Verification method and verification system for reference positioning system, receiver and storage medium

Abstract

The invention provides a verification method for a reference positioning system, the reference positioning system receives a satellite signal of a global navigation satellite system, and the method comprises the following steps: determining an initial floating point positioning solution meeting a threshold value requirement through real-time differential positioning based on carrier information of the satellite signal and a pseudo range of a navigation system; generating a plurality of initial candidate positioning solutions in a search area determined by the floating point positioning solution by a particle swarm genetic optimization algorithm; inputting the plurality of initial candidate positioning solutions into an improved fuzzy function model to obtain a first motion vector MV1 of two adjacent epochs; determining a second motion vector MV2 based on the two adjacent epochs according to data obtained by the set non-satellite navigation device; comparing the first motion vector MV1 with the second motion vector MV2; when the comparison result is within a receiving threshold value, the verification of the reference positioning system is passed; otherwise, the verification is not passed. The invention further provides a corresponding verification system and the like.

Description

Technical Field

[0001] The present application relates to a real-time dynamic measurement technology based on satellite signals, and more specifically, to a verification technology for a reference positioning system. Background Art

[0002] For navigation products that use satellites for navigation, a reference positioning system is usually required to verify its positioning accuracy. If the inspection is abnormal, it means that the positioning of the product is not accurate enough. However, for the reference system used to verify navigation products, it is expected to combine the map with the driving video of the vehicle to determine whether it is normal. However, due to compliance reasons or the cost of collecting high-precision maps, maps may not be available. Therefore, it is necessary to propose a method to verify the accuracy of the reference positioning system without using a map. Summary of the invention

[0003] One aspect of the present application is to provide a verification method for a reference positioning system, which receives satellite signals of a global navigation satellite system. The method includes: determining an initial floating-point positioning solution that meets a threshold requirement by real-time differential positioning based on carrier information of the satellite signal and a pseudorange of a navigation system; generating a plurality of initial candidate positioning solutions within a search area determined by the floating-point positioning solution by a particle swarm genetic optimization algorithm; inputting the plurality of initial candidate positioning solutions into an improved fuzzy function model to obtain a first motion vector MV_1 of two adjacent epochs; determining a second motion vector MV_2 based on the two adjacent epochs by using data obtained by a navigation device that is not satellite navigation; comparing the first motion vector MV_1 and the second motion vector MV_2; if the comparison result is within a receiving threshold, the verification of the reference positioning system is passed; otherwise, the verification fails.

[0004] According to the verification method described in the present application, optionally, the method also includes outputting a signal indicating that the verification has failed if the verification has failed.

[0005] According to the verification method described in the present application, optionally, the multiple initial candidate positioning solutions are input into an improved fuzzy function model to obtain a first motion vector MV_1 of two adjacent epochs, including: inputting the multiple initial candidate positioning solutions generated by the particle swarm genetic optimization algorithm into the improved fuzzy function model; the improved fuzzy function model generates two adjacent epoch optimal positioning solutions based on the initial candidate positioning solutions, which are the first epoch position P1 and the second epoch position P2; and the first motion vector MV_1 is generated from the first epoch position P1 and the second epoch position P2.

[0006] According to the verification method described in the present application, optionally, the first epoch position P1 is a positioning solution with a minimum cost function among multiple solutions calculated by the improved fuzzy function model in the process of calculating the first epoch position, and the second epoch position P2 is a positioning solution with a minimum cost function among multiple solutions calculated by the improved fuzzy function model in the process of calculating the second epoch position.

[0007] According to the verification method described in the present application, optionally, before the particle swarm genetic optimization algorithm generates a plurality of initial candidate positioning solutions within the search area determined by the floating point positioning solution, the parameters of the particle swarm genetic optimization algorithm are initialized.

[0008] According to the verification method described in the present application, optionally, the reference positioning system is used for a navigation system of a vehicle, and the non-satellite navigation device is an inertial navigation device arranged on the vehicle.

[0009] According to another aspect of the present application, a verification system for a reference positioning system is also provided, which includes a memory and a processor, the memory storing program instructions, and the processor being configured to execute the stored instructions and implement any one of the verification methods described above during the execution process.

[0010] According to another aspect of the present application, a receiver is also provided, which may include the verification system described herein, or the receiver is configured to implement any one of the verification methods described above.

[0011] A computer storage medium is also provided, which is used to store program instructions, and the program instructions implement any one of the verification methods described above when executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a flow chart of a verification method for a reference positioning system according to an example of the present application;

[0013] Figure 2 The process from RTD positioning result to the first motion vector MV_1 is briefly illustrated;

[0014] Figure 3 is a structural diagram of a verification system for a reference positioning system according to an example of the present application. DETAILED DESCRIPTION

[0015] In order to help those skilled in the art to accurately understand the subject matter for which protection is sought in the present application, the specific implementation of the present application will be described in detail below in conjunction with the accompanying drawings.

[0016] The terms "first", "second", etc. in the specification, claims and drawings of this application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the invention described herein can be implemented in an order other than those illustrated or described herein. In addition, "first" and "second" are used for descriptive purposes only and are not to be understood as expressly or implicitly indicating relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise stated, "multiple" means two or more.

[0017] As used hereinafter, the words “example” or “exemplarily” or “exemplarily” mean “serving as an example, embodiment, or illustration.” Any embodiment described as “example” or “exemplarily” or “exemplarily” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0018] The Global Navigation Satellite System (GNSS) is a positioning system consisting of a group of globally distributed satellites and ground receivers. Using the wireless signals transmitted by satellites, ground receivers can receive signals from multiple satellites. The receiver can calculate the arrival time of different satellite signals and the distance between each satellite and the receiver based on the carrier information of the received signal and the pseudo-range of the system. For navigation products, the positioning accuracy of the product is usually verified by a reference positioning system. For example, for a vehicle's navigation system, its positioning accuracy is verified by a reference positioning system.

[0019] For example, the navigation data collected during the driving of the vehicle is input into the reference positioning system, and the reference positioning system determines the positioning accuracy of the navigation system. The solution of the present application is used to verify whether the operating results of the reference positioning system itself are accurate, so as to avoid using an inaccurate reference positioning system to verify the accuracy of the navigation product. In some other cases, the reference positioning system may be set on the vehicle, so the positioning accuracy of the vehicle navigation system can be directly verified during the driving of the vehicle, without the need to collect navigation data first and then verify the navigation data by the reference positioning system.

[0020] Genetic algorithm for Particle Swarm Optimization (GPSO) is a known global optimization algorithm. In the examples of this application, the modified ambiguity function approach (MAFA) usually used by GNSS real-time differential positioning (RTD) is used to obtain the optimal positioning solution. This application combines GPSO with MAFA to provide a GPSO-MAFA algorithm. However, although the MAFA algorithm is used in each example of this application, it does not exclude the use of the ambiguity function method (AFM) to replace the MAFA algorithm.

[0021] Figure 1 It is a flow chart of a verification method for a reference positioning system according to an example of the present application. The reference positioning system mentioned here is used to verify the positioning accuracy of a navigation product based on satellite navigation, such as a receiver, etc., and the verification method according to the present application is used to verify the accuracy of the reference positioning system. The method according to the present application can be performed by the reference positioning system.

[0022] Reference Figure 1 In step S100, based on the carrier information of the satellite signal and the pseudorange of the navigation system, an initial floating point positioning solution that meets the threshold requirement is determined by real-time differential positioning RTD. The threshold requirement here is a preset positioning area range.

[0023] In step S102, a plurality of initial candidate positioning solutions are generated by the particle swarm genetic optimization algorithm GPSO in the search area determined by the floating point positioning solution. It should be understood that before using GPSO, the parameters of GPSO can be initialized so that GPSO searches in the search area determined by the floating point positioning solution of RTD.

[0024] In step S104, multiple initial candidate positioning solutions are input into the improved fuzzy function model to obtain the first motion vector MV_1 of two adjacent epochs. According to the example of the present application, MAFA successively calculates the epoch positions of two adjacent epochs, which are respectively represented as the first epoch position P1 and the second epoch position P2, and obtains the first motion vector MV_1 from the two epoch positions.

[0025] In step S106, the second movement vector MV_2 based on the two adjacent epochs is determined with the data obtained by the non-satellite navigation navigation device, wherein the two adjacent epochs for obtaining the second movement vector MV_2 are the same as the two adjacent epochs in step S102. Taking the navigation system of the vehicle as an example, the non-satellite navigation navigation device is an inertial navigation device arranged on the vehicle, for example, it may include the inertial sensor IMU of the vehicle. The data obtained by the inertial navigation device of the vehicle includes the sensing data of the inertial sensor, and the wheel speed data of the vehicle may also be obtained by the wheel speed sensor of the vehicle or the ESP system of the vehicle, etc. Based on the sensing data of the inertial sensor, the wheel speed data, etc., for example, the dead reckoning algorithm (DR) is used to obtain the driving trajectory of the two adjacent epochs.

[0026] In step S108, the first moving vector MV_1 and the second moving vector MV_2 are compared to determine whether the comparison result is within the receiving threshold. For example, the absolute value of the difference between the first moving vector MV_1 and the second moving vector MV_2 is compared with the receiving threshold to determine whether it falls within the receiving threshold. The receiving threshold is a preset value range within which the comparison result is acceptable.

[0027] If the comparison result is within the receiving threshold, the process proceeds to step S108, indicating that the first moving vector MV_1 and the second moving vector MV_2 are substantially the same, indicating that the parameters and operation of the reference positioning system are normal and the verification is passed. If the comparison result is that the absolute value falls outside the receiving threshold, the process proceeds to step S109, indicating that there is an abnormality in the reference positioning system and the verification is not passed. According to an example of the present application, a signal indicating that the reference positioning system has not passed the verification may be further output in step S109. According to some other examples of the present application, step S108 may also output a signal indicating that the reference positioning system has passed the verification.

[0028] Figure 2 The process from RTD positioning result to the first motion vector MV_1 is briefly illustrated. Figure 1 and Figure 2 The GPSO searches within the area 20 determined by the initial floating point positioning solution determined by the RTD, and generates a plurality of initial candidate positioning solutions based on the search area 20, which are indicated by solid dots in the figure.

[0029] Multiple initial candidate positioning solutions are used as inputs of the MAFA model 22, and the MAFA model 22 calculates the input multiple candidate positioning solutions respectively to obtain multiple MAFA solutions. The MAFA model can select the positioning solution with the smallest cost function among these solutions as the first epoch position P1. Subsequently, GPSO again obtains multiple initial floating-point positioning solutions from the RTD positioning results, and these solutions are used as inputs of the MAFA model 22. MAFA calculates multiple MAFA solutions, and the one with the smallest cost function among the multiple MAFA solutions is used as the second epoch position P2. The first motion vector MV_1 between the first epoch position P1 and the second epoch position P2 is determined by the first epoch position P1 and the second epoch position P2.

[0030] In summary, based on the candidate positioning solutions given by the particle swarm optimization algorithm GPSO, the MAFA convergence solution and cost function corresponding to each candidate solution are calculated through the MAFA model, and the process is iterated until the iterative convergence condition is met to give the optimal positioning solution P1 for the current epoch. The same method is used to calculate the optimal positioning solution P2 for the next epoch, and the motion vector MV_1 of two adjacent epochs is calculated based on the positioning solutions P1 and P2.

[0031] It should be noted that in some possible examples, the maximum value in the MAFA solution may be selected as the epoch position.

[0032] In the case where GPSO is not used, that is, under the existing conventional practice, the MAFA model directly performs MAFA calculations on the points within the area determined by the initial floating point of the RTD one by one. Since there are too many points calculated by MAFA, the speed is relatively slow. According to the method of the example of the present application, GPSO first initializes the search area based on the initial floating point positioning solution of the RTD, and multiple candidate positioning solutions can be determined relatively quickly, and most of the points in the search area are screened out, so that the input entering the MAFA model becomes much less, which increases the speed of MAFA calculation and reduces the amount of MAFA calculation. In addition, since the MAFA algorithm itself is not sensitive to cycle slips, the calculation process performed by MAFA combined with the GPSO algorithm is also insensitive to cycle slips.

[0033] The following is a brief description of the MAFA model and its calculation process.

[0034] The double differential observation model is used to reduce or eliminate the error of the reference positioning system, as shown in equation (1):

[0035]

[0036] Where Φ is the measurement result of the carrier phase double differentiator in units of integer cycles; λ is the wavelength of the carrier phase in meters; e is the residual value of the integer cycle, which is usually the noise of the measurement result; ρ(Xc ) is the double-difference geometric range with the true positioning system position; a is the double-difference integer ambiguity of the whole cycle.

[0037] When ρ is used to simplify the expression of ρ(X c ), equation (1) can be simplified to equation (2):

[0038]

[0039] Considering that the residual value e cannot exceed half a period and the ambiguity parameter a has an integer property, equation (2) can be rewritten as the following equation (3):

[0040]

[0041] Among them, round(.) is the rounding function.

[0042] Equation (3) can be updated using different and continuous functions, as shown in the following process of finding e, where s is obtained according to the following equation (4):

[0043]

[0044]

[0045] The system equation can be expanded in Taylor form as shown in equation (5):

[0046]

[0047] in,

[0048]

[0049]

[0050]

[0051] Where, e is the error vector (n rows and 1 column); b is the value of the increment of the approximate coordinate vector x0; B is the design matrix (n rows and 3 columns); Δ is the residual error vector (n rows and 1 column); x0 is the previous coordinate vector; ρ n (x0) is the double-differenced geometry using previous positions and satellite coordinates; n is the number of double-differenced observations.

[0052] Furthermore, the minimum value in MAFA can be determined by equation (6):

[0053]

[0054] The value of b is obtained according to equation (7):

[0055] b=-λ(B T WWB) -1 B T WΔ (7)

[0056] Among them, e is the error vector, T represents the transpose of the e matrix, and W is the weight matrix.

[0057] The verification method for a reference positioning system according to the example of the present application may be implemented as an instruction program in a programming language, so as to be executed by the reference positioning system or a device implementing the reference positioning system.

[0058] Figure 3 is a structural diagram of a verification system for a reference positioning system according to an example of the present application. The system includes a memory 30 and a processor 32. The memory 30 stores program instructions, and the processor 32 executes the instructions stored in the memory 30, and implements any one of the verification method examples described above in the process of executing the instructions. Figure 3 The verification system shown can be implemented in an electronic device such as a computer, such as a computer, a tablet, a smart phone, a measuring instrument, etc. Alternatively, the verification system can also be implemented in a receiver for receiving satellite signals. Figure 3 The illustrated verification system may also be implemented in a surveying instrument.

[0059] According to an example of the present application, a receiver is also provided. The receiver receives satellite signals for positioning. The receiver can be configured to include a receiver according to the present application in combination with a receiver. Figure 3 In some examples, the receiver is configured to implement a method described in any one of the examples of the method for verifying a reference positioning system described in this application.

[0060] According to the method of the example of the present application, the GPSO method is used to search within the search area to obtain a candidate positioning solution with higher positioning accuracy, and then the candidate positioning solution is used as the input of the MAFA model. The MAFA model calculates the final solution with a relatively high cost function value relatively quickly with less input.

[0061] When verifying the accuracy of the vehicle navigation system according to the verification method or verification system of the example of this application, the motion vectors of the same two adjacent epochs can be obtained by using the sensing data of the inertial sensor and the wheel speed information, for example, using the DR algorithm. Then, the motion vector calculated by the MAFA model is compared with the motion vector determined by the DR algorithm to judge the accuracy of the MAFA result, thereby verifying the parameter accuracy and operation of the reference positioning system. The introduction of the optimized search algorithm of GPSO avoids the MAFA model from using a step-by-step search to calculate the points in the search area one by one, thereby improving the search efficiency of the MAFA method.

[0062] Although specific embodiments of the present application have been shown and described in detail to illustrate the principles of the present application, it should be understood that the present application can be implemented in other ways without departing from such principles.

Claims

1. A verification method for a reference positioning system, the reference positioning system receiving satellite signals of a global navigation satellite system, characterized in that: The method comprises: Based on the carrier information of the satellite signal and the pseudorange of the navigation system, determine an initial floating point positioning solution that meets the threshold requirement by real-time differential positioning; Generate multiple initial candidate positioning solutions in the search area determined by the floating point positioning solution by a particle swarm genetic optimization algorithm; Input the multiple initial candidate positioning solutions into the improved ambiguity function model to obtain the first motion vector MV_1 of two adjacent epochs; Determine a second movement vector MV_2 based on the two adjacent epochs using data obtained by the non-satellite navigation device; Comparing the first motion vector MV_1 and the second motion vector MV_2; When the comparison result is within the receiving threshold, the verification of the reference positioning system is passed; otherwise, the verification fails.

2. The method according to claim 1, characterized in that The method further comprises: If the verification fails, a signal indicating that the verification fails is output.

3. The method according to claim 1, characterized in that Inputting the multiple initial candidate positioning solutions into the improved ambiguity function model to obtain the first motion vector MV_1 of two adjacent epochs includes: Inputting the multiple initial candidate positioning solutions generated by the particle swarm genetic optimization algorithm into an improved fuzzy function model; The improved fuzzy function model generates two adjacent epoch optimal positioning solutions based on the initial candidate positioning solution, which are the first epoch position P1 and the second epoch position P2; The first movement vector MV_1 is generated by the first epoch position P1 and the second epoch position P2.

4. The method according to claim 3, characterized in that The first epoch position P1 is a positioning solution with a minimum cost function among multiple solutions calculated by the improved fuzzy function model in the process of calculating the first epoch position, and the second epoch position P2 is a positioning solution with a minimum cost function among multiple solutions calculated by the improved fuzzy function model in the process of calculating the second epoch position.

5. The method according to claim 3, characterized in that: The improved fuzzy function model determines the minimum solution according to the following equation: Where b = -λ(B T WB) -1 B T WΔ, e is the error vector (n rows and 1 column); T represents the transpose of the e matrix; W is the weight matrix; b is the value of the increment of the approximate coordinate vector x0; B is the design matrix (n rows and 3 columns); Δ is the residual error vector (n rows and 1 column); x0 is the previous coordinate vector; ρ n (x0) is the double-difference geometry using previous positions and satellite coordinates; n is the number of double-differentiated observations.

6. The method according to claim 1, characterized in that Before the particle swarm genetic optimization algorithm generates a plurality of initial candidate positioning solutions in the search area determined by the floating point positioning solution, each parameter of the particle swarm genetic optimization algorithm is initialized.

7. The method according to any one of claims 1 to 6, characterized in that The reference positioning system is used in the navigation system of the vehicle, and the non-satellite navigation navigation device is an inertial navigation device arranged on the vehicle.

8. A verification system for a reference positioning system, characterized in that: The method comprises a memory and a processor, wherein the memory stores program instructions, and the processor is configured to execute the stored instructions and implement the method according to any one of claims 1 to 7 during the execution process.

9. The verification system according to claim 8, characterized in that: The system is implemented in electronic devices, and the data processing devices include computers, tablets, smart phones, satellite signal receivers, and measuring instruments.

10. A receiver, characterized in that: The receiver comprises the verification system according to claim 8, or the receiver is configured to implement the method according to any one of claims 1 to claim 7.

11. A computer storage medium for storing program instructions, wherein the program instructions implement the method according to any one of claims 1 to 7 when executed.

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