CN112394376A - Non-differential network parallel processing method for large-scale GNSS network observation data - Google Patents
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Abstract
The invention relates to a non-differential whole network parallel processing method for large-scale GNSS network observation data, and particularly relates to a non-differential whole network parallel processing method for large-scale GNSS network observation data, which is realized on the basis of a bottom multi-core parallel computing technology. Firstly, screening a certain number of survey stations from a large-scale GNSS network as core stations, estimating satellite orbits and satellite clock errors in parallel, performing parallel non-differential floating solution calculation between stations, and estimating the fractional phase deviation of a wide lane and a narrow lane at a satellite end in parallel; secondly, parallel computing of parallel non-differential floating solutions between stations is carried out on other stations in the GNSS network, and non-differential ambiguities of all stations are fixed in parallel by utilizing fractional phase deviations of wide lanes and narrow lanes estimated by a core station, so that carrier distances of all stations in the GNSS network are generated in parallel; and finally, performing unified solution on the whole network in a non-differential mode by using the pseudo-range observed value and the carrier distance, wherein the parameters to be estimated comprise a survey station coordinate, a satellite orbit, a receiver and satellite clock error, double-difference ambiguity, troposphere delay, decimal phase deviation, earth rotation parameters and the like.
Description
Technical Field
The invention relates to a non-differential network parallel processing method for large-scale GNSS network observation data, and belongs to the technical field of large-scale GNSS network observation data processing.
Background
As an important ground infrastructure of a country, a gnss (global Navigation Satellite system) reference station network consisting of hundreds or even thousands of continuously operating reference stations is established in a plurality of countries and regions. Large-scale global GNSS network data provides rich computing resources, but also poses significant challenges in data processing. The time consumed by data processing and resolving is increased in a geometric progression along with the enlargement of the scale of the GNSS network, the global GNSS large network data processing faces the difficult problems of low calculation efficiency and even no resolving, and a corresponding high-efficiency data processing model and method are urgently needed to be used as a support so as to give full play to the advantages of the large network and improve the precision, reliability and real-time performance of large network service. The search for efficient and rapid processing of large-scale GNSS network data becomes one of the current hot spots, and is receiving more and more attention and attention.
Research institutions and researchers at home and abroad have paid extensive attention and research on the aspects of improving models and algorithms, applying parallel computing technology and the like, and published documents mainly comprise: global GPS reference frame definitions of unknown sizes and Parallel resolution of large-scale GNSS networks, Global Navigation Satellite System (GNSS) Global data processing protocols for huge GNSS networks and enhanced Global Navigation Satellite System (GNSS) data processing strategies for GNSS networks, GNSS network Global Navigation Satellite System (GNSS) Global data processing strategies, GNSS network Global Navigation Satellite System (GNSS) Global navigation satellite system (mass navigation satellite system) Global data processing studies, GNSS data Parallel processing studies, large-network double-difference model Parallel fast solving methods and PPP network solution UPD ambiguity fixed large-scale CORS station Global positioning system (GNSS) Global navigation satellite system (GPS) Global surveying and earth dynamics Research abroad, and GNSS data fast processing strategies, GNSS data fast processing and fast processing methods under multi-core environment, of national survey and mapping science report.
In the non-differential whole network computing method of the large-scale GNSS network, the existing solution adopts a serial computing method to synchronously process data of all reference stations in the large-scale GNSS network, serially estimate satellite orbits and clock errors, serially estimate fractional phase deviation of wide lanes and narrow lanes, serially fix non-differential ambiguity, serially generate carrier distance and serially resolve the whole network in sequence. In parallel solution of observation data of a large-scale GNSS network, research has been focused on the aspects of baseline vector network adjustment, double-difference baseline solution, single-station non-difference floating solution, fixed solution calculation and the like.
Disclosure of Invention
The invention aims to provide a non-differential whole network parallel processing method for observation data of a large-scale GNSS network, which is used for solving the problem that the time consumption of data processing and resolving is exponentially increased due to the fact that the current GNSS network is continuously enlarged in size.
In order to achieve the above object, the scheme of the invention comprises:
the invention relates to a non-differential network parallel processing method for observation data of a large-scale GNSS network, which comprises the following steps:
1) selecting a set number of observation stations as core stations, and resolving satellite orbits, satellite clock errors and earth rotation parameters in parallel according to observation data of the core stations;
2) estimating wide lane decimal phase deviation and narrow lane decimal phase deviation of a satellite end by using the satellite orbit, the satellite clock error and the earth rotation parameters;
3) constructing a non-differential ambiguity for each core station, and fixing the combined ambiguity without the ionized layer by using the rounded wide lane decimal phase deviation and narrow lane decimal phase deviation;
for other stations except the core station, performing inter-station parallel non-differential fixed solution calculation by using satellite orbits, satellite clock errors, earth rotation parameters, wide lane decimal phase deviation and narrow lane decimal phase deviation of a satellite end, constructing non-differential ambiguity for each other station, and fixing the non-ionosphere combination ambiguity;
4) according to the carrier phase observed value of each observation station and the fixed ionosphere-free combined mold ambiguity, generating carrier distance observed values of the corresponding observation stations in parallel;
5) and combining the pseudo-range observed values and the carrier distance observed values of all the stations, parallelly constructing a method equation, parallelly inverting the method equation, and carrying out whole-network parallel solution based on a non-differential mode.
The invention provides a novel large-scale GNSS network observation data non-differential whole network multi-core parallel processing method, which realizes the parallel execution of a plurality of links of large-scale GNSS network observation data non-differential whole network calculation, improves the utilization rate of the calculation on a multi-core platform, and improves the whole network resolving efficiency.
Further, the core station is not less than 100 measuring stations which are uniformly distributed in the whole world.
Further, in the step 1), satellite orbit and earth rotation parameters are estimated through Kalman filtering in a non-differential mode, a single core station is used as a parallel granularity to construct a non-differential observation equation, and the non-differential observation equation is constrained through constructing double-differential ambiguity and fixing the ambiguity.
Further, in the step 2), the wide lane decimal phase deviation and the narrow lane decimal phase deviation at the satellite end are subjected to inter-station parallel non-differential floating solution calculation on the core station to realize parallel estimation.
Further, in the step 4), the carrier phase observed value is corrected by using the fixed ionosphere-free combined mold ambiguity and the antenna phase winding effect, so as to obtain a carrier distance observed value.
Further, in step 5), the pseudorange observed value is deducted by satellite-side pseudorange hardware delay.
Further, in step 5), the method for performing whole network parallel computation based on the non-differential mode is as follows: taking the pseudo-range observed value and the carrier distance observed value as observed quantities; establishing a normal equation of each epoch in parallel by taking the epoch as granularity; solving a normal equation by adopting a parameter elimination algorithm based on recursive least squares; and (3) inverting the normal equation by adopting a matrix block inversion method and a Cholesky parallel decomposition method.
At present, the requirement of non-differential network unified solution of observation data of a large-scale GNSS network on real-time performance and computing efficiency is higher and higher, in order to shorten the time of non-differential network computing of the observation data of the large-scale GNSS network and improve the timeliness of the non-differential network computing of the observation data of the large-scale GNSS network, a multi-core parallel computing technology, namely a Task Parallel Library (TPL), is adopted to design the non-differential network computing method of the observation data of the large-scale GNSS network under a multi-core platform, and the parallel solution of satellite orbit, satellite clock difference and earth rotation parameters of a core station is realized; the core station estimates the fractional phase deviation of the wide lane and the fractional phase deviation of the narrow lane in parallel; and performing multi-core parallel execution of whole network resolving in a non-differential mode.
The method is simple and easy to operate, improves the utilization rate of the multi-core platform, shortens the time of calculating the non-differential whole network of the observation data of the large-scale GNSS network, and improves the efficiency of resolving the non-differential whole network of the observation data of the large-scale GNSS network.
Further, the parameters to be estimated obtained by the parallel solution of the whole network include: station coordinates, satellite orbits, double-differenced ambiguities, tropospheric delays, receiver and satellite clock offsets, fractional phase deviations, and earth rotation parameters.
Further, for the non-ionospheric combination model ambiguity which is not fixed by each survey station in the step 3), constructing the non-ionospheric combination model ambiguity of the station-satellite double differences, and fixing the non-ionospheric combination model ambiguity.
For the ambiguity that each station cannot be fixed through a non-differential mode, a double-differential mode is further tried to be adopted for fixing, the success rate of ambiguity fixing is improved, so that more observation data can be utilized in the whole network resolving process, and the resolving precision and reliability of the parameters to be estimated are improved.
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FIG. 1 is a flow chart of non-differential network parallel computation of observation data of a large-scale GNSS network under a multi-core platform.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, for observation data of a large-scale GNSS network reference station (observation station), the method of the present invention discloses a process for non-differential network parallel processing of observation data of a large-scale GNSS network under a multi-core platform, which comprises the following steps:
step 1: screening a certain number of survey stations which are uniformly distributed globally in a large-scale GNSS network as core stations, and resolving satellite orbits, satellite clock errors and earth rotation parameters in parallel based on task parallelism and data parallelism;
step 2: performing interstation parallel non-differential floating-point solution calculation on a core station by using a satellite orbit, a satellite clock error and an earth rotation parameter, and estimating a wide lane decimal phase deviation and a narrow lane decimal phase deviation at a satellite end in parallel;
and step 3: parallel calculation of non-differential floating solutions of inter-station parallel is carried out on other survey stations in the large-scale GNSS network, and non-differential ambiguity is fixed for each survey station by utilizing fractional phase deviation of a wide lane and a narrow lane estimated by a core station to obtain the fixed non-differential non-ionosphere combined model ambiguity;
and 4, step 4: deducting corresponding fixed non-differential ionosphere-free combined model ambiguity of carrier phase observed values of all stations in a large-scale GNSS network based on a two-stage parallel mechanism between the stations and between satellites, converting the non-differential ionosphere-free combined model ambiguity into ambiguity-free carrier distance observed values, and deducting satellite-side pseudo range hardware delay from pseudo range observed values of all stations;
and 5: combining the pseudo range and the carrier distance obtained in the step 4, parallel constructing a method equation, parallel inverting the method equation, and carrying out whole network parallel resolving based on a non-differential mode.
In the step 1, the number of globally uniform core stations is not less than 100, the strategy of satellite orbit, satellite clock error and earth rotation parameter estimation is Kalman filtering in a non-differential mode, a single survey station is used as parallel granularity to construct a non-differential observation equation, and the non-differential equation is constrained by constructing double-differential ambiguity and fixing.
In step 3), the non-difference fixed solution parallel computing method comprises the following steps:
aiming at the core station, non-differential floating solution calculation is not needed, the wide lane decimal phase deviation and the narrow lane decimal phase deviation obtained in the step 2) are directly utilized, the whole is obtained to obtain the wide lane ambiguity and the narrow lane ambiguity, and the fixed non-differential ionosphere-free combined ambiguity is obtained through the fixed wide lane ambiguity and the fixed narrow lane ambiguity;
and for other stations in the GNSS network, performing parallel non-differential fixed solution calculation between the stations by using the satellite orbit, the satellite clock error, the earth rotation parameter, the wide lane decimal phase deviation and the narrow lane decimal phase deviation of the satellite end.
In the step 4), the carrier distance is generated by utilizing the non-differential ionosphere-free combined model ambiguity fixed in the step 3) and the antenna phase winding effect obtained by model calculation, and the ionosphere-free combined carrier phase observed quantity is corrected to obtain the carrier distance
In the formula (I), the compound is shown in the specification,
the distance is the carrier distance, L is the observed value of the original ionosphere-free combined carrier phase, lambda is the ionosphere-free combined wavelength, N is the fixed non-differential ionosphere-free combined pattern ambiguity, and xi is the antenna phase winding effect.
And (3) further constructing the ionosphere-free combined mold ambiguity of the satellite-satellite double-difference of the stations without realizing the non-difference fixed ionosphere-free combined mold ambiguity for each station in the step 3), trying to fix the double-difference ambiguity, effectively improving the success rate of ambiguity fixation, increasing the effective carrier distance observation value which can be used for the whole network parallel solution in the step 5), and further improving the accuracy and reliability of parameter solution to be estimated.
In step 5), the whole network parallel resolving method in the non-differential mode comprises the following steps:
firstly, the obtained pseudo range and carrier distance are used as observed quantities, when a method equation is constructed, epochs are used as granularity, the method equation of each epoch is constructed in parallel based on data parallelism, and the method equation is solved by adopting a parameter elimination algorithm based on recursive least squares; when the normal equation is used for solving the inverse, matrix block inversion, a Cholesky parallel decomposition method and the like can be adopted.
Parameters to be estimated, which are solved in the whole network in a non-differential mode, comprise station coordinates, satellite orbits, double-difference ambiguity, troposphere delay, receiver and satellite clock errors, decimal phase deviation, earth rotation parameters and the like.
The observation data of the present invention includes pseudorange and carrier phase observations for the GPS, Galileo, BDS and GLONASS systems at multiple frequencies.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited thereto, and any simple modifications or equivalent substitutions of the technical solutions that can be obviously obtained by those skilled in the art within the technical scope of the present invention are within the scope of the present invention.
The invention discloses a non-differential whole network parallel processing method for large-scale GNSS network observation data, which is characterized by realizing multi-core parallel execution of a plurality of links of non-differential whole network calculation of the large-scale GNSS network observation data. The experiment utilizes the observation data of 500 GNSS reference stations which are distributed globally, and adopts three parallel schemes of dual-core, four-core and six-core respectively. Through tests, compared with the traditional single-core serial method, the large-scale GNSS network observation data non-differential whole network multi-core parallel processing method provided by the invention greatly shortens the calculation time, improves the calculation efficiency, and has the acceleration ratios of the double-core, four-core and six-core parallel reaching 1.60, 3.05 and 4.50 times respectively. The effect of practical application is closely related to the performance of a hardware system, the size of the scale of the GNSS network, the quality of observed data and the like.
Therefore, compared with the prior art, the invention has the following outstanding beneficial technical effects:
(1) the timeliness of non-differential network calculation of observation data of the large-scale GNSS network is improved.
The invention provides a non-differential whole network parallel computing method for observation data of a large-scale GNSS network, which realizes parallel resolving of satellite orbits, satellite clock errors and earth rotation parameters by a certain number of uniformly distributed core stations in the GNSS network under a multi-core platform; the core station estimates decimal phase deviation of a wide lane and a narrow lane in parallel; and the parallel computation of the whole network non-differential ambiguity parallel fixation, the whole network carrier distance parallel generation and the whole network resolving in the non-differential mode shortens the time of the large-scale GNSS network observation data non-differential whole network computation and improves the computation efficiency.
(2) Easy to expand.
The method provided by the invention has wider applicability and stronger expansibility, is suitable for processing the multi-frequency observation values of various satellite navigation systems, and is used for carrying out whole network calculation based on a non-differential mode. Observation data of systems such as GPS, Galileo, BDS, GLONASS and the like can be included in the non-differential whole network parallel computing method, the method provided by the invention can be adopted to carry out whole network parallel computing on multi-frequency observation data of a selected satellite navigation system in a non-differential mode, and the method is not limited to the 2 frequencies, but is still suitable for 3, 4 and 5 frequencies.
Claims (9)
1. A non-differential network parallel processing method for large-scale GNSS network observation data is characterized by comprising the following steps:
1) selecting a set number of observation stations as core stations, and resolving satellite orbits, satellite clock errors and earth rotation parameters in parallel according to observation data of the core stations;
2) estimating wide lane decimal phase deviation and narrow lane decimal phase deviation of a satellite end by using the satellite orbit, the satellite clock error and the earth rotation parameters;
3) constructing a non-differential ambiguity for each core station, and fixing the combined ambiguity without the ionized layer by using the rounded wide lane decimal phase deviation and narrow lane decimal phase deviation;
for other stations except the core station, performing inter-station parallel non-differential fixed solution calculation by using satellite orbits, satellite clock errors, earth rotation parameters, wide lane decimal phase deviation and narrow lane decimal phase deviation of a satellite end, constructing non-differential ambiguity for each other station, and fixing the non-ionosphere combination ambiguity;
4) according to the carrier phase observed value of each observation station and the fixed ionosphere-free combined mold ambiguity, generating carrier distance observed values of the corresponding observation stations in parallel;
5) and combining the pseudo-range observed values and the carrier distance observed values of all the stations, parallelly constructing a method equation, parallelly inverting the method equation, and carrying out whole-network parallel solution based on a non-differential mode.
2. The non-differential network parallel processing method for large-scale GNSS network observation data according to claim 1, wherein the core stations are not less than 100 stations uniformly distributed around the world.
3. The non-differential whole network parallel processing method for large-scale GNSS network observation data according to claim 1, characterized in that in step 1), satellite orbit and earth rotation parameters are estimated through Kalman filtering in a non-differential mode, a single core station is used as parallel granularity to construct a non-differential observation equation, and the non-differential observation equation is constrained through constructing double-differential ambiguity and fixing the ambiguity.
4. The large-scale GNSS network observation data non-differential whole network parallel processing method according to claim 1, wherein in step 2), the wide lane fractional phase bias and the narrow lane fractional phase bias of the satellite end are subjected to inter-station parallel non-differential floating solution calculation to realize parallel estimation.
5. The non-differential global processing method for observation data of the large-scale GNSS network according to claim 1, wherein in the step 4), the carrier phase observation value is corrected to obtain the carrier distance observation value by using the fixed ionosphere-free combined model ambiguity and the antenna phase winding effect.
6. The non-differencing network parallel processing method for large-scale GNSS network observation data according to claim 1, wherein in step 5), satellite-side pseudorange hardware delay is deducted from the pseudorange observation.
7. The non-differential whole network parallel processing method for observation data of the large-scale GNSS network according to claim 1, wherein in the step 5), the method for performing whole network parallel solution based on the non-differential mode comprises: taking the pseudo-range observed value and the carrier distance observed value as observed quantities; establishing a normal equation of each epoch in parallel by taking the epoch as granularity; solving a normal equation by adopting a parameter elimination algorithm based on recursive least squares; and (3) inverting the normal equation by adopting a matrix block inversion method and a Cholesky parallel decomposition method.
8. The non-differential global network parallel processing method for observation data of the large-scale GNSS network according to claim 1, wherein the parameters to be estimated obtained by the parallel computation of the global network comprise: station coordinates, satellite orbits, double-differenced ambiguities, tropospheric delays, receiver and satellite clock offsets, fractional phase deviations, and earth rotation parameters.
9. The non-differential whole-network parallel processing method for observation data of the large-scale GNSS network according to claim 1, wherein the ionospheric-free combined ambiguity free of the satellite double-differential is constructed for the ionospheric-free combined ambiguity free of the unfixed observation station in step 3), and the ionospheric-free combined ambiguity is fixed.
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