Low-power-consumption satellite navigation mobile acquisition station network system and construction method

A mobile collection station and satellite navigation technology, applied in the field of satellite navigation, can solve the problems of low spatial correlation of observation environment observation environment, evaluation results cannot be directly connected to users in time for users to use, and high maintenance costs, so as to achieve optimal calculation The effect of efficiency and positioning accuracy

Pending Publication Date: 2020-04-10

长沙金维信息技术有限公司

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AI-Extracted Technical Summary

Problems solved by technology

The existing satellite navigation monitoring and evaluation system still has the following limitations: (1) It is mainly used to monitor the overall operation status of the satellite navigation system, and is not suitable for flexible application scenarios of observation data for a large number of users; (2) The network coverage of the collection station is limited, The spatial correlation between its observation environment and the user’s observation environment is low. If a large number of collection stations nee...

Method used

(4) cloud solution calculation platform selects the satellite navigation space-time observation big data quality information of its place area according to the rough positioning result of mobile collection station as solution reference, removes the observation signal that has problem or error is bigger, improves this flow The quality of the observation data of the collection station, optimize the calculation efficiency and positioning accuracy, and then use the observation data to realize the reliable positioning of the mobile collection station;

User station: according to the quali...

Abstract

The invention discloses a low-power-consumption satellite navigation mobile acquisition station network system and a construction method. The system comprises a station and a cloud resolving platform.The station comprises two working modes of a user station and a mobile acquisition station, and a positioning resolving function is not needed. The cloud resolving platform is connected with a plurality of stations and has monitoring and positioning resolving functions. On the basis that a user station and a mobile acquisition station can be dynamically switched and satellite navigation space-time observation big data monitoring information is generated, based on a cloud resolving mode, the mobile acquisition station can send observation data to the cloud resolving platform, positioning resolving is achieved on the cloud resolving platform, and resolving efficiency and positioning reliability are optimized through quality monitoring information of satellite navigation space-time observation big data. And if other mobile acquisition stations with high-precision positioning capability exist in the same area, high-precision positioning of the mobile acquisition station applying for cloudcalculation can be completed through relative positioning, and services can be provided for other mobile acquisition stations applying for cloud calculation.

Application Domain

Satellite radio beaconing

Technology Topic

TelecommunicationsReal-time computing +5

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Examples

Experimental program(1)

Example Embodiment

[0054] The present invention will be further described below in conjunction with specific embodiments and accompanying drawings:

[0055] figure 1 A schematic diagram of the principle of the low-power satellite navigation mobile collection station network system in this embodiment. The system includes a site and a cloud solution platform. The site includes two working modes: a user station and a mobile collection station, and does not need to have a positioning solution. computing function; the cloud computing platform is connected with multiple sites, and the cloud computing platform has monitoring and positioning computing functions;

[0056] User station: According to the quality monitoring information of satellite navigation space-time observation big data broadcasted by the cloud solution platform, abnormal data is eliminated, the quality of observation data is improved, the solution efficiency and positioning accuracy are optimized, and the positioning reliability is enhanced; after the site is connected to the network system, The default initial mode is the user station mode, the user station can apply to switch to the mobile collection station working mode, and the application can execute the function of the mobile collection station;

[0057] Mobile collection station: It is obtained from the user station through mode switching to realize the function of satellite navigation data collection. It does not need to have the function of positioning and solving. It only needs to upload its own satellite navigation observation data to the cloud computing platform, and obtain it through the cloud computing platform. Positioning solution information;

[0058] Cloud solution platform: This solution requires a cloud solution platform with multiple subordinate sites (including user stations and mobile collection stations) to receive information such as spatiotemporal observation data uploaded by the mobile collection station; Application, use the uploaded observation data to complete the positioning calculation of the mobile collection station on the cloud computing platform, and send the positioning calculation results back to the mobile collection station; it is used to process and analyze the observation data uploaded by the mobile collection station, It includes the construction and division of data dimensions, generates monitoring information of spatiotemporal observation big data, and broadcasts the monitoring information of satellite navigation spatiotemporal observation big data to subordinate sites; it is used to review the application for switching the working mode of the mobile collection station submitted by the user station. According to the audit strategy, the mobile collection station is regularly inspected. If it is judged that the mobile collection station no longer meets the requirements of the collection station, it will be switched back to the user station mode.

[0059] This embodiment also provides a low-power satellite navigation mobile collection station network construction method, including the following steps:

[0060] Step 1: After the site is connected to the system of this solution, it is matched with the cloud solution platform, and the initial default is the user station mode;

[0061] Step 2: One cloud computing platform corresponds to multiple sites (including user stations and mobile collection stations), and the cloud computing platform broadcasts satellite navigation space-time observation big data monitoring information products to the sites;

[0062] Step 3: If the user station wants to switch to the working mode of the mobile collection station, it submits an application to the cloud computing platform. After receiving the application from the user station, the cloud computing platform passes the basic information (including but not limited to receiver type, model , manufacturer, location information) integrity and authenticity review to determine whether the user station can become a mobile collection station; after judgment, if the user station passes the review to become a mobile collection station, the cloud computing platform will issue a mode switching command , switch the user station to the mobile collection station working mode; if it does not pass the review (for example, the information is incomplete), it will continue to be the user station working mode, and the next review application can be submitted after completing the application information;

[0063] Step 4: After the user station is switched to the mobile acquisition station working mode, the mobile acquisition station function is executed, and the satellite navigation observation data information of the corresponding level is transmitted to the cloud computing platform in real time;

[0064] Step 5: Based on the positioning calculation function of the cloud calculation platform, the mobile collection station can submit a cloud calculation application to the cloud calculation platform, and only needs to upload the satellite navigation observation data required for positioning, and the cloud calculation platform completes its positioning calculation. After that, the positioning result is sent back to the mobile collection station;

[0065] Step 6: According to the data dimension division strategy, the cloud computing platform divides the observation data uploaded by the mobile collection station into dimensions according to space-time information and high-precision positioning information, and aggregates the data from other mobile collection stations for the cloud computing platform to process. Analyze, generate spatiotemporal observation big data monitoring information products and broadcast them to subordinate stations;

[0066] Step 7: The cloud computing platform will conduct regular inspections on the mobile collection station, and judge whether the mobile collection station can continue to maintain the collection station working mode through the reliability of the data. It is judged that if the mobile collection station passes the inspection, it will continue to maintain the collection station working mode. If it does not pass the inspection, it will switch to the user station working mode and only perform the user station function. After the correction, the next mobile collection station working mode can be submitted. Switch application.

[0067] Among them, the mode switching strategy of the mobile collection station, the specific implementation process is as follows:

[0068] The site connected to this solution must at least have the user station mode, with the function of receiving monitoring information broadcast by the cloud computing platform, but not with the function of collecting and uploading observation data;

[0069] If you want to become a mobile collection station, you need to have two working modes at the same time: one is the ordinary user station mode, and the other is the working mode of the mobile collection station, which not only has the function of receiving the monitoring information broadcast by the cloud computing platform, but also has the function of collecting observation data. upload function.

[0070] (1) Switch the user station mode to the mobile collection station mode

[0071]The initial default working mode of the site is the user station mode. If you want to switch to the real-time acquisition mode, you need to submit basic information (including but not limited to receiver type, model, manufacturer, and location information) to the cloud computing platform. After reviewing the integrity and authenticity of the submitted information, if it passes the review, issue a mode switching instruction to the user station, and then switch to the mobile collection station working mode;

[0072] (2) Switch the mobile collection station mode to the user station mode

[0073] The cloud computing platform determines whether the mobile collection station can maintain this working mode by regularly judging the reliability of the observation data uploaded by the mobile collection station. The observation data reliability refers to the observation data uploaded by the mobile collection station to the cloud computing platform. The measurement of objective real data, the observation data includes navigation text, pseudorange, carrier phase, and the observation data uploaded by different mobile collection stations at the same time and in the same space (see "Dimensional Division Strategy of Observation Data" for details) should satisfy certain rules. Among them, the pseudorange observation value and the carrier phase observation value should be within a certain numerical range, and the content of the navigation message should be consistent. Therefore, it is possible to judge whether the data uploaded by a certain mobile collection station is objective and true by comparing and inspecting the same spatiotemporal observation data uploaded by different mobile collection stations;

[0074] For the non-numerical observation data such as navigation texts, the reliability of the navigation texts uploaded by different mobile collection stations is compared to determine their reliability. The implementation steps are as follows:

[0075] 1) Count the navigation messages uploaded by n mobile collection stations in a certain time and space (same time and same space), denoted as {M 1 , M 2 ,...,M i ,...,M n};

[0076] 2) For the navigation message M uploaded by a mobile collection station i i , compare it with other messages in the same time and space, and perform XOR operation by bit. If the XOR value is 1, it means inconsistency. i The number of consistent navigation messages is denoted as m;

[0077] 3) Set the reliability threshold to θ (preferably θ=0.9). If m/(n-1) ≥ θ, it is considered that the navigation message M uploaded by the mobile collection station i i The reliability is not less than θ, the content of the navigation message is objective and true, otherwise the status of the navigation message uploaded by the mobile collection station is marked as abnormal.

[0078] For numerical observation data such as pseudorange observations and carrier phase observations, we can judge whether the values are credible by calculating whether the values are within a reasonable range. The implementation steps are as follows:

[0079] a) Assuming a total of n mobile collection stations in a certain space and time, first calculate the effective value of pseudo-range observations for all frequency points of all satellites received by a mobile collection station i (i=1, 2...n) (that is, all satellites have The rms value of the pseudorange observations of the frequency points) C i , and the rms value P of the carrier observation i;

[0080] b) Count the pseudorange observations of n mobile collection stations {C 1 , C 2 , ..., C i , ..., C n The mean u(C) and standard deviation σ(C) of}, and the carrier phase observations {P 1 , P 2 ,...,P i ,...,P n} mean u(P) and standard deviation σ(P):

[0081]

[0082] c) Count the set of pseudorange observations {C 1 , C 2 , ..., C i , ..., C n}, calculate the upper and lower bounds T of its reasonable values C and B C , T C =x C +1.5(x C -y C ), B C =x C +1.5(x C -y C ), where x C Indicates a pseudorange observation amount and 1/4 of the pseudorange observation amount is greater than this observation amount, y C Indicates a certain pseudorange observation amount and 1/4 of the pseudorange observation amount is smaller than this observation amount; in the same way, the carrier phase observation amount {P 1 , P 2 ,...,P i ,...,P n} T P and B P;

[0083] d) If the pseudorange observations of mobile collection station i satisfy:

[0084] {|C i -u(C)|>3σ(C)}&&{C i ∈(-∞, B C )∪(T C , +∞)}, then it is considered that the pseudo-range observations of the mobile collection station are not within a reasonable range, that is, the unreliable data, and its status is marked as abnormal;

[0085] In the same way, if the carrier phase observation of mobile collection station i satisfies:

[0086] {|P i -u(P)|>3σ(P)}&&{P i ∈(-∞, B C )∪(T C , +∞)}, then its carrier phase observation is considered unreliable, and its state is marked as abnormal.

[0087] To sum up, the cloud computing platform checks the data of the mobile collection station regularly (for example, every 60 minutes). Then the cloud computing platform increases the inspection frequency of the data uploaded by the corresponding collection station (for example, once every 10 minutes). If the abnormal data maintenance time exceeds the threshold t (preferably 60 minutes), the observation data uploaded by the mobile collection station is considered unreliable and is no longer used. The mobile collection station observes the data and informs the mobile collection station, the mobile collection station can interpret or adjust within a specified time limit (eg, 2 days), otherwise it switches to the user station mode.

[0088] Among them, the realization process of the low-power mobile collection station method is as follows:

[0089] The mobile collection station does not need to have the function of positioning and solving. It only needs to send the cloud solving application and the corresponding observation data to the cloud solving platform, and the positioning and solving of the mobile gathering station can be completed on the cloud solving platform. For long-term, large-scale applications with low power consumption, the specific process is as follows:

[0090] (1) The mobile acquisition station applying for cloud computing does not need to have the function of positioning and computing, but only needs to complete data acquisition and transmission, including RF signal reception, amplification, down-conversion, A/D conversion (analog-to-digital conversion), and then The generated observation data is transmitted to the cloud computing platform through the existing communication link;

[0091] (2) The cloud computing platform receives the cloud computing application and observation data of the mobile collection station;

[0092] (3) The cloud computing platform first obtains the rough positioning result of the mobile acquisition station according to the pseudorange information of the mobile acquisition station;

[0093] (4) According to the rough positioning results of the mobile collection station, the cloud computing platform selects the satellite navigation space-time observation big data quality information in its area as a reference for calculation, removes the observation signals with problems or large errors, and improves the performance of the mobile collection station. Observing data quality, optimizing the solution efficiency and positioning accuracy, and then using the observation data to achieve reliable positioning of the mobile collection station;

[0094] (5) If there is a mobile acquisition station with high-precision positioning capability in the area where the mobile acquisition station to be solved is located, use the high-precision positioning position and observation data of the high-precision mobile acquisition station, as well as the observation data of the mobile acquisition station to be solved. Data, complete the high-precision positioning of the mobile collection station through relative positioning, and then provide services to other mobile collection stations that apply for cloud computing, forming an ecological service that optimizes satellite navigation and positioning;

[0095] (6) The cloud solution platform sends the positioning result to the corresponding mobile collection station, and ends the cloud positioning solution process.

[0096] Among them, the dimension division strategy of observation data, the specific implementation process is as follows:

[0097] After switching to the mobile collection station working mode, the mobile collection station uploads satellite navigation observation data and other information to the cloud computing platform in real time. The spatial dimension, time dimension, and precision dimension, the basic configuration information of the observation data collected by each mobile collection station contains the information of the above three dimensions. Among them, the minimum unit of each dimension is configured by the cloud computing platform. After the monitoring information is broadcast to the user, the user can modify the configuration of the minimum unit on the user side during actual use, that is, expand the space and time of the observation data according to the user's use environment. , the smallest unit of measure for precision, the detailed scheme is as follows:

[0098] (1) Space dimension SPACE

[0099] Based on the location information, according to the latitude and longitude information, the geographic three-dimensional position space is divided into grid information of x*y*z (unit: meters), and the observation data uploaded by the mobile collection station in the same grid space is divided into the same grid space. Spatial dimension, the value of the spatial dimension of the observation data is marked as SPACE[Value]. The cloud computing platform can configure the grid parameters x, y, and z to be 10 meters, that is, the minimum unit of the three-dimensional grid space of the spatial dimension is 5*5*10 (unit: meter). The space is the smallest unit of observation data. In the actual use process, if the user wants to obtain the observation data monitoring information based on a wider coverage, he can multiply the smallest unit of the space dimension of the observation data on the user side by a multiple, that is, multiply the space The range is modified to 5n*5m*10l (unit: meter), where n, m, and l are positive integers. Select a space covering a certain area to obtain the observation data monitoring information of the corresponding space.

[0100] (2) Time dimension TIME

[0101] Based on the time information and t (unit: second) as the benchmark, the time is divided into different time periods with a length of t. For the observation data collected by the mobile collection station in the same time period, it is divided into The time dimension value of the collection station is marked as TIME[Value]. The cloud computing platform can configure t to be 1 second, that is, the minimum unit of the time dimension of the observation data is 1 second, and the cloud computing platform broadcasts the observation data with this time as the minimum unit to the user. If you want to obtain the monitoring information of the observation data in the time period of the factory change, you can multiply the minimum unit of the time dimension of the observation data by a multiple on the user side, that is, modify the time range to 1*n, where n is a positive integer, and choose to cover a certain The time period of the period is to obtain the observation data monitoring information of the corresponding period.

[0102] (3) Type dimension ACC

[0103]Based on whether the mobile collection station has high-precision positioning capability, the observation data is divided into high-precision observation data types and common observation data types, marked as ACC[hig] and ACC[gen] respectively. If the mobile collection station does not have the function of high-precision positioning, it only needs to upload the satellite navigation observation data and the calculation results to the cloud computing platform, and the observation data is marked as ordinary observation data ACC[gen]; if the real-time collection has the function of high-precision positioning, Satellite navigation observation data and calculation results, as well as high-precision position data, can be uploaded to the cloud computing platform, and the observation data is marked as high-precision observation data ACC[hig].

[0104] The mobile collection station of the present invention does not need to have the function of positioning and calculation, and only needs to complete the data collection and transmission, and send the observation data to the cloud calculation platform to realize the positioning and calculation, which can greatly reduce the calculation pressure and power of the site positioning terminal. consumption and increase the service life of the positioning terminal.

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