Validation method and validation system for reference positioning system, receiver, and storage medium

Abstract

The present application provides a validation method for a reference positioning system, the reference positioning system receives a satellite signal from a global navigation satellite system, the method comprises: determining, based on carrier information of the satellite signal and a pseudo-range of the navigation system, an initial floating point positioning solution that satisfies a threshold requirement with real time differential positioning; generating, by a genetic algorithm for particle swarm optimization, a plurality of initial candidate positioning solutions within a search region determined by the floating point positioning solution; inputting the plurality of initial candidate positioning solutions into a modified ambiguity function model to obtain a first mobile vector MV_1 of two adjacent epochs; determining a second mobile vector MV_2 based on the two adjacent epochs with data obtained by the set navigation device other than satellite navigation; comparing the first mobile vector MV_1 and the second mobile vector MV_2; when a comparison result is within a receiving threshold, considering validation of the reference positioning system passed; and conversely, considering the validation failed. A respective validation system, etc. are also provided.

Description

[0001] Validation Method and Validation System for Reference Positioning System, Receiver, And Storage Medium

[0002] Technical Field

[0003] The present application relates to real-time dynamic measurement techniques based on satellite signals, and more specifically relates to validation techniques for reference positioning systems.

[0004] Background Art

[0005] For navigation products that use satellites for navigation, a reference positioning system is often needed to validate its positioning accuracy. If a validation process is abnormal, it is indicated that positioning of the products is not accurate enough. However, for reference systems for validating navigation products, it is desirable to determine whether they are normal by a map in conjunction with a driving video of the vehicle. However, maps may not be available for reasons of compliance, etc. or reasons of collection costs of high-precision maps, etc. Therefore, it is necessary to provide a method for validating whether a reference positioning system is accurate without using a map.

[0006] Summary of Invention

[0007] One aspect of the present application is to provide a validation method for a reference positioning system, the reference positioning system receiving a satellite signal from a global navigation satellite system, the method comprising: determining, based on carrier information of the satellite signal and a pseudo-range of the navigation system, an initial floating point positioning solution that satisfies a threshold requirement with real time differential positioning; generating, by a genetic algorithm for particle swarm optimization, a plurality of initial candidate positioning solutions within a search region determined by the floating point positioning solution; inputting the plurality of initial candidate positioning solutions into a modified ambiguity function model to obtain a first mobile vector MV_1 of two adjacent epochs; determining a second mobile vector MV_2 based on the two adjacent epochs with data obtained by the set navigation device other than satellite navigation; comparing the first mobile vector MV_1 and the second mobile vector MV_2; when a comparison result is within a receiving threshold, considering validation of the reference positioning system passed; and conversely, considering the validation failed.

[0008] According to the validation method described in the present application, optionally, the method further comprises: in the event that the validation fails, outputting a signal for indicating that the validation fails.

[0009] According to the validation method described in the present application, optionally, inputting the plurality of initial candidate positioning solutions into a modified ambiguity function model to obtain a first mobile vector MV_1 of two adjacent epochs comprises: inputting the plurality of initial candidate positioning solutions generated by the genetic algorithm for particle swarm optimization into the modified ambiguity function model; generating, by the modified ambiguity function model, two adjacent epoch optimal positioning solutions based on the initial candidate positioning solutions, which comprise a first epoch position P1 and a second epoch position P2, respectively; producing, by the first epoch position P1 and the second epoch position P2, the first mobile vector MV_1.

[0010] According to the validation method described in the present application, optionally, the first epoch position P1 is a positioning solution with a minimum cost function in a plurality of solutions calculated in the process of calculating the first epoch position by the modified ambiguity function model, and the second epoch position P2 is a positioning solution with a minimum cost function in a plurality of solutions calculated in the process of calculating the second epoch position by the modified ambiguity function model.

[0011] According to the validation method described in the present application, optionally, various parameters of the genetic algorithm for particle swarm optimization are initialized prior to generating, by the genetic algorithm for particle swarm optimization, a plurality of initial candidate positioning solutions within the search region determined by the floating point positioning solution.

[0012] According to the validation method described in the present application, optionally, the reference positioning system is used for a navigation system of a vehicle, and the navigation device other than satellite navigation is an inertial navigation device disposed on the vehicle.

[0013] According to yet another aspect of the present application, a validation system for a reference positioning system is also provided, the validation system comprises a memory and a processor, the memory stores program instructions, and the processor is set to execute the stored instructions and implement any one of the validation method described above in an execution process.

[0014] According to another aspect of the present application, a receiver is also provided. The receiver may comprise the validation system described herein, or the receiver is set to implement any one of the validation method described above.

[0015] A computer storage medium is also provided, which is used for storing program instructions, the program instructions, when executed, implementing any one of the validation method described above.

[0016] Description of Accompanying Drawings

[0017] FIG. 1 is a flow diagram of a validation method for a reference positioning system according to an example of the present application;

[0018] FIG. 2 simply illustrates a process from an RTD positioning result to a first mobile vector MV_1 ;

[0019] FIG. 3 is a structural diagram of a validation system for a reference positioning system according to an example of the present application.

[0020] Specific Embodiments

[0021] To help those skilled in the art accurately understand the subject matter claimed in the present application, the specific embodiments of the present application will be described in detail below in conjunction with the accompanying drawings.

[0022] The terms “first,” “second,” and the like in the specification and claims as well as the accompanying drawings of the present application are used to distinguish similar objects, and not to describe a particular sequence or precedence order. It should be understood that the numbers so used are interchangeable where appropriate so that the examples of the present invention described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms “first” and “second” are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. As such, features defined as “first,” “second,” may expressly or implicitly comprise one or more of the features, unless otherwise indicated in the description of the examples of the present application, “plurality” means two or more.

[0023] The words “example” or “exemplary” or “exemplarily” as used below means “used as samples, as examples or for illustration”. Any example illustrated as “example” or “exemplary” or “exemplarily” needs not be construed as being superior or better than other examples.

[0024] The global navigation satellite system (GNSS) is a global positioning system consisting of a group of satellites and terrestrial receivers distributed globally. Using wireless signals transmitted by the satellites, the ground receivers may receive signals from a plurality of satellites. The receivers may calculate time of arrival of different satellite signals and distances between the satellites and the receivers based on carrier information of received signals and a pseudo-range of the system. For navigation products, the positioning accuracy of the products is typically validated by the reference positioning system. For example, for a navigation system of a vehicle, its positioning accuracy is validated by the reference positioning system.

[0025] For example, navigation data collected in a driving process of a vehicle is input into the reference positioning system, and the positioning accuracy of the navigation system is determined by the reference positioning system. The solution of the present application is to validate whether running results of the reference positioning system itself are accurate so as to avoid the use of inaccurate reference positioning system to validate the accuracy of navigation products. In still other cases, the reference positioning system is disposed on the vehicle, and thus the positioning accuracy of the vehicle navigation system may be directly validated in the driving process of the vehicle without the need to collect navigation data before the reference positioning system validates and operates navigation data for validation.

[0026] A genetic algorithm for particle swarm optimization (GPSO) is known as a global optimization algorithm. In an example of the present application, GNSS real time differential (RTD) positioning typically employs a modified ambiguity function approach (MAFA) to find optimal positioning solutions, while the present application uses GPSO in combination with MAFA, thereby providing a GPSO-MAFA algorithm. However, although the MAFA algorithm is employed in the example of the present application, the substitution of the MAFA algorithm with the ambiguity function method (AFM) is not excluded.

[0027] FIG. 1 is a flow diagram of a validation method for a reference positioning system according to an example of the present application. The reference positioning system is mentioned herein to be used for validating the positioning accuracy of navigation products (for example, receivers, etc.) based on satellite navigation, while the validation method according to the present application is used for validating the accuracy of the reference positioning system. The method according to the present application may be performed by the reference positioning system.

[0028] Referring to FIG. 1 , at step S100, based on carrier information of the satellite signal and a pseudo-range of the navigation system, an initial floating point positioning solution that satisfies a threshold requirement is determined with real time differential RTD positioning. The threshold requirement here is a preset positioning region range.

[0029] At step S102, a plurality of initial candidate positioning solutions are generated by a genetic algorithm for particle swarm optimization GPSO within a search region determined by the floating point positioning solution. It will be understood that before the GPSO is employed, various parameters of GPSO may be initialized such that the GPSO searches within the search region determined by the floating point positioning solution of RTD.

[0030] At step S104, a plurality of initial candidate positioning solutions are input into a modified ambiguity function model to obtain a first mobile vector MV_1 of two adjacent epochs. According to an example of the present application, MAFA calculates the epoch positions of the two adjacent epochs in sequence, expressed as the first epoch position P1 and the second epoch position P2, respectively, and the first mobile vector MV_1 is obtained from the two epoch positions.

[0031] At step S106, the data obtained with the set navigation device other than satellite navigation determines a second mobile vector MV_2 based on the two adjacent epochs, wherein the two adjacent epochs mentioned to obtain the second mobile vector MV_2 are the same as the two adjacent epochs in step S102. Taking the navigation system of the vehicle as an example, a navigation device other than satellite navigation is an inertial navigation device disposed on the vehicle, for example, may comprise an inertial sensor IMU of the vehicle, etc. The data obtained by the inertial navigation device of the vehicle comprises sensing data of the inertial sensor, and wheel speed data of the vehicle may also be obtained by a wheel speed sensor of the vehicle or an ESP system of the vehicle, etc., and a driving trajectory of two adjacent epochs is obtained based on the sensing data of the inertial sensor, the wheel speed data, etc., for example, dead reckoning with a dead reckoning (DR) algorithm.

[0032] At step S108, the first mobile vector MV_1 and the second mobile vector MV_2 are compared to determine if the comparison results of the two are within the receiving threshold. For example, an absolute value of a difference of the first mobile vector MV_1 and the second mobile vector MV_2 is compared to the receiving threshold to determine if it falls within the receiving threshold. The receiving threshold is preset, and it is a numerical range that can be received if the comparison results fall within the receiving threshold.

[0033] If the comparison results fall within the receiving threshold, go to step S108, indicating that the first mobile vector MV_1 and the second mobile vector MV_2 are substantially the same, and illustrating that the parameters of the reference positioning system are normal to run and the validation passes. If the comparison result is that the absolute value falls outside the range of the receiving threshold, go to step S109, indicating that there is an abnormality in the reference positioning system and the validation fails. According to an example of the present application, at step S109, the signal indicating that the reference positioning system does not pass validation can be further output. According to still other examples of the present application, at step S108, the signal indicating that the reference positioning system passes the validation can be further output.

[0034] FIG. 2 simply illustrates a process from an RTD positioning result to a first mobile vector MV_1. FIG. 1 and FIG. 2 may be incorporated simultaneously. GPSO searches within the region 20 determined by the initial floating point positioning solution determined by RTD to generate a plurality of initial candidate positioning solutions based on the search region 20, and these solutions are illustrated with solid dots in the figures.

[0035] The plurality of initial candidate positioning solutions are taken as input to the MAFA model 22, and the plurality of MAFA solutions are obtained by the MAFA model 22 calculating the plurality of candidate positioning solutions that are input respectively. The MAFA model can select to take the positioning solution with the minimum cost function in these solutions as the first epoch position P1 . Subsequently, a plurality of initial floating point positioning solutions are again obtained by the GPSO from the RTD positioning results, these solutions in turn serve as the input to the MAFA model 22, and the MAFA calculates the plurality of MAFA solutions and takes the solution with the minimum cost function in the plurality of MAFA solutions as the second epoch position P2. The first mobile vector MV_1 between the first epoch position P1 and the second epoch position P2 is determined by the two epoch positions.

[0036] In summary, based on the candidate positioning solution given by the particle population search algorithm GPSO, MAFA convergence solutions and cost functions corresponding to various candidate solutions are calculated by the MAFA model, and the process is iterated until the iterative convergence condition is met and the current epoch optimal positioning solution P1 is given. The next epoch optimal positioning solution P2 is calculated using the same method, and the motion vector MV_1 of the two adjacent epochs is calculated based on the P1 and P2 positioning solutions.

[0037] It is to be noted that in still other possible examples, the maximum value in the MAFA solutions may be selected as the epoch position.

[0038] In the absence of GPSO, i.e. , under the existing conventional approach, the MAFA model directly performs MAFA calculation on points within the region determined by initial floating points of the RTD one by one, and the speed is relatively low as there are too many points calculated by the MAFA. According to the method of an example of the present application, the search region is first initialized by the GPSO according to the initial floating point positioning solution of the RTD, a plurality of candidate positioning solutions can be determined relatively quickly, most of points in the search region are screened out, resulting in much less input into the MAFA model, increasing the MAFA calculation speed, and meanwhile reducing the MAFA calculation amount. Moreover, since the MAFA algorithm itself is insensitive to cycle slip, the calculation process performed by MAFA in cooperation with the GPSO algorithm is also insensitive to cycle slip.

[0039] The MAFA model and its calculation process are briefly described below.

[0040] A double differential viewing model is used to reduce or eliminate errors in the reference positioning system, as shown by equation (1): wherein Φ is measurements of a double differential of a carrier phase with a whole circle as a unit; λ is a wavelength of the carrier phase in meters; e is a residual value of the whole circle, which is usually the noise of the measurements; p(Xc) is a double- differential geometric range having a position of the real positioning system; a is the double-differential integer ambiguity of the whole circle.

[0041] When using p to simply represent p(Xc) in equation (1), equation (1) may be simplified to equation (2):

[0042] Considering that the residual value e cannot exceed half a cycle and the ambiguity parameter has an integer property, equation (2) may be rewritten into the form of equation (3): wherein round(.) is a rounded rounding function.

[0043] The equation (3) may be updated using different and continuous functions, as shown in the following process of obtaining e, wherein s is obtained according to formula (4): e = s - round{s) The system equation can be expanded in Taylor form, as shown in formula (5) below: wherein,

[0044] Wherein, e is an error vector (n rows, 1 column); b is a value of an increment of an approximate coordinate vector xo; B is a design matrix (n rows, 3 columns); Δ is a residual error vector (n rows, 1 column); xo is a previous coordinate vector; pn(x0) is a double- differential geometric range utilizing a previous position and satellite coordinates; n is the number of double-differential observed values.

[0045] Further, the minimum value in MAFA can be determined by equation (6): wherein the value of b is obtained according to equation (7): wherein e is the error vector, T represents transposing of the e matrix, and W is the weight matrix.

[0046] The validation method for the reference positioning system according to an example of the present application may be implemented into an instruction program by a programming language, thereby being executed by the reference positioning system or a device implementing the reference positioning system.

[0047] FIG. 3 is a structural diagram of a validation system for a reference positioning system according to an example of the present application. The system comprises a memory 30 and a processor 32. The memory 30 stores program instructions, and the processor 32 executes instructions stored by the memory 30 and implements any of examples implementing the above-described validation method in the process of executing the instructions. The validation system as shown in FIG. 3 may be implemented in an electronic device, for example, a computer, and the electronic device is, for example, a computer, a tablet, a smartphone, a measurement instrument, etc. Alternatively, the validation system may also be implemented in a receiver for receiving satellite signals. The validation system as shown in FIG. 3 may also be implemented in a mapping instrument.

[0048] According to an example of the present application, a receiver is also provided. The receiver receives satellite signals for positioning. The receiver may be set to comprise the validation system described in connection with FIG. 3 of the present application. In some examples, the receiver is set to implement a method described in any example in the validation method for the reference positioning system described in the present application.

[0049] According to the method of the example of the present application, a candidate positioning solution with a higher positioning accuracy is obtained by searching within a search region using the GPSO method, then the candidate positioning solution is used as input to the MAFA model, and the MAFA model calculates a final solution with relatively high cost function value relatively fast with relatively low input.

[0050] In the event that the accuracy of the vehicle navigation system is validated according to a validation method or a validation system according to an example of the present application, a mobile vector of the same two adjacent epochs may be obtained using, for example, the DR algorithm by sensing data of an inertial sensor and wheel speed information. The mobile vector calculated by the MAFA model is then compared to the mobile vector determined by the DR algorithm, thereby determining the accuracy of the MAFA result, and further validating the parameter accuracy and operation conditions of the reference positioning system. The introduction of optimized search algorithms for GPSO eliminates the use of step-by-step search by the MAFA model to calculate points within the search region one by one, thereby improving the search efficiency of the MAFA method.

[0051] While specific examples of the present application have been shown and described in detail to illustrate the principles of the present application, it will be appreciated that the present application may be implemented in other ways without departing from such principles.

Claims

CLAIMS1. A validation method for a reference positioning system, the reference positioning system receiving a satellite signal from a global navigation satellite system, wherein the method comprises: determining, based on carrier information of the satellite signal and a pseudo-range of a navigation system, an initial floating point positioning solution that satisfies a threshold requirement with real time differential positioning; generating, by a genetic algorithm for particle swarm optimization, a plurality of initial candidate positioning solutions within a search region determined by the floating point positioning solution; inputting the plurality of initial candidate positioning solutions into a modified ambiguity function model to obtain a first mobile vector MV_1 of two adjacent epochs; determining a second mobile vector MV_2 based on the two adjacent epochs with data obtained by the set navigation device other than satellite navigation; comparing the first mobile vector MV_1 and the second mobile vector MV_2; when a comparison result is within a receiving threshold, considering validation of the reference positioning system passed; and conversely, considering the validation failed.

2. The method according to Claim 1 , wherein the method further comprises: in the event that the validation fails, outputting a signal for indicating that the validation fails.

3. The method according to Claim 1 , wherein inputting the plurality of initial candidate positioning solutions into a modified ambiguity function model to obtain a first mobile vector MV_1 of two adjacent epochs comprising: inputting the plurality of initial candidate positioning solutions generated by the genetic algorithm for particle swarm optimization into the modified ambiguity function model; generating, by the modified ambiguity function model, two adjacent epoch optimal positioning solutions based on the initial candidate positioning solutions, which are a firstepoch position P1 and a second epoch position P2, respectively; producing, by the first epoch position P1 and the second epoch position P2, the first mobile vector MV 1.

4. The method according to Claim 3, wherein the first epoch position P1 is a positioning solution with a minimum cost function in a plurality of solutions calculated in the process of calculating the first epoch position by the modified ambiguity function model, and the second epoch position P2 is a positioning solution with a minimum cost function in a plurality of solutions calculated in the process of calculating the second epoch position by the modified ambiguity function model.

5. The method according to Claim 3, wherein the modified ambiguity function model determines a minimum solution according to the following equation:wherein,e is an error vector (n rows, 1 column); T represents transposing of an e-matrix; W is a weight matrix; b is a value of an increment of an approximate coordinate vector x0; Bis a design matrix (n rows, 3 columns); Δ is a residual error vector (n rows, 1 column); x0is a previous coordinate vector;n(x0) is a double-differential geometric range employing a previous position and satellite coordinates; n is a number of double-differential observed values.

6. The method according to Claim 1 , wherein various parameters of the genetic algorithm for particle swarm optimization are initialized prior to generating, by the genetic algorithm for particle swarm optimization, a plurality of initial candidate positioning solutions within the search region determined by the floating point positioning solution.

7. The method according to any one of Claims 1 to 6, wherein the reference positioning system is used for a navigation system of a vehicle, and the navigation device other than satellite navigation is an inertial navigation device disposed on the vehicle.

8. A validation system for a reference positioning system, wherein the validation system comprises a memory and a processor, the memory stores program instructions, and the processor is set to execute the stored instructions and implement the method according to any one of Claim 1 to Claim 7 in an execution process.

9. The validation system according to Claim 8, wherein the system is implemented in an electronic device, and a data processing device comprises a computer, a tablet, a smartphone, a satellite signal receiver, and a measurement instrument.

10. A receiver, wherein the receiver comprises the validation system according to Claim 8, or the receiver is set to be capable of implementing the method according to any one of Claim 1 to Claim 7.

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

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