Differential quality control method suitable for long baseline

Abstract

The invention discloses a differential quality control method and system suitable for a long baseline, and the method comprises the steps: collecting a satellite navigation observed quantity and an inertial navigation observed quantity, the satellite navigation observed quantity comprising a carrier phase observed quantity and a pseudo-range observed quantity; performing quality control on a satellite carrier phase observed quantity and a pseudo-range observed quantity by adopting double w test, and performing information fusion on a selected satellite set by adopting a scoring method in combination with inertial navigation data to obtain a fault-free satellite set; performing ionosphere delay error and troposphere delay error correction on the observed quantity of the satellite navigation corresponding to the fault-free satellite set; and carrying out long baseline differential positioning by using the processed carrier phase observed quantity. According to the method, quality control is carried out on the satellite observed quantity through double w inspection, the quality of the satellite observed quantity is effectively improved, the situation of leak detection is prevented, information fusion is carried out on a selected satellite set by adopting a scoring method in combination with inertial navigation data, optimal fusion is achieved by distributing fusion weights, and then reliable differential quality control is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of communication satellite navigation and positioning, and in particular to a differential quality control method suitable for a long baseline. Background Art

[0002] Differential GNSS (satellite navigation) is also called GNSS relative positioning. Its basic working principle is mainly based on the spatial correlation and temporal correlation of satellite clock error, satellite ephemeris error, ionospheric delay and tropospheric delay for receivers that are not far away. One or more GNSS reference receivers are installed at known locations, and the real distance from the satellite to the receiver is used to obtain real-time measurement errors. The reference station broadcasts the errors to the user receivers. The user receivers can use the broadcasted errors to correct their own measurements, thereby improving the user's measurement and positioning accuracy.

[0003] In satellite navigation relative positioning, when the baseline length is short, the double difference mode of the observation equation can basically eliminate the influence of atmospheric delay (mainly ionospheric delay and tropospheric delay) on the positioning result, and the residual error of atmospheric delay error is small and negligible; with the increase of baseline length, the atmospheric delay correlation at the zenith of the base station and the mobile station gradually weakens, and the double difference method cannot eliminate most of the atmospheric delay error, resulting in a large residual error. At this time, it is necessary to eliminate the atmospheric delay error by means of model correction or introduction of additional parameters to be estimated. At the same time, due to the complex dynamic measurement environment of long baselines, it is necessary to study how to effectively detect and control the influence of cycle slips and gross errors in order to improve the accuracy of carrier phase measurement.

[0004] The baseline lengths involved in traditional research are mostly concentrated within 100km, and most of them are single satellite navigation systems in static mode. There are few studies on dynamic baseline solutions for multi-system combinations over longer distances. The main technical problems of existing technologies include: using a single quality control method for observations, resulting in unreliable quality control results, which in turn affects the accuracy of subsequent positioning. Summary of the invention

[0005] In order to solve the deficiencies in the prior art, the present invention provides a.

[0006] The present invention adopts the following technical solution.

[0007] The beneficial effects of the present invention are that, compared with the prior art, the present invention has at least the following beneficial effects:

[0008] 1. The present invention combines INS with GNSS long-baseline high-dynamic differential positioning technology to provide more redundant observations, which provides a basis for the observation quality control of the positioning system. At the same time, the atmospheric delay correlation at the zenith of the long-baseline reference station and the mobile station gradually weakens, and the residual ionospheric delay and tropospheric delay are still the main errors, which need to be eliminated through model correction.

[0009] 2. The present invention adopts a sequential structure process of multiple quality control technical means, designs a quality control process that gradually refines from large cycle slips, residual correction, ionosphere and rheosphere, and considers inertial navigation information when correcting the carrier and pseudorange. Compared with the single quality control method in the prior art, this patent adopts a fusion of multiple quality control methods and designs a set of processes for step-by-step detection and elimination from gross errors to precise errors, which can achieve further reliable quality control of the observed quantity and ensure the accuracy of positioning;

[0010] 3. The present invention makes full use of carrier, INS and code pseudorange observations, and obtains detection and elimination of outlier data and repaired data of carrier cycle slips through carrier phase quality control technology. Further, satellite observations are quality controlled through double w inspection to effectively improve the quality of satellite observations and prevent missed detections; information fusion is performed on the selected satellite set using a scoring method in combination with inertial navigation data, and optimal fusion is achieved by allocating fusion weights, further obtaining the best satellite set detection observations, and using the processed carrier observations for reliable long baseline differential positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 is a flow chart of the long baseline difference processing method of the present invention;

[0012] Figure 2 is a flow chart of the quality control method of the present invention;

[0013] Figure 3 It is a structural diagram of the long baseline differential processing system in the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0015] The present invention designs a differential quality control method suitable for long baselines. The overall execution flow chart is as follows: Figure 1As shown, the following steps are included:

[0016] Step 1, collecting satellite navigation observations and inertial navigation observations, where the satellite navigation observations include carrier phase observations and pseudorange observations;

[0017] Specifically, carrier phase observations and pseudorange observations are collected through satellite navigation GNSS, and inertial navigation observations are obtained through an inertial navigation system (INS).

[0018] Step 2: Use double w test to control the quality of satellite carrier phase observation and pseudorange observation, and use scoring method to fuse the selected satellite set in combination with inertial navigation observation to obtain a set of fault-free satellites;

[0019] like Figure 2 As shown, it is a flow chart of the quality control method in step 2, and step 2 includes the following steps:

[0020] Step 2-1, use triple difference detection method to remove large cycle slips from carrier phase observations.

[0021] Step 2-2, obtaining baseline vector prediction information according to the carrier phase observation, combining the baseline vector prediction information and performing fault detection on the satellite observation data based on the double w test;

[0022] The fault detection of satellite navigation observations based on double w test specifically includes:

[0023] Step 2-2-1, divide the satellites into several subsets, obtain the baseline vector prediction information according to the carrier phase observation, perform fault detection based on the 1-o and 3-σ double w test under the least squares method, and obtain the set of satellites that pass the detection;

[0024] Among them, the technicians in this field divide all satellites into several satellite subsets, and perform RTK solution on the carrier phase observation of each satellite to obtain baseline vector prediction information, perform double w test on the baseline vector prediction information, and then determine whether the corresponding satellite passes the test and whether each satellite subset passes the test according to the test results;

[0025] Specifically, the test thresholds are set to 3sigma and 1sigma respectively, and the fault detection of satellite observation data is performed based on 1-σ and 3-σ double w tests respectively. Specifically, the following are performed:

[0026] (1) A 3-σ w test is applied to the baseline vector prediction information to prevent the satellite observation noise variance σ from being too small under some severe observation conditions, which will reduce the corresponding local threshold and thus result in overly strict screening. This will result in satellites that can meet the positioning solution being eliminated, thus reducing the availability of the algorithm.

[0027] (2) If the 3-σ w test fails, it means that the satellite corresponding to the satellite navigation observation is faulty;

[0028] If the 3-σ w test passes, the standard 1-σ w test is performed to prevent σ from being too large, causing all test statistics to pass and resulting in missed tests.

[0029] (3) If the 1-σ w test fails, it means that the satellite corresponding to the satellite observation data is faulty;

[0030] If the 1-σ w test passes, it means that the satellite corresponding to the satellite observation data has passed the test.

[0031] After the above tests, a set of satellites that have passed the test is obtained.

[0032] Step 2-2-2, determine the fault mode based on the conditions of the satellite set that passed the test and the entire satellite set obtained after the w test, and finally select the corresponding satellite set according to the fault mode.

[0033] The failure mode is determined based on the inspection results of the subset and the whole set after the w inspection, including:

[0034] ① The full set solution w passes the test, and all subset solutions w pass the test;

[0035] ②The full set solution w passed the test, and some subset solutions w passed the test;

[0036] ③ The full set solution w fails the test, but only one subset solution w passes the test. The corresponding satellite deleted from the subset solution is the satellite with the largest predicted pseudorange difference.

[0037] ④ The full set solution w test fails, some subset solutions w test passes or all subset solutions fail;

[0038] Select the corresponding satellite set according to the failure mode, including:

[0039] According to the above four test conditions, the fault condition at that moment is judged, that is, when condition 1 occurs, it is judged that there is no faulty satellite. Because there is no faulty satellite, both the subset and the full set solutions can pass the test;

[0040] When situation 2 occurs, it is judged that there are multiple faults at this moment; because at this time the correlation between the faulty satellites is strong, the full solution passes the test, and after the subset solution removes one satellite, the correlation between the remaining faulty satellite and other satellites in the subset solution is weak, so that part of the subset solution cannot pass the test.

[0041] When situation 3 occurs, it is judged that there is a single fault at this moment. Because of the single fault, the full set solution cannot pass the w test, so only the subset of the subset solution that deletes the faulty satellite passes the test.

[0042] When situation 4 occurs, it is judged that there are multiple faults at this moment, because both the full solution and the subset solution have faulty satellites, causing part of the subset solution or none of it to pass the inspection.

[0043] Step 2-3, select the best satellite set by scoring method;

[0044] The scoring method is used to select the best satellite set and the corresponding satellite information is integrated with the inertial navigation system output information, including:

[0045] The calculated position of the satellite set after range detection in the corresponding fault mode is subtracted from the predicted position of the inertial navigation to obtain the score value of each set, and the best satellite set is selected based on the score value of each set.

[0046] Step 3: Select the ionosphere-free combination model of the three-frequency carrier phase observations and the Saastamoinen improved model to correct the ionospheric delay error and tropospheric delay error of the satellite navigation observations of the best satellite set, respectively.

[0047] Step 3 specifically includes:

[0048] Step 3-1, construct an ionosphere-free combination model of three-frequency carrier phase observations and correct the ionospheric delay errors of pseudorange observations and carrier phase observations;

[0049] The ionosphere-free combination model of three-frequency carrier phase observations is:

[0050] Phase propagation refractive index n for a single frequency p and the refractive index n of the ranging code group wave propagation g It can be expressed as:

[0051]

[0052]

[0053] Formulas (1) and (2) can be simplified as:

[0054] n p =1-a1f -2 +a2f -3 -a3f -4 (3)

[0055] n g =1+a1f -2 -2a2f -3 +3a3f -4 (4)

[0056] Ignoring the path bending effect in the electromagnetic wave propagation path, when the signal passes through the ionosphere, the propagation path distance error caused by the refractive index change is:

[0057] δρ=∫ l (1-n)dl (5)

[0058] Where 1 is the optical path of electromagnetic wave propagation.

[0059] Substituting equations (3) and (4) into (5), we can obtain

[0060] δρ p =A1f -2 -A2f -3 +A3f -4 (6)

[0061] δρ g =-A1f -2 +2A2f -3 -3A3f -4 (7)

[0062] Where A1 = ∫ s a1ds,A2=∫ s a2ds,A3=∫ s a3ds.

[0063] Equations (6) and (7) are the ionospheric delay of carrier phase propagation and the ionospheric delay of ranging code propagation, respectively. It can be seen from the above equations that after the satellite signal passes through the ionosphere, the ranging code group wave will be delayed, while the carrier phase will be advanced.

[0064] The triple-frequency pseudorange observations correct the second-order term of the ionospheric refraction error, which is obtained by equation (7):

[0065]

[0066] Let ρ1, ρ2, ρ3 be the pseudoranges of the ranging code synchronous observations of the navigation frequencies f1, f2, f3, respectively, and S be the geometric distance between the satellite stations, then:

[0067]

[0068] Solving the above equation, we get:

[0069]

[0070]

[0071] Among them, ρ 12 =ρ1-ρ2,ρ 23 =ρ2-ρ3.

[0072] In the Beidou satellite navigation signal, there are currently three frequency points: B1 is 1561.098MHz, B2 is 1207.41MHz, and B3 is 1268.52MHz. Substituting equations (10) and (11) into equation (8), we get:

[0073]

[0074] Formula (12) is the ionospheric refraction correction from the triple-frequency pseudorange observation to the second-order term.

[0075] The ionospheric refraction correction of the triple-frequency carrier phase measurement observations to the second-order terms can be derived according to the above method, but the integer ambiguity N must be introduced.

[0076] From formula (9), we can get:

[0077]

[0078] Let P1, P2, P3 be the pseudoranges of carrier phase synchronization observations of navigation frequencies f1, f2, f3, respectively, and S be the geometric distance between satellite stations, then:

[0079]

[0080] Solving the above equation, we get:

[0081]

[0082]

[0083] so,

[0084]

[0085] Among them, P i =λ1(N i +φ i ), i = 1, 2, 3. Equation (17) is the ionospheric refraction correction from the triple-frequency carrier phase observation to the second-order term.

[0086] The ionospheric errors of the pseudorange observations and carrier phase observations are corrected by the ionospheric refraction correction from the triple-frequency pseudorange observations to the second-order terms and the ionospheric refraction correction from the triple-frequency carrier phase observations to the second-order terms, respectively.

[0087] Step 3-2, using the improved Saastamoinen model to correct the tropospheric delay errors of the pseudorange observations and carrier phase observations;

[0088] The Saastamoinen improved model is given:

[0089]

[0090] e w =RH×exp(-37.2465+0.213166T-0.000256908T 2 ) (19)

[0091] Where z is the zenith distance of the satellite and T is the station temperature in K. w They are atmospheric pressure and water vapor pressure, both in mbar.

[0092] All parameters in the formula can be obtained from standard atmospheric parameters. The relationship between air pressure, temperature, relative humidity and altitude is obtained from the following empirical formula:

[0093] P=P0[1-0.000226(H-H0)] 5.225

[0094] T=T0-0.0065(H-H0)

[0095] RH=RH0×exp[-0.0006396(H-H0)]

[0096] P0, T0, RH0 are the default values ​​of standard pressure, temperature and relative humidity:

[0097] H0=0m, P0=1013.28mbar, T0=18℃, RH0=50%. B and δR are obtained by adaptive search table interpolation.

[0098] The pseudorange and carrier phase observations are corrected according to the tropospheric correction number, and the observation accuracy is further improved.

[0099] Step 4: Use the carrier phase observations processed in step 3 to perform RTK solution to obtain the long baseline differential positioning result.

[0100] like Figure 3 As shown, the present invention also proposes a differential quality control system suitable for long baseline, which is used to implement the above differential quality control method. The system includes: an acquisition module, a quality control module, an error correction module and a differential positioning module;

[0101] The acquisition module is used to collect satellite navigation observations and inertial navigation observations, including carrier phase observations and pseudorange observations;

[0102] The quality control module uses double w test to control the quality of satellite carrier phase observation and pseudo-range observation. It combines the inertial navigation data with the selected satellite set to perform information fusion using the scoring method to obtain the best satellite set.

[0103] The error correction module is used to correct the ionospheric delay error and tropospheric delay error of the satellite navigation observation after quality control;

[0104] The differential positioning module uses the processed carrier phase observations to perform long baseline differential positioning.

[0105] The beneficial effect of the present invention is that, compared with the prior art, the present invention controls the quality of satellite observations through double w-tests, effectively improves the quality of satellite observations, and prevents missed detections; combines inertial navigation data with a selected satellite set using a scoring method to perform information fusion, and achieves optimal fusion by allocating fusion weights, thereby obtaining a reliable differential positioning result.

[0106] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0107] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive lists) of computer readable storage medium include: portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), static random access memories (SRAM), portable compact disk read-only memories (CD-ROM), digital versatile disks (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or convex structures in grooves on which instructions are stored, and any suitable combination thereof. Computer readable storage medium used here is not interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagated by waveguides or other transmission media (e.g., light pulses by optical fiber cables), or electrical signals transmitted by wires.

[0108] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0109] The computer program instructions for performing the disclosed operation may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, executed as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of a computer-readable program instruction, and the electronic circuit may execute a computer-readable program instruction, thereby realizing various aspects of the present disclosure.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A differential quality control method suitable for long baselines, characterized in that: The steps include: Step 1, collecting satellite navigation observations and inertial navigation observations, where the satellite navigation observations include carrier phase observations; Step 2: Use double w test to control the quality of satellite navigation observations, combine inertial navigation data to use scoring method to fuse information of selected satellite set, and obtain fault-free satellite set; Step 3, correcting the ionospheric delay error and the tropospheric delay error of the satellite navigation observations corresponding to the set of fault-free satellites; Step 4: Use the processed carrier phase observations to perform long baseline differential positioning.

2. The differential quality control method applicable to long baseline according to claim 1, characterized in that: The step 2 also includes: Step 2-1, using triple difference detection method to remove large cycle slips from carrier phase observations; Step 2-2, obtaining baseline vector prediction information according to the carrier phase observation, combining the baseline vector prediction information and performing fault detection on the satellite observation data based on the double w test; Step 2-3, use the scoring method to select the best satellite set and fuse the corresponding satellite information with the inertial navigation system output information.

3. The differential quality control method applicable to long baseline according to claim 2, characterized in that: The step 2-2 specifically includes: Step 2-2-1, divide the satellites into several subsets, obtain baseline vector prediction information based on carrier phase observations, and perform fault detection based on 1-σ and 3-σ double w tests under the least squares method; Step 2-2-2, determine the fault mode based on the inspection results of the subset and the full set after the double w inspection, and select the corresponding satellite set according to the fault mode.

4. The differential quality control method applicable to long baseline according to claim 3, characterized in that: In step 2-2-1, fault detection of satellite observation data based on 1-σ and 3-σ double w test specifically includes: The test thresholds are set to 3sigma and 1sigma respectively, and a 3-σ w test is performed on the baseline vector prediction information. If the 3-σ w test fails, it means that the satellite corresponding to the satellite observation data is faulty; If the 3-o w test passes, the 1-σ w test is performed; If the 1-σ w test fails, it means that the satellite corresponding to the satellite observation data is faulty; If the 1-σ w test passes, it means that the satellite corresponding to the satellite observation data has no faults. Combining the 1-σ and 3-σ w test results, we can get the set of fault-free satellites.

5. The differential quality control method applicable to long baseline according to claim 3, characterized in that: In step 2-2-2, the inspection of the subset and the full set based on the w inspection includes: Case 1: The solution w of the entire satellite set passes the test, and the solution w of all satellite subsets passes the test; Case 2: The solution w of the entire satellite set passes the test, and the solution w of some satellite subsets passes the test; Case 3: The solution w of the entire satellite set fails the test, but only one solution w of a satellite subset passes the test. The corresponding satellite deleted from the solution of this satellite subset is the satellite corresponding to the largest predicted pseudorange difference. Case 4: The solution w of the entire satellite set fails the test, but the solution w of some satellite subsets passes the test, or all satellite subset solutions fail the test; According to the inspection results, the failure modes include: When situation 1 occurs, it is determined that there is no faulty satellite. Since there is no faulty satellite, both the subset and full set solutions can pass the test; When situation 2 occurs, it is judged that there are multiple faults at this moment; because the correlation between the faulty satellites is strong at this time, the full solution passes the test, and after the subset solution removes one satellite, the correlation between the remaining faulty satellite and other satellites in the subset solution is weak, so that the subset solution cannot pass the test; When situation 3 occurs, it is determined that there is a single fault at this moment. Since the full set solution contains a single fault, it cannot pass the w test, so only the subset of the subset solution that deletes the faulty satellite passes the test; When situation 4 occurs, it is judged that there are multiple faults at this moment. Since both the full solution and the subset solution have faulty satellites, part of the subset solution or none of them pass the inspection.

6. The differential quality control method applicable to long baseline according to claim 3, characterized in that: The steps 2-3 specifically include: The calculated position of the satellite set after range detection in each fault mode is subtracted from the predicted position of the inertial navigation to obtain the score value of each set, and the best satellite set is selected based on the score value of each set.

7. The differential quality control method applicable to long baseline according to claim 1, characterized in that: The step 3 also includes: Step 3-1, construct an ionosphere-free combination model of three-frequency carrier phase observations and correct the ionospheric delay errors of pseudorange observations and carrier phase observations; In step 3-2, the Saastamoinen improved model is used to correct the tropospheric delay errors of the pseudorange observations and carrier phase observations.

8. A differential quality control system applicable to a long baseline using the differential quality control method applicable to a long baseline as claimed in any one of claims 1 to 7, characterized in that: include: Acquisition module, quality control module, error correction module and differential positioning module; The acquisition module is used to collect satellite navigation observations and inertial navigation observations, including carrier phase observations and pseudorange observations; The quality control module uses double w test to control the quality of satellite carrier phase observation and pseudo-range observation. It combines the inertial navigation data with the selected satellite set to perform information fusion using the scoring method to obtain the best satellite set. The error correction module is used to correct the ionospheric delay error and tropospheric delay error of the satellite navigation observation after quality control; The differential positioning module uses the processed carrier phase observations to perform long baseline differential positioning.

9. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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