process is filtered observed result and subsequent smooth.By determine satellite orbital position and
Receiver location can calculate satellite to the actual distance of receiver.And the side that different navigation system its satellite orbit determines
Method is also different, GPS, the method that Galileo and COMPASS systems are resolved using ephemeris text, and GLONASS systems use
Be orbit equation calculate method；
(2) delay is modified：Ionospheric delay and be the weight of time delay in signal communication process to flow time delay
Component, GPS, GLONASS are wanted, Galileo and COMPASS four systems can directly measure electricity using dual-frequency receiver
Absciss layer delay correction value.GNSS troposphere time delay correction value appraising model generally use Hopfield models.
(3) real-time time is compared and Kalman filter and RTS post-processings is carried out with regard to result：Kalman filter algorithm is to disappear
Except the most effective means of random error in calculating process and the most common algorithms most in use to observing data processing, using most
For maturation, the present invention adds in the subsequent smoothing algorithms of RTS after Kalman filter algorithm, can on the basis of Kalman filter,
Further improve precision of time comparison.
Compared with prior art, advantage of the invention is that：(1) present invention is easily achieved, and altogether regarding method Time transfer receiver, is only needed
Allow one satellite of measured receiver while observation that Time transfer receiver can be realized；(2) add in Kalman filter and RTS is subsequent
The Time transfer receiver of processing is compared to simple common-view time alignment algorithm precision higher.
Description of the drawings
Fig. 1 is GNSS common-view receiver schematic diagrams.
Fig. 2 is pseudo range measurement schematic diagram.
Fig. 3 is Time transfer receiver algorithm flow chart.
Specific embodiment
The present invention is described further below in conjunction with the accompanying drawings.
The present invention provides a kind of temporal frequencies comprising GNSS common-view time alignment algorithms to transfer receiver, belongs to satellite
Navigation system receiver Time transfer receiver research field, and in particular to Satellite Orbit Determination, after signal propagation delays amendment and data
Phase is handled.
The present invention includes GNSS receiver hardware design, and Fig. 1 is common-view receiver schematic diagram.
The GNSS common-view receivers mainly include time frequency unit, radio frequency unit, signal processing unit and monitoring unit
Four parts.
1) time frequency unit
Radio frequency unit and the required time frequency signal of signal processing unit are generated, while exports PPS signal.
2) radio frequency unit
Radio frequency unit separates the radiofrequency signal by Anneta module processing, downconverted and low noise amplification, AGC
After control, the intermediate-freuqncy signal for meeting certain signal-to-noise ratio (S/N) and amplitude requirement is provided for signal processing unit.
Since entry signal level is low (more much lower than white noise vocal level), in order to provide enough amplitudes to back-end processing section
Intermediate-freuqncy signal, frequency converter unit and antenna element passage net gain should be greater than 110dB, wherein antenna part gain about
40dB, discounting for the influence of transmission cable, the gain of two paths of signals should all be more than 70dB.Therefore radio frequency chip is required to configure
Into Low Medium Frequency and zero intermediate frequency output interface mode.
3) signal processing unit
The sampling of signal processing unit mainly completion navigation signal, capture, tracking, navigation message demodulation, pseudo range measurement
And report the tasks such as various observed quantities.
Receiver signal processing eleement includes signal capture module, channels track module (group containing tracking channel and flow control
Unit processed) and message processing module.Trapping module receives user configuration parameter, is combined according to different configurations, can complete institute
The acquiring pseudo code for having frequency point works, and in order to extend conveniently, which is arranged to the bit rate and carrier frequency of all frequency points
Configurable, so the module is easier to realize each frequency point interoperability function, and is easily adapted to different radio-frequency channels；With
Track channel module includes pseudo-code generator, carrier wave NCO, code NCO and accumulator；The groundwork of message processing module is to receive letter
The moonscope amount and navigation message that number processing unit provides carry out autonomous integrity detection, and the text different to multisystem
Parameter, time system, coordinate system carry out unification, then complete positioning calculation and export result etc..
4) monitoring unit
Monitoring unit is mainly transmitted to observation data and issues control instruction to each unit.
The present invention includes the foundation of pseudorange equation, the algorithm modeling of Deferred Correction value and Kalman filter and RTS is subsequent
Processing.Fig. 3 is Time transfer receiver algorithm flow chart.
First, pseudo range measurement model is established.Fig. 2 is pseudo range measurement schematic diagram, and pseudo range measurement is based on pseudorange observation
The basis of GNSS common-view time alignment algorithms, process are the multiple ground for being located at different position on the earth of a GNSS system
Receiver is simultaneously observed the markers in the GNSS satellite navigation signal of same the system, and during by local clock clock face
Make difference during the satellite clock face calculated with satellite timing signal multiplied by with signal velocity, and then ground receiver can be calculated
Machine and the pseudorange value for being observed satellite, then these pseudorange values are transferred by internet.
Following formula is pseudorange observation equation
ρj (s)=rj+δtuj-δtj (s)+Ij+Tj+ερj (1)
In formula, footmark behalf satellite, footmark u represents receiver, and i and j represent different receivers.ρ(s)It is receiver
Pseudo-range Observations, δ t(s)For satellite clock correction, I is ionosphere delay, and T is delayed for troposphere, r for receiver to satellite it is true away from
From ερFor pseudorange observation noise, can ignore in algorithm for design.
The actual distance method of determination of satellite to receiver is as follows, and satellite position can be calculated according to satellite ephemeris and obtained, and be connect
Receipts seat in the plane, which is put, to be also known.Following formula is the expression formula of r
In formula, footmark behalf satellite, footmark d represents receiver, and i and j represent different receivers.xs,ys,zsFor for satellite
Coordinate value in ECEF coordinate system.GPS, COMPASS and Galileo system can be real by resolving satellite ephemeris text
When obtain satellite orbital position, and the method that satellite orbit reckoning may be employed in GLONASS.xu,yu,zuFor the position of receiver
Coordinate.
The expression formula that pseudorange observation equation inference goes out two ground receiver time differences is
In formula, footmark L1 and L2 represent different signals, and i and j represent different receivers, δuFor two receiver times
Difference, ρ represent observation pseudorange, γ12For square of two signal frequency ratios, r is satellite to the actual distance of receiver, and T is convection current
Layer delay correction value, εijFor observation noise.
Then, the algorithm modeling of Deferred Correction value is carried out.Power layer Deferred Correction value uses dual-frequency receiver computational methods,
The method is suitable for systems, the advantages of this algorithm such as GPS, GLONASS, Galileo and COMPASS and is mathematical modulo is not required
Type, dual-frequency receiver can obtain real-time ionospheric delay value by pseudorange observation and calculating.Formula is as follows
In formula, I1And I2Ionosphere delay correction value respectively in two-frequency signal communication process；WithRespectively
The pseudorange value that receiver is observed by L1 signals and L2 signals；f1And f2The frequency of respectively different signal；γ12For f1And f2Ratio
Square.
In the troposphere in estimating GPS, Galileo, GLONASS and COMPASS measured value, delay uses unification suddenly
General Field (Hopfield) model, can be divided into two kinds of situations of dry component time delay and hygroscopic water amount time delay, and dry component refers generally to oxygen
With the dry air such as nitrogen, and hygroscopic water amount refers mainly to vapor.
The dry component T of troposphere delay zenith directionzdEstimation formula be：
In formula, P0With Tk0It is highly air gross pressure and thermodynamic temperature at zero on the ground to represent respectively.
Zenith is to troposphere delay hygroscopic water amount TzwEstimation formula
E in formula00=11.691mbar is the water vapor partial pressure at the zero elevation of ground.
Troposphere delay T on signal propagation direction, i.e.,
T=TzdFd+TzwFw (7)
Dry component slope FdAppraising model be
Hygroscopic water amount slope FwAppraising model be
In formula, elevation angle that θ is formed between satellite and ground receiver, unit is radian.
Above-mentioned algorithm may finally to different receivers time difference data, but due to the influence of observation noise, this group of number
According to precision be extremely difficult to require, so finally carrying out Kalman filter and RTS post-processings.
Kalman filter is a process that signal or data are handled and converted, main purpose be remove or
Weaken influence of the undesired ingredient to estimate, and enhance the weight of desired ingredient.And Kalman filter uses recursion
Processing, with the strange land clock bias estimation value of last sampling instant and the strange land clock correction observation at current time, to estimate current time
Strange land clock bias estimation value, observation after current time will not generate the estimate at current time any influence, thus
It is suitable for regarding data processing altogether in real time.Therefore it is to improve precision of time comparison that Kalman filter is carried out to time difference data
Common method.Kalman filter is carried out to the clock correction data (observed quantity) of Noise, accurate clock correction can be estimated.Assuming that
K moment clock correction true value xkIt represents, it constitutes state variable Xk, here
Xk=(xk)
The state equation of Kalman filter is
Xk=Φk,k-1Xk-1+Wk-1 (10)
Φk,k-1For state-transition matrix, Wk-1For plant noise.
The dynamical system dimension n of Kalman filter, observation system dimension m are 1.
Consider that receiver fails according to altogether regarding table regulation moment timely locking satellite or even altogether regarding the entire of table defined
It tracks in the period, fails locking satellite always, cause altogether depending on lacking the record in data.We use equally spaced Kalman
Wave filter, for regarding interval altogether and failing the lock star successful period, we are with clock bias estimation value in preceding 3 moment pointsSecond order polynomial extrapolation is carried out, as the observation x (k) at current time, makes Kalman filter
Continue.Second order polynomial extrapolation takes into account the frequency difference of two station atomic clocks and the influence of opposite drift, when can be to interval
Between in section two station clock correction variable quantities accurately estimated, so as to ensure that Kalman filter performance.
After pre-processing of the information is regarded altogether, Kalman filter, algorithm mistake are carried out to the strange land clock correction data sequence of Noise
Cheng Wei：
The first step, state variable Xk(containing only clock correction true value one-component) is estimated with its KalmanBetween mean square error
Matrix is known as estimation error covariance matrix, uses CkIt represents.Given C0One initial value, according to the following formula
P can be calculated1.Wherein, PkFor state variable XkWith its estimation under the conditions of no observation noise and plant noiseBetween mean squared error matrix, QkFor 1 × 1 rank plant noise covariance matrix.
Second step obtains P1Afterwards, according to Kalman gain matrixs GkExpression
Acquire G1, wherein RkFor 1 × 1 rank observation noise VkCovariance matrix.
3rd step, according to the following formula
The state variable estimate at k=1 moment can be obtained(i.e. k=1 moment clock correction Kalman estimates).
4th step, by P1Bring following formula into
Ck=(I-GkHk)Pk (14)
1 × 1 rank estimation error covariance battle array C at k=1 moment can be acquired1.Then, cycled into next time.
In near real-time regards altogether, it should to strange land clock correction X0Have preliminary estimation, with this value to wave filter into
Row initialization, can accelerate the convergence rate of wave filter.At this point, estimation error covariance matrix initial value is taken as
RTS fixed interval Optimal Smoothing Algorithms are on the basis of Kalman filter, are owned using in entire time interval
Metric data obtains the minimum variance estimate of state, can obtain fusion results more higher than Kalman filter precision.Smoothing solution
Calculation process is reverse compared with filtering.Therefore, RTS fixed-interval smoothers stress in Transfer Alignment accuracy evaluation etc.
In the application obtained in original state, the reading manner of final smooth value and the reading manner phase of common forward-direction filter estimate
Instead.Smoothing process is to obtain filtering estimate to Kalman filter before carrying out first, then by a reversed smoothing process,
And then to smooth estimate.Therefore, smooth resolve needs the real-time storage data in filtering, and the data stored are 4
A matrix is respectively estimateOne step shifts battle array, estimation mean square deviation battle arrayAnd one-step prediction mean square deviation battle array
Smoothing formula is：
K=N-1, N-2 ... 2,1,0
Wherein
In formula,For RTS smooth values；Ks,kFor filtering gain；Ps,kFor covariance matrix.
By RTS, treated that time difference data is final data.
Claims (7)
1. a kind of temporal frequency comprising GNSS common-view time alignment algorithms transfers receiver, it is characterised in that：The GNSS
Common-view receiver mainly include time frequency unit, radio frequency unit, four part of signal processing unit and monitoring unit, wherein, time-frequency list
Member generates radio frequency unit and the required time frequency signal of signal processing unit, while exports PPS signal；Radio frequency unit is by day
The radiofrequency signal of wire module processing is separated, and after downconverted and low noise amplification, AGC controls, is carried for signal processing unit
For meeting the intermediate-freuqncy signal of certain signal-to-noise ratio (S/N) and amplitude requirement；Signal processing unit is mainly to complete adopting for navigation signal
Sample, capture, tracking, navigation message demodulation, pseudo range measurement simultaneously report the tasks such as various observed quantities；Monitoring unit is mainly to observing number
According to being transmitted and issue control instruction to each unit.
2. a kind of temporal frequency comprising GNSS common-view time alignment algorithms transfers receiver, it is characterised in that：Described regards altogether
Time transfer receiver algorithm includes establishing pseudorange observation equation, the algorithm modeling of Deferred Correction value, the result for comparing real-time time
Carry out Kalman filter and RTS post-processings.
3. a kind of temporal frequency comprising GNSS common-view time alignment algorithms according to claim 1 transfers receiver,
It is characterized in that：The signal processing unit includes signal capture module, channels track module (group containing tracking channel and flow control
Unit processed) and message processing module；Trapping module receives user configuration parameter, is combined according to different configurations, completes all frequencies
The acquiring pseudo code work of point, which is arranged to the bit rate and carrier frequency of all frequency points configurable；Tracking channel
Module includes pseudo-code generator, carrier wave NCO, code NCO and accumulator；What message processing module receipt signal processing unit provided defends
Star observed quantity and navigation message carry out autonomous integrity detection, and to the different text parameter of multisystem, time system, coordinate
System carries out unification, then completes positioning calculation and exports result.
4. a kind of temporal frequency comprising GNSS common-view time alignment algorithms according to claim 2 transfers receiver,
It is characterized in that：The process for establishing pseudorange observation equation is as follows, and GNSS system is located at different position on the earth
Multiple ground receivers are simultaneously observed the markers in the GNSS satellite navigation signal of same the system, and during by local
Make difference during the satellite clock face calculated during clock clock face with satellite timing signal multiplied by with signal velocity, and then calculate ground
Receiver and the pseudorange value for being observed satellite, following formula are pseudorange observation equation
ρi (s)=ri+δtui-δti (s)+Ii+Ti+ερi
ρj (s)=rj+δtuj-δtj (s)+Ij+Tj+ερj (1)
In formula, footmark behalf satellite, footmark u represents receiver, and i and j represent different receivers.ρ(s)It is the pseudorange of receiver
Observation, δ t(s)For satellite clock correction, I is delayed for ionosphere, and T is delayed for troposphere, and r is actual distance of the receiver to satellite,
ερFor pseudorange observation noise, can ignore in algorithm for design；
The actual distance method of determination of satellite to receiver is as follows, and satellite position can be calculated according to satellite ephemeris and obtained, receiver
Position is also known, and following formula is the expression formula of r
<mrow>
<msub>
<mi>r</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<msqrt>
<mrow>
<msup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mi>s</mi>
</msubsup>
<mo>-</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>u</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>+</mo>
<msup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>y</mi>
<mi>i</mi>
<mi>s</mi>
</msubsup>
<mo>-</mo>
<msub>
<mi>y</mi>
<mrow>
<mi>u</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>+</mo>
<msup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>z</mi>
<mi>i</mi>
<mi>s</mi>
</msubsup>
<mo>-</mo>
<msub>
<mi>z</mi>
<mrow>
<mi>u</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</msqrt>
</mrow>
<mrow>
<msub>
<mi>r</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<msqrt>
<mrow>
<msup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>x</mi>
<mi>j</mi>
<mi>s</mi>
</msubsup>
<mo>-</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>u</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>+</mo>
<msup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>y</mi>
<mi>j</mi>
<mi>s</mi>
</msubsup>
<mo>-</mo>
<msub>
<mi>y</mi>
<mrow>
<mi>u</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>+</mo>
<msup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>z</mi>
<mi>j</mi>
<mi>s</mi>
</msubsup>
<mo>-</mo>
<msub>
<mi>z</mi>
<mrow>
<mi>u</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</msqrt>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula, footmark behalf satellite, footmark d represents receiver, and i and j represent different receivers, xs,ys,zsTo be satellite on ground
Coordinate value in heart body-fixed coordinate system, xu,yu,zuFor the position coordinates of receiver；
The expression formula that pseudorange observation equation inference goes out two ground receiver time differences is
<mrow>
<msub>
<mi>&delta;</mi>
<mi>u</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>&rho;</mi>
<mrow>
<mi>i</mi>
<mo>_</mo>
<mi>L</mi>
<mn>2</mn>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>&rho;</mi>
<mrow>
<mi>j</mi>
<mo>_</mo>
<mi>L</mi>
<mn>2</mn>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>&gamma;</mi>
<mn>12</mn>
</msub>
<msub>
<mi>&rho;</mi>
<mrow>
<mi>j</mi>
<mo>_</mo>
<mi>L</mi>
<mn>1</mn>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>&gamma;</mi>
<mn>12</mn>
</msub>
<msub>
<mi>&rho;</mi>
<mrow>
<mi>i</mi>
<mo>_</mo>
<mi>L</mi>
<mn>1</mn>
</mrow>
</msub>
</mrow>
<mrow>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>&gamma;</mi>
<mn>12</mn>
</msub>
</mrow>
</mfrac>
<mo>+</mo>
<msub>
<mi>r</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<msub>
<mi>r</mi>
<mi>i</mi>
</msub>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<msub>
<mi>T</mi>
<mi>i</mi>
</msub>
<mo>+</mo>
<msub>
<mi>&epsiv;</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula, footmark L1 and L2 represent different signals, and i and j represent different receivers, δuIt is poor for two receiver times, ρ generations
Apparent survey pseudorange, γ12For square of two signal frequency ratios, r is actual distance of the satellite to receiver, and T is delayed for troposphere
Correction value, εijFor observation noise.
5. a kind of temporal frequency comprising GNSS common-view time alignment algorithms according to claim 2 transfers receiver,
It is characterized in that：The algorithm modeling process of the Deferred Correction value is as follows, and power layer Deferred Correction value uses dual-frequency receiver meter
Calculation method, formula are as follows
<mrow>
<msub>
<mi>I</mi>
<mn>1</mn>
</msub>
<mo>=</mo>
<mfrac>
<msubsup>
<mi>f</mi>
<mn>2</mn>
<mn>2</mn>
</msubsup>
<mrow>
<msubsup>
<mi>f</mi>
<mn>1</mn>
<mn>2</mn>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>f</mi>
<mn>2</mn>
<mn>2</mn>
</msubsup>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<msubsup>
<mi>&rho;</mi>
<mrow>
<mi>L</mi>
<mn>2</mn>
</mrow>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>&rho;</mi>
<mrow>
<mi>L</mi>
<mn>1</mn>
</mrow>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>&gamma;</mi>
<mn>12</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<msubsup>
<mi>&rho;</mi>
<mrow>
<mi>L</mi>
<mn>2</mn>
</mrow>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>&rho;</mi>
<mrow>
<mi>L</mi>
<mn>1</mn>
</mrow>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>I</mi>
<mn>2</mn>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>&gamma;</mi>
<mn>12</mn>
</msub>
<mrow>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>&gamma;</mi>
<mn>12</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<msubsup>
<mi>&rho;</mi>
<mrow>
<mi>L</mi>
<mn>2</mn>
</mrow>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>&rho;</mi>
<mrow>
<mi>L</mi>
<mn>1</mn>
</mrow>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula, I1And I2Ionosphere delay correction value respectively in two-frequency signal communication process；WithRespectively receiver
The pseudorange value observed by L1 signals and L2 signals；f1And f2The frequency of respectively different signal；γ12For f1And f2Square of ratio；
In the troposphere in estimating GPS, Galileo, GLONASS and COMPASS measured value, delay is luxuriant and rich with fragrance using unified Hope
Er De (Hopfield) model, can be divided into two kinds of situations of dry component time delay and hygroscopic water amount time delay, and dry component refers generally to oxygen and nitrogen
The dry air such as gas, and hygroscopic water amount refers mainly to vapor,
The dry component T of troposphere delay zenith directionzdEstimation formula be：
<mrow>
<msub>
<mi>T</mi>
<mrow>
<mi>z</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>=</mo>
<mn>1.552</mn>
<mo>&times;</mo>
<msup>
<mn>10</mn>
<mrow>
<mo>-</mo>
<mn>5</mn>
</mrow>
</msup>
<mfrac>
<msub>
<mi>P</mi>
<mn>0</mn>
</msub>
<msub>
<mi>T</mi>
<mrow>
<mi>k</mi>
<mn>0</mn>
</mrow>
</msub>
</mfrac>
<msub>
<mi>H</mi>
<mi>d</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula, P0With Tk0It is highly air gross pressure and thermodynamic temperature at zero on the ground to represent respectively,
Zenith is to troposphere delay hygroscopic water amount TzwEstimation formula
<mrow>
<msub>
<mi>T</mi>
<mrow>
<mi>z</mi>
<mi>w</mi>
</mrow>
</msub>
<mo>=</mo>
<mn>0.0746</mn>
<mfrac>
<msub>
<mi>e</mi>
<mn>00</mn>
</msub>
<msubsup>
<mi>T</mi>
<mrow>
<mi>k</mi>
<mn>0</mn>
</mrow>
<mn>2</mn>
</msubsup>
</mfrac>
<msub>
<mi>H</mi>
<mi>w</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
E in formula00=11.691mbar is the water vapor partial pressure at the zero elevation of ground,
Troposphere delay T on signal propagation direction, i.e.,
T=TzdFd+TzwFw (7)
Dry component slope FdAppraising model be
<mrow>
<msub>
<mi>F</mi>
<mi>d</mi>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mi>s</mi>
<mi>i</mi>
<mi>n</mi>
<msqrt>
<mrow>
<msup>
<mi>&theta;</mi>
<mn>2</mn>
</msup>
<mo>+</mo>
<msup>
<mrow>
<mo>(</mo>
<mfrac>
<mrow>
<mn>2.5</mn>
<mi>&pi;</mi>
</mrow>
<mn>180</mn>
</mfrac>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</msqrt>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>8</mn>
<mo>)</mo>
</mrow>
</mrow>
Hygroscopic water amount slope FwAppraising model be
<mrow>
<msub>
<mi>F</mi>
<mi>w</mi>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mi>s</mi>
<mi>i</mi>
<mi>n</mi>
<msqrt>
<mrow>
<msup>
<mi>&theta;</mi>
<mn>2</mn>
</msup>
<mo>+</mo>
<msup>
<mrow>
<mo>(</mo>
<mfrac>
<mrow>
<mn>1.5</mn>
<mi>&pi;</mi>
</mrow>
<mn>180</mn>
</mfrac>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</msqrt>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>9</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula, elevation angle that θ is formed between satellite and ground receiver, unit is radian.
6. a kind of temporal frequency comprising GNSS common-view time alignment algorithms according to claim 2 transfers receiver,
It is characterized in that：The Kalman filter process is as follows, it is assumed that in k moment clock correction true value xkIt represents, it constitutes state change
Measure Xk, here
Xk=(xk)
The state equation of Kalman filter is
Xk=Φk,k-1Xk-1+Wk-1 (10)
Φk,k-1For state-transition matrix, Wk-1For plant noise, dynamical system dimension n, the observation system dimension of Kalman filter
Number m is 1；
Consider that receiver fails according to altogether regarding table regulation moment timely locking satellite or even altogether regarding the entire tracking of table defined
In period, fail locking satellite always, cause altogether depending on lacking the record in data, using equally spaced Kalman filter,
For being total to depending on interval and failing to lock the star successful period, with clock bias estimation value in preceding 3 moment points
<mrow>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>-</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>-</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
Second order polynomial extrapolation is carried out, as the observation x (k) at current time, continues Kalman filter, second order is multinomial
Formula extrapolation takes into account the frequency difference of two station atomic clocks and the influence of opposite drift, can be to two station clock correction variation in intermittent time section
It measures and is accurately estimated,
After pre-processing of the information is regarded altogether, Kalman filter is carried out to the strange land clock correction data sequence of Noise, algorithmic procedure is,
The first step, state variable Xk(containing only clock correction true value one-component) is estimated with its KalmanBetween Square Error matrix
Referred to as estimation error covariance matrix use CkIt represents, gives C0One initial value, according to the following formula
<mrow>
<msub>
<mi>P</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>&Phi;</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<mi>k</mi>
</mrow>
</msub>
<msub>
<mi>C</mi>
<mi>k</mi>
</msub>
<msubsup>
<mi>&Phi;</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<mi>k</mi>
</mrow>
<mi>T</mi>
</msubsup>
<mo>+</mo>
<msub>
<mi>Q</mi>
<mi>k</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
P can be calculated1Wherein, PkFor state variable XkWith its estimation under the conditions of no observation noise and plant noiseIt
Between mean squared error matrix, QkFor 1 × 1 rank plant noise covariance matrix；
Second step obtains P1Afterwards, according to Kalman gain matrixs GkExpression
<mrow>
<msub>
<mi>G</mi>
<mi>k</mi>
</msub>
<mo>=</mo>
<msub>
<mi>P</mi>
<mi>k</mi>
</msub>
<msubsup>
<mi>H</mi>
<mi>k</mi>
<mi>T</mi>
</msubsup>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msub>
<mi>H</mi>
<mi>k</mi>
</msub>
<msub>
<mi>P</mi>
<mi>k</mi>
</msub>
<msubsup>
<mi>H</mi>
<mi>k</mi>
<mi>T</mi>
</msubsup>
<mo>+</mo>
<msub>
<mi>R</mi>
<mi>k</mi>
</msub>
<mo>&rsqb;</mo>
</mrow>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>12</mn>
<mo>)</mo>
</mrow>
</mrow>
Acquire G1, wherein RkFor 1 × 1 rank observation noise VkCovariance matrix；
3rd step, according to the following formula
<mrow>
<msub>
<mover>
<mi>X</mi>
<mo>^</mo>
</mover>
<mi>k</mi>
</msub>
<mo>=</mo>
<msub>
<mi>&Phi;</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msub>
<mover>
<mi>X</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>G</mi>
<mi>k</mi>
</msub>
<mo>&lsqb;</mo>
<msub>
<mi>Y</mi>
<mi>k</mi>
</msub>
<mo>-</mo>
<msub>
<mi>H</mi>
<mi>k</mi>
</msub>
<msub>
<mi>&Phi;</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<msub>
<mover>
<mi>X</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>13</mn>
<mo>)</mo>
</mrow>
</mrow>
The state variable estimate at k=1 moment can be obtained(i.e. k=1 moment clock correction Kalman estimates)；
4th step, by P1Bring following formula into
Ck=(I-GkHk)Pk (14)
1 × 1 rank estimation error covariance battle array C at k=1 moment can be acquired1, then, cycled into next time；
In near real-time regards altogether, it should to strange land clock correction X0There is preliminary estimation, wave filter is initialized with this value, this
When, estimation error covariance matrix initial value is taken as
7. a kind of temporal frequency comprising GNSS common-view time alignment algorithms according to claim 2 transfers receiver,
It is characterized in that：The RTS post-processing processes are as follows, and RTS fixed interval Optimal Smoothing Algorithms are the bases in Kalman filter
On plinth, the minimum variance estimate of state is obtained using all metric data in entire time interval, smooth solution process compared with
Filtering is reverse, and therefore, RTS fixed-interval smoothers lay particular emphasis on original state in Transfer Alignment accuracy evaluation etc. and obtain
In the application taken, the reading manner of the reading manner of final smooth value and common forward-direction filter estimate is on the contrary, smoothing process
To obtain filtering estimate to Kalman filter before carrying out first, then by a reversed smoothing process, and then to smooth
Estimate, therefore, smooth resolve need the real-time storage data in filtering, and the data stored are 4 matrixes, are respectively
EstimateOne step shifts battle arrayThe mean square deviation battle array of estimationAnd one-step prediction mean square deviation battle array
Smoothing formula is：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>K</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mi>k</mi>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>f</mi>
<mo>,</mo>
<mi>k</mi>
</mrow>
</msub>
<msubsup>
<mi>&phi;</mi>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<mi>k</mi>
</mrow>
<mi>T</mi>
</msubsup>
<msubsup>
<mi>P</mi>
<mrow>
<mi>f</mi>
<mo>,</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>/</mo>
<mi>k</mi>
</mrow>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>X</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mi>k</mi>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mover>
<mi>X</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>f</mi>
<mo>,</mo>
<mi>k</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>K</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mi>k</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mover>
<mi>X</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mover>
<mi>X</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>f</mi>
<mo>,</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>/</mo>
<mi>k</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>P</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mi>k</mi>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>f</mi>
<mo>,</mo>
<mi>k</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>K</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mi>k</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>f</mi>
<mo>,</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>/</mo>
<mi>k</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<msubsup>
<mi>K</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mi>k</mi>
</mrow>
<mi>T</mi>
</msubsup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>15</mn>
<mo>)</mo>
</mrow>
</mrow>
K=N-1, N-2 ... 2,1,0
Wherein
In formula,For RTS smooth values；Ks,kFor filtering gain；Ps,kFor covariance matrix, by RTS treated time difference datas
As final data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711272912.4A CN108051834A (en) | 2017-12-06 | 2017-12-06 | A kind of temporal frequency comprising GNSS common-view time alignment algorithms transfers receiver |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711272912.4A CN108051834A (en) | 2017-12-06 | 2017-12-06 | A kind of temporal frequency comprising GNSS common-view time alignment algorithms transfers receiver |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108051834A true CN108051834A (en) | 2018-05-18 |
Family
ID=62121860
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711272912.4A Pending CN108051834A (en) | 2017-12-06 | 2017-12-06 | A kind of temporal frequency comprising GNSS common-view time alignment algorithms transfers receiver |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108051834A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108873029A (en) * | 2018-06-07 | 2018-11-23 | 南京航空航天大学 | A method of realizing the modeling of navigation neceiver clock deviation |
| CN113132046A (en) * | 2021-03-25 | 2021-07-16 | 中国电子科技集团公司第五十四研究所 | Common-view time synchronization device and method based on mode-locked optical frequency comb |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102571226A (en) * | 2011-08-25 | 2012-07-11 | 中国电子科技集团公司第十研究所 | Method for testing ground station EIRP value by bistatic common-view comparison method |
| CN205861909U (en) * | 2016-05-26 | 2017-01-04 | 北京卓越航导科技有限责任公司 | A kind of Time Transmission device regarded altogether based on the Big Dipper/GPS |
| CN106785366A (en) * | 2016-12-20 | 2017-05-31 | 中国电子科技集团公司第五十四研究所 | A kind of full range point measurement type antenna for satellite common vision receiver |
-
2017
- 2017-12-06 CN CN201711272912.4A patent/CN108051834A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102571226A (en) * | 2011-08-25 | 2012-07-11 | 中国电子科技集团公司第十研究所 | Method for testing ground station EIRP value by bistatic common-view comparison method |
| CN205861909U (en) * | 2016-05-26 | 2017-01-04 | 北京卓越航导科技有限责任公司 | A kind of Time Transmission device regarded altogether based on the Big Dipper/GPS |
| CN106785366A (en) * | 2016-12-20 | 2017-05-31 | 中国电子科技集团公司第五十四研究所 | A kind of full range point measurement type antenna for satellite common vision receiver |
Non-Patent Citations (3)
| Title |
|---|
| XIANDE WU ET AL: "Accuracy analysis of a combined Common-view time Comparison algorithm of GNSS navigation system based on Kalman filtering and RTS filtering", 《2017 CHINESE AUTOMATION CONGRESS (CAC)》 * |
| 刘利: "相对论时间比对理论与高精度时间同步技术", 《中国博士学位论文全文数据库 基础科学辑》 * |
| 武文俊: "卫星双向时间频率传递的误差研究", 《中国博士学位论文全文数据库 信息科技辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108873029A (en) * | 2018-06-07 | 2018-11-23 | 南京航空航天大学 | A method of realizing the modeling of navigation neceiver clock deviation |
| CN108873029B (en) * | 2018-06-07 | 2022-04-22 | 南京航空航天大学 | Method for realizing clock error modeling of navigation receiver |
| CN113132046A (en) * | 2021-03-25 | 2021-07-16 | 中国电子科技集团公司第五十四研究所 | Common-view time synchronization device and method based on mode-locked optical frequency comb |
| CN113132046B (en) * | 2021-03-25 | 2022-10-11 | 中国电子科技集团公司第五十四研究所 | Common-view time synchronization device and method based on mode-locked optical frequency comb |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9121940B2 (en) | Vehicle navigation using non-GPS LEO signals and on-board sensors | |
| US7916070B2 (en) | Tight coupling of GPS and navigation estimates with reducer or eliminated inertial measurement unit data | |
| CN101666868B (en) | Satellite signal vector tracking method based on SINS/GPS deep integration data fusion | |
| US8228230B1 (en) | Satellite orbit prediction method, receiving apparatus and navigation satellite system employing the method | |
| CN103728647B (en) | A kind of body roll angle measurement method based on satellite carrier signal modulation | |
| CN107607971A (en) | Temporal frequency transmission method and receiver based on GNSS common-view time alignment algorithms | |
| Zhao et al. | Differential GPS aided inertial navigation: A contemplative realtime approach | |
| WO2017215026A1 (en) | Extended kalman filter positioning method based on height constraint | |
| CN104931995A (en) | Vector tracking-based GNSS/SINS deep integrated navigation method | |
| US9618625B2 (en) | System and method for continuous carrier wave reconstruction | |
| CN102033236A (en) | Position and speed combined estimation method for satellite navigation | |
| Capuano et al. | Orbital filter aiding of a high sensitivity GPS receiver for lunar missions | |
| CN104062672A (en) | SINSGPS integrated navigation method based on strong tracking self-adaptive Kalman filtering | |
| CN102819029A (en) | Supercompact combination satellite navigation receiver | |
| CN108181633A (en) | A kind of GNSS time Frequency Transfer receiver and method of reseptance | |
| Tang et al. | Practical implementation and performance assessment of an Extended Kalman Filter-based signal tracking loop | |
| CN106199668A (en) | A kind of tandem type GNSS/SINS deep integrated navigation method | |
| CN109375248A (en) | A kind of Kalman's multimodality fusion location algorithm model and its method serially updated | |
| CN108051834A (en) | A kind of temporal frequency comprising GNSS common-view time alignment algorithms transfers receiver | |
| CN105388496A (en) | Traffic application vulnerability detection system based on GPS (Global Positioning System) and method thereof | |
| Wang et al. | A robust backtracking CKF based on Krein space theory for in-motion alignment process | |
| CN102944888B (en) | Low calculating quantity global position system (GPS) positioning method based on second-order extended Kalman | |
| Chen et al. | Theoretical analysis and application of Kalman filters for ultra-tight global position system/inertial navigation system integration | |
| Hu et al. | Cycle slip detection and repair using an array of receivers with known geometry for RTK positioning | |
| Pesyna et al. | A phase-reconstruction technique for low-power centimeter-accurate mobile positioning |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20180518 |