A method, apparatus, and medium for reducing observation update error

Abstract

The application discloses a method, device and medium for reducing observation update error, and relates to the technical field of satellite positioning. The observation value of the PPP-RTK of the current epoch is acquired, and the error data of the current epoch and the error data of a preset number of continuous epochs before the current epoch are acquired respectively; each error data is weighted, and the output value of the filter and the filter weight value are acquired according to each weighted error data; the iteration formula of the filter weight value is acquired; the iteration error signal is acquired according to the observation value, the output value and the iteration formula, so as to adjust the filter through the iteration error signal. In the above scheme, the filter filtering does not depend on the accuracy parameter output by the PPP-RTK algorithm, and only uses the input observation value and error data as the reference signal input; once an abnormal value appears, the filter can track the change, readjust the parameter, reduce the observation update error, improve the positioning accuracy, and make the filter performance optimal again.

Description

Technical Field

[0001] This application relates to the field of satellite positioning technology, and in particular to a method, apparatus and medium for reducing observation update errors. Background Technology

[0002] Currently, the combined positioning method based on Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), and wheel velocity meter is being used more and more widely. Among them, GNSS uses the PPP-RTK (Precise Point Positioning–Real Time Kinematic) algorithm, which is a high-precision positioning technology based on the state domain, and can provide high-precision positioning services with fast convergence worldwide.

[0003] However, the PPP-RTK algorithm is also affected by satellite signals. In complex environments such as urban canyons, tree-lined roads, and under overpasses, or when the vehicle receiving the receiver is stationary, non-fixed solutions and outliers are prone to occur. However, the standard deviation (STD) provided by the PPP-RTK algorithm cannot adequately reflect these outliers, thus impacting the performance of the integrated navigation algorithm.

[0004] Given the above issues, how to filter outliers in PPP-RTK input data, reduce observation update errors, and improve positioning accuracy is a problem that technical personnel in this field urgently need to solve. Summary of the Invention

[0005] The purpose of this application is to provide a method, apparatus, and medium for reducing observation update errors, thereby filtering outomas in PPP-RTK input data, reducing observation update errors, and improving positioning accuracy.

[0006] To address the aforementioned technical problems, this application provides a method for reducing observation update errors, comprising:

[0007] Obtain the PPP-RTK observation value of the current epoch, and obtain the error data of the current epoch and the error data of the consecutive preset number of epochs before the current epoch; wherein the observation value exists in each epoch where the error data is located.

[0008] The error data are weighted, and the output value and filter weights are obtained based on the weighted error data.

[0009] An iterative formula for obtaining the filter weights is obtained; an iterative error signal is obtained based on the observed value, the output value, and the iterative formula, and is used to adjust the filter using the iterative error signal.

[0010] Preferably, obtaining the filter output value and filter weights based on the weighted error data includes:

[0011] The weighted error data are used as input, and the output value and the filter weights are obtained through the filter output formula.

[0012] The filter output formula includes:

[0013]

[0014] Where y(n) is the output value, wi(n) is the weighted value of the error data, x(n-1) is the error data, X(n) is the weighted error data, W(n) is the filter weight, n = 0, 1, 2, ..., N-1, and N is a positive integer.

[0015] Preferably, the iterative formula for obtaining the filter weights includes:

[0016] W(n+1) = W(n) + 2μX(n)e(n);

[0017] Where W(n) is the filter weight, μ is the filter learning rate, X(n) is the weighted error data, e(n) is the iterative error signal, n = 0, 1, 2, ..., N-1, and N is a positive integer.

[0018] Preferably, the step of acquiring the error data of the current epoch and the error data of the preceding preset number of epochs includes:

[0019] The state domain correction data for the current epoch and the state domain correction data for the preset number of consecutive epochs preceding the current epoch are obtained respectively.

[0020] Preferably, adjusting the filter using the iterative error signal includes:

[0021] Obtain the prediction error value of the Kalman filter in the integrated navigation system;

[0022] The observation information is obtained based on the iterative error signal and the prediction error value;

[0023] The observation noise covariance of the Kalman filter is obtained based on the observation information, and the filter is adjusted using the observation noise covariance.

[0024] Preferably, after obtaining the iterative error signal based on the observed value, the output value, and the iterative formula, the method further includes:

[0025] Output a message indicating that the iteration error signal was successfully acquired.

[0026] Preferably, after adjusting the filter using the iterative error signal, the method further includes:

[0027] Generate the adjustment log for the filter.

[0028] To address the aforementioned technical problems, this application also provides an apparatus for reducing observation update errors, comprising:

[0029] The first acquisition module is used to acquire the PPP-RTK observation value of the current epoch, and to acquire the error data of the current epoch and the error data of the previous preset number of epochs respectively; wherein the observation value exists in each epoch where the error data is located.

[0030] The second acquisition module is used to perform weighted processing on each of the error data, and to acquire the output value of the filter and the filter weights based on the weighted error data.

[0031] The third acquisition module is used to acquire the iterative formula for the filter weights;

[0032] The fourth acquisition module is used to acquire an iteration error signal based on the observed value, the output value, and the iteration formula, so as to adjust the filter using the iteration error signal.

[0033] To address the aforementioned technical problems, this application also provides another device for reducing observation update errors, comprising:

[0034] Memory, used to store computer programs;

[0035] A processor is used to implement the above-described method steps for reducing observation update errors when executing the computer program.

[0036] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for reducing observation update errors described above.

[0037] The method for reducing observation update errors provided in this application involves acquiring PPP-RTK observations for the current epoch, and separately acquiring error data for the current epoch and error data for a predetermined number of consecutive epochs preceding the current epoch. Each epoch containing error data contains observations. The error data is weighted, and the output value and filter weights are obtained based on the weighted error data. An iterative formula for the filter weights is obtained. An iterative error signal is obtained based on the observations, output value, and iterative formula, and is used to adjust the filter. Therefore, the filter in this scheme does not rely on the accuracy parameters output by the PPP-RTK algorithm; it only uses the input observations and error data as reference signals. Once an outlier occurs, the filter can track this change, readjust its parameters, reduce observation update errors, improve positioning accuracy, and restore the filter's performance to its optimal state.

[0038] In addition, this application also provides a device and medium for reducing observation update errors, with the same effect as above. Attached Figure Description

[0039] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 A schematic diagram of combined localization fusion filtering provided in an embodiment of this application;

[0041] Figure 2 A flowchart illustrating a method for reducing observation update errors provided in this application embodiment;

[0042] Figure 3 A schematic diagram of adaptive filtering provided in an embodiment of this application;

[0043] Figure 4 A schematic diagram of an apparatus for reducing observation update errors provided in an embodiment of this application;

[0044] Figure 5 This is a schematic diagram of another device for reducing observation update errors provided in an embodiment of this application. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0046] The core of this application is to provide a method, apparatus, and medium for reducing observation update errors.

[0047] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Currently, GNSS can provide continuous and accurate autonomous positioning at a relatively low cost, and GNSS position calculation does not generate cumulative errors. However, the positioning performance of GNSS is not only related to the performance of the satellite positioning receiver, but also to environmental factors in the GNSS space segment, ground control segment, and signal propagation path, especially the surrounding environment of the satellite positioning receiver. In relatively open environments, GNSS positioning accuracy is relatively high, while in environments with severe obstruction, GNSS positioning accuracy will decrease or even fail to achieve positioning. Inertial navigation systems (INS) can also achieve low-cost, short-term, high-precision autonomous positioning, but their long-term position calculation is also susceptible to cumulative and drift errors, resulting in significant errors.

[0049] Therefore, GNSS output information and INS sensor output information are fused. GNSS's advantage of having no accumulated error compensates for INS's susceptibility to accumulated errors, while INS's low susceptibility to environmental influences offsets GNSS's susceptibility to environmental influences. This complementary approach achieves low-cost, high-precision positioning. However, since GNSS is completely lost when the receiver is in a long tunnel, prolonged use of INS positioning alone will increase its accumulated error. In this case, auxiliary information such as wheel speedometers is needed to correct the INS error.

[0050] The above-mentioned positioning method based on GNSS / INS / wheel speed meter requires a filter to extract and process different signal features. In practical applications, different sensors have different output frequencies. Therefore, before fusion filtering, it is necessary to perform time calibration on both GNSS and INS, and use the fusion output as the final output. Figure 1 This is a schematic diagram of the combined positioning and fusion filtering provided in an embodiment of this application. The filtering structure is as follows: Figure 1 As shown.

[0051] It is important to note that GNSS employs the PPP-RTK algorithm, which combines the advantages of Real-Time Kinematic (RTK) and Precise Point Position (PPP). It overcomes the limitations of traditional RTK technology in internet wireless communication and the excessively long convergence time of PPP, thus possessing the advantages of both in practical applications. It can provide high-precision positioning services with rapid convergence worldwide. However, the PPP-RTK algorithm is also affected by satellite signals. In complex environments such as urban canyons, tree-lined roads, under overpasses, or when the system is parked, it is prone to non-fixed solutions and outliers. The standard deviation provided by the algorithm cannot adequately reflect these outliers, thus affecting the performance of the integrated navigation algorithm. Therefore, this application provides a method for observation update error, which can filter outliers in PPP-RTK input data. Figure 2 A flowchart illustrating a method for reducing observation update errors provided in an embodiment of this application. Figure 2 As shown, the method includes:

[0052] S10: Obtain the PPP-RTK observations for the current epoch, and obtain the error data for the current epoch and the error data for the previous preset number of epochs respectively.

[0053] Each epoch containing error data contains observations.

[0054] Specifically, after receiving the observation value transmitted by PPP-RTK at the current epoch, it is used as the expected value vector d(n) of the filter; at the same time, the error data of the current epoch and the error data of the preset number of epochs before the current epoch are obtained.

[0055] It is important to note that observations are present in each epoch of the error data acquisition. This embodiment does not limit the preset number of observations; it depends on the specific implementation. Furthermore, the source of the error data can be observational information, the standard deviation of GNSS data, or PPP-RTK State Space Representation (SSR) data, depending on the specific implementation. As a preferred embodiment, the error data uses SSR data, specifically including orbital clock errors, code biases, phase biases, grid ionospheric errors, and tropospheric errors.

[0056] S11: Perform weighted processing on each error data, and obtain the output value and filter weights of the filter based on the weighted error data.

[0057] Figure 3 This is a schematic diagram of the adaptive filtering principle provided in an embodiment of this application. Figure 3As shown, after obtaining the SSR data, the error data is further weighted to serve as the input signal vector X(n) of the filter, thereby obtaining the output value and filter weights of the filter based on the weighted error data.

[0058] In a preferred embodiment, the weighted error data are used as input, and the filter output value and filter weights are obtained through the filter output formula. The filter output formula is shown below:

[0059]

[0060] Where y(n) is the output value, w i (n) represents the weighted value of the error data, x(n-1) represents the error data, X(n) represents the weighted error data, and W(n) represents the filter weights. n = 0, 1, 2, ..., N-1, where N is a positive integer.

[0061] S12: Iterative formula for obtaining filter weights.

[0062] Furthermore, based on the obtained filter weights W(n), its iterative formula is derived. The iterative formula is shown below:

[0063] W(n+1) = W(n) + 2μX(n)e(n);

[0064] Where W(n) is the filter weight, μ is the filter learning rate, X(n) is the weighted error data, e(n) is the iteration error signal, n = 0, 1, 2, ..., N-1, and N is a positive integer.

[0065] It should be noted that μ is the filter learning rate, and in practice, its value typically ranges from 0 to the reciprocal of the largest eigenvalue of the X(n) matrix. This embodiment does not impose any restrictions on the specific value of the filter learning rate; it depends on the specific implementation.

[0066] S13: Obtain the iterative error signal based on the observed value, output value, and iterative formula, so as to adjust the filter using the iterative error signal.

[0067] Finally, given the observed values ​​and output values ​​y(n) as the expected value vector d(n), the iterative error signal e(n) is obtained through the iterative formula:

[0068] e(n)=d(n)-y(n)=d(n)-W T (n)X(n);

[0069] It is important to note that in this embodiment, the sum of the observed value and the observation error is used as the expected value, and the SSR data error is used as the filter input. The iterative error signal e(n) is the final GNSS signal output. Using the aforementioned adaptive filtering algorithm, more accurate position information can be obtained by calculating the instantaneous gradient and updating the filter coefficients.

[0070] In order to adjust the filter and output positioning information with higher accuracy, in specific implementation, after obtaining the iterative error signal, the prediction error value of the integrated navigation Kalman filter is obtained. It should be noted that the prediction error value is a prediction value generated in advance based on various errors in the positioning system. In this embodiment, the generation process of the prediction error value is not limited and depends on the specific implementation situation.

[0071] Further, observational information is obtained based on the iterative error signal and the prediction error value. Specifically, the difference between the iterative error signal and the prediction error value is calculated to obtain observational information for a continuous preset number of epochs. Finally, the observation noise covariance of the Kalman filter is obtained based on the observational information, which is used to adjust the filter. It should be noted that the observation noise covariance can be obtained by calculating the covariance of the observational information, or by other methods. This embodiment does not limit the specific process of obtaining the observation noise covariance, and it depends on the specific implementation.

[0072] In this embodiment, the PPP-RTK observations of the current epoch are acquired, along with the error data of the current epoch and the error data of a predetermined number of consecutive epochs preceding the current epoch. Each epoch containing error data contains observations. The error data are weighted, and the output value and filter weights are obtained based on the weighted error data. An iterative formula for the filter weights is obtained. An iterative error signal is obtained based on the observations, output value, and iterative formula, and is used to adjust the filter. Therefore, the filter in the above scheme does not rely on the accuracy parameters output by the PPP-RTK algorithm; it only uses the input observations and error data as reference signals. Once an outlier occurs, the filter can track this change, readjust its parameters, reduce observation update errors, improve positioning accuracy, and allow the filter performance to return to its optimal state.

[0073] In addition, to enable users to better understand the error situation in the current combined positioning process, after obtaining the iterative error signal based on the observed value, output value and iterative formula, a prompt message indicating successful acquisition of the iterative error signal can be output, so that users can keep abreast of the current positioning error situation through this prompt message.

[0074] Furthermore, after adjusting the filter using the iterative error signal, a filter adjustment log is generated so that the user can be informed of the entire process of error adjustment during the combined positioning process.

[0075] In the above embodiments, the method for reducing observation update errors has been described in detail. This application also provides embodiments of an apparatus for reducing observation update errors. It should be noted that this application describes the embodiments of the apparatus from two perspectives: one based on functional modules and the other based on hardware structure.

[0076] Figure 4 This is a schematic diagram of an apparatus for reducing observation update errors provided in an embodiment of this application. Figure 4 As shown, the device for reducing observation update errors includes:

[0077] The first acquisition module 10 is used to acquire the PPP-RTK observations of the current epoch, and to acquire the error data of the current epoch and the error data of a preset number of consecutive epochs preceding the current epoch. Each epoch containing the error data contains observations.

[0078] The second acquisition module 11 is used to perform weighted processing on each error data, and to obtain the output value of the filter and the filter weights based on the weighted error data.

[0079] The third acquisition module 12 is used to acquire the iterative formula for the filter weights.

[0080] The fourth acquisition module 13 is used to acquire the iteration error signal based on the observed value, the output value and the iteration formula, so as to adjust the filter through the iteration error signal.

[0081] In this embodiment, the device for reducing observation update errors includes a first acquisition module, a second acquisition module, a third acquisition module, and a fourth acquisition module. The method implemented by the device is the same as the method for reducing observation update errors described above. It acquires the PPP-RTK observation values ​​of the current epoch, and acquires the error data of the current epoch and the error data of a preset number of consecutive epochs preceding the current epoch; wherein each epoch containing error data contains observation values; it performs weighted processing on each error data, and acquires the output value and filter weights based on the weighted error data; it acquires the iterative formula for the filter weights; and it acquires the iterative error signal based on the observation values, output values, and iterative formula, which is used to adjust the filter. Therefore, in the above scheme, the filter filtering does not rely on the accuracy parameters output by the PPP-RTK algorithm, but only uses the input observations and error data as reference signals; once an outlier occurs, the filter can track this change, readjust the parameters, reduce observation update errors, improve positioning accuracy, and allow the filter performance to reach its optimal state again.

[0082] Figure 5 This is a schematic diagram of another device for reducing observation update errors provided in an embodiment of this application.

[0083] like Figure 5 As shown, the device for reducing observation update errors includes:

[0084] Memory 20 is used to store computer programs.

[0085] The processor 21 is configured to implement the steps of the method for reducing observation update error as described in the above embodiments when executing a computer program.

[0086] The device for reducing observation update errors provided in this embodiment may include, but is not limited to, smartphones, tablets, laptops, or desktop computers.

[0087] The processor 21 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 21 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 21 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 21 may also include an Artificial Intelligence (AI) processor, which handles computational operations related to machine learning.

[0088] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 20 is used to store at least the following computer program 201, which, after being loaded and executed by the processor 21, is capable of implementing the relevant steps of the method for reducing observation update errors disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, and the storage method may be temporary or permanent storage. The operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include, but is not limited to, the data involved in the method for reducing observation update errors.

[0089] In some embodiments, the apparatus for reducing observation update errors may further include a display screen 22, an input / output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.

[0090] Those skilled in the art will understand that Figure 5 The structure shown does not constitute a limitation on the device for reducing observation update errors and may include more or fewer components than illustrated.

[0091] In this embodiment, the device for reducing observation update errors includes a memory and a processor. The memory stores a computer program. The processor executes the computer program to implement the steps of the method for reducing observation update errors as described in the above embodiment. This involves acquiring PPP-RTK observations for the current epoch, and acquiring error data for the current epoch and error data for a predetermined number of consecutive epochs preceding the current epoch; wherein each epoch containing error data contains observations; weighting the error data; obtaining the filter output value and filter weights based on the weighted error data; obtaining the iterative formula for the filter weights; and obtaining an iterative error signal based on the observations, output value, and iterative formula, which is used to adjust the filter. Therefore, in the above scheme, the filter does not rely on the accuracy parameters output by the PPP-RTK algorithm, but only uses the input observations and error data as reference signals. Once an outlier occurs, the filter can track this change, readjust its parameters, reduce observation update errors, improve positioning accuracy, and allow the filter performance to return to its optimal state.

[0092] Finally, this application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above method embodiments.

[0093] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0094] In this embodiment, a computer program is stored on a computer-readable storage medium. When the computer program is executed by a processor, it implements the steps described in the above method embodiment. This involves acquiring the PPP-RTK observations of the current epoch, and acquiring the error data of the current epoch and the error data of a preset number of consecutive epochs preceding the current epoch; wherein each epoch containing error data contains observations; weighting the error data; and obtaining the filter output value based on the weighted error data.

[0095] The filter weights are determined using an iterative formula; the iterative error signal is obtained based on the observed values, output values, and the iterative formula, and is used to adjust the filter. Therefore, the filter in the above scheme does not rely on the accuracy parameters output by the PPP-RTK algorithm, but only uses the input observed values ​​and error data as reference signals. Once an outlier occurs, the filter can track this change, readjust its parameters, reduce observation update errors, improve positioning accuracy, and bring the filter performance back to its optimal state.

[0096] The foregoing has provided a detailed description of a method, apparatus, and medium for reducing observation update errors provided in this application. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0097] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method of reducing observation update error, characterized by, The method comprises the following steps: acquiring observation values of PPP-RTK at a current epoch, and acquiring error data at the current epoch and error data at a preset number of epochs before the current epoch respectively; wherein each of the error data is at an epoch in which the observation values exist; performing weighted processing on each of the error data, and acquiring an output value of a filter and a filter weight value according to each of the error data after weighting; acquiring an iterative formula of the filter weight value; acquiring an iterative error signal according to the observation values, the output value and the iterative formula, so as to adjust the filter through the iterative error signal.

2. The method of reducing observation update error of claim 1, wherein, The acquiring of the output value of the filter and the filter weight value according to each of the error data after weighting comprises: acquiring the output value of the filter and the filter weight value through a filter output formula with each of the error data after weighting as input; The filter output formula comprises: where y(n) is the output value, w i (n) is the weighted value of the error data, x(n-1) is the error data, X(n) is the weighted error data, W(n) is the filter weight, n=0, 1, 2, …, N-1, and N is a positive integer.

3. The method of reducing observation update error of claim 2, wherein, The acquiring of the iterative formula of the filter weight value comprises: W(n+1)=W(n)+2μX(n)e(n); wherein W(n) is the filter weight value, μ is a filter learning rate, X(n) is the error data after weighting, e(n) is the iterative error signal, n=0,1,2,...,N-1, and N is a positive integer.

4. The method of reducing observation update error of claim 1, wherein, The acquiring of the error data at the current epoch and the error data at a preset number of epochs before the current epoch respectively comprises: acquiring state domain correction data at the current epoch and state domain correction data at a preset number of epochs before the current epoch respectively.

5. The method of reducing observation update error of claim 1, wherein, The adjusting of the filter through the iterative error signal comprises: acquiring a prediction error value of a combined navigation Kalman filter; acquiring observation innovation according to the iterative error signal and the prediction error value; acquiring observation noise covariance of Kalman filtering according to the observation innovation, so as to adjust the filter through the observation noise covariance.

6. The method of reducing observation update error according to any one of claims 1 to 5, wherein, After the acquiring of the iterative error signal according to the observation values, the output value and the iterative formula, the method further comprises: outputting prompt information that the acquiring of the iterative error signal is successful.

7. The method of reducing observation update error of claim 6, wherein, After the adjusting of the filter through the iterative error signal, the method further comprises: generating an adjustment log of the filter.

8. An apparatus for reducing observation update error, the apparatus comprising: The method comprises the following steps: a first acquiring module is configured to acquire observation values of PPP-RTK at a current epoch, and acquire error data at the current epoch and error data at a preset number of epochs before the current epoch respectively; wherein each of the error data is at an epoch in which the observation values exist; a second acquiring module is configured to perform weighted processing on each of the error data, and acquire an output value of a filter and a filter weight value according to each of the error data after weighting; a third acquiring module is configured to acquire an iterative formula of the filter weight value; a fourth acquiring module is configured to acquire an iterative error signal according to the observation values, the output value and the iterative formula, so as to adjust the filter through the iterative error signal.

9. An apparatus for reducing observation update error, the apparatus comprising: The method comprises the following steps: a memory is configured to store a computer program; A processor for implementing the steps of the method of reducing observation update error as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of reducing observation update error as claimed in any one of claims 1 to 7.

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