Receiver position positioning method, electronic equipment and storage medium

Abstract

The invention provides a receiver position positioning method, electronic equipment and a storage medium, relates to the technical field of high-precision positioning and navigation, and is used for improving the positioning precision. The method comprises the following steps: acquiring a double-frequency observation value generated by a receiver in response to a satellite signal; based on the double-frequency observation value, generating an ionosphere comprehensive index which is used for representing the total interference degree of ionosphere disturbance on the satellite signal; the ionosphere comprehensive index is mapped into a variance of an ionosphere pseudo observation value, the variance of the ionosphere pseudo observation value is used for representing a constraint weight of a to-be-estimated parameter, and the to-be-estimated parameter at least comprises a position parameter of a receiver and an ionosphere delay parameter; positioning the receiver in the ionosphere weighting model according to the variance of the ionosphere pseudo observation value to obtain a positioning result; wherein the ionosphere weighting model is used for representing a mode of allocating constraint weights of the to-be-estimated parameters, and the positioning result is used for representing an optimal estimation value of the to-be-estimated parameters.

Description

Technical Field

[0001] This application relates to the field of high-precision positioning and navigation technology, and in particular to a method for locating the position of a receiver, an electronic device, and a storage medium. Background Technology

[0002] Currently, high-precision positioning services such as Network Real-Time Kinematic (NRTK) and Precise Point Positioning-Real-Time Kinematic (PPP-RTK) are a type of real-time positioning technology that relies on a network of reference stations and precise data processing capabilities. This type of technology typically deploys a continuously operating network of reference stations within a certain area. The network side aggregates and processes observation data from multiple stations to generate differential correction information (including ionospheric correlation corrections) for the user, which is then transmitted to the user's end via a communication link, thereby supporting the user in achieving real-time high-precision positioning.

[0003] However, the ionosphere exhibits significant spatiotemporal variation characteristics. Under conditions such as enhanced ionospheric activity, abrupt gradient changes, or scintillation, the effectiveness of differential correction information from the network side fluctuates across different user locations and satellite line-of-sight directions, thereby affecting the stability and reliability of terminal computation.

[0004] In related technologies, the characterization and transmission of ionospheric activity levels largely rely on platform-side or external monitoring capabilities, requiring the network side to additionally calculate and broadcast corresponding activity indicators or quality information to the terminal. However, in current common NRTK or PPP-RTK service modes and broadcast content, the platform side typically broadcasts correction data, often lacking information on activity levels per satellite and per epoch, along with supporting information directly usable for terminal adaptive processing. This approach results in a lack of a unified, real-time source of activity level information for terminals in ionospheric activity scenarios, leading to low positioning accuracy. Summary of the Invention

[0005] This application provides a method for locating the position of a receiver, an electronic device, and a storage medium to improve positioning accuracy.

[0006] In a first aspect, this application provides a method for locating a receiver, the method comprising: acquiring dual-frequency observations generated by the receiver in response to satellite signals, wherein the dual-frequency observations include pseudorange observations and carrier phase observations measured at a first frequency and a second frequency, respectively; generating an ionospheric composite index based on the dual-frequency observations, wherein the ionospheric composite index is used to represent the overall interference level of ionospheric disturbances on satellite signals; mapping the ionospheric composite index to the variance of ionospheric pseudo-observations, wherein the variance of ionospheric pseudo-observations is used to represent the constraint weights of parameters to be estimated, the parameters to be estimated including at least the receiver's position parameters and ionospheric delay parameters; locating the receiver according to the variance of the ionospheric pseudo-observations in an ionospheric weighted model to obtain a location result; wherein the ionospheric weighted model is used to represent the method of allocating constraint weights of the parameters to be estimated, and the location result is used to represent the optimal estimate of the parameters to be estimated.

[0007] The technical solution provided in this application brings at least the following beneficial effects: It generates an ionospheric composite index using dual-frequency observations, and then maps this index to the variance of ionospheric pseudo-observations. This allows for high-precision positioning within the ionospheric weighted model based on the variance of the ionospheric pseudo-observations, resulting in a positioning result. In other words, this application constructs an ionospheric composite index using the user terminal's own observation data, achieving real-time, autonomous quantitative assessment of ionospheric activity without relying on an external ionospheric monitoring network. Furthermore, it maps the ionospheric composite index to constraint weights for ionospheric parameters, forming an adaptive adjustment mechanism with weak constraints in disturbed scenarios and strong constraints in calm scenarios. Ultimately, this suppresses risks such as solution deviations and mis-fixing of ambiguities during ionospheric disturbances, and ensures positioning convergence speed and accuracy during calm ionospheric conditions. This solves the technical problem of low positioning accuracy and achieves the technical effect of improving positioning accuracy.

[0008] One possible implementation involves generating an ionospheric composite index based on dual-frequency observations, including: calculating ionospheric disturbance characteristics based on dual-frequency observations, wherein the ionospheric disturbance characteristics are used to represent the degree of interference of different characteristic components of ionospheric disturbances to satellite signals; and generating an ionospheric composite index based on the ionospheric disturbance characteristics.

[0009] Another possible implementation involves ionospheric disturbance characteristics that include at least an amplitude scintillation index and a total electron content change rate index. Based on dual-frequency observations, the ionospheric disturbance characteristics are calculated, including: calculating the signal-to-noise ratio (SNR) density and total electron content based on the dual-frequency observations, where the SNR density represents the quality of the satellite signal received by the receiver, and the total electron content represents the total number of free electrons in the satellite signal's propagation path through the ionosphere; generating the amplitude scintillation index based on the SNR density; and generating the total electron content change rate index based on the total electron content. The amplitude scintillation index represents the amplitude scintillation of the satellite signal caused by the ionosphere, and the total electron content change rate index represents the phase path disturbance of the satellite signal caused by the ionosphere.

[0010] Another possible approach is to generate a comprehensive ionospheric index based on ionospheric perturbation characteristics, which includes fusing the amplitude scintillation index and the total electron content change rate index to obtain the comprehensive ionospheric index.

[0011] Another possible implementation involves calculating the signal-to-noise ratio density based on dual-frequency observations, including: extracting the carrier-to-noise ratio from the dual-frequency observations, where the carrier-to-noise ratio represents the relative strength of the useful carrier power to the noise power in the satellite signal; and converting the carrier-to-noise ratio to obtain the signal-to-noise ratio density.

[0012] Another possible implementation involves generating an amplitude flicker index based on the signal-to-noise ratio density, including: smoothing and detrending the signal-to-noise ratio density to obtain a detrended signal strength, wherein the detrended signal strength is used to represent the signal obtained after filtering out the slowly varying background in the satellite signal and retaining the high-frequency flicker components; and determining the amplitude flicker index based on the mean and second moment of the detrended signal strength.

[0013] Another possible implementation involves calculating the total electron content based on dual-frequency observations, including: extracting pseudorange observations corresponding to the first frequency and the second frequency from the dual-frequency observations; determining the combined observation as the difference between the pseudorange observations corresponding to the first frequency and the pseudorange observations corresponding to the second frequency; and determining the total electron content based at least on the combined observations, satellite errors, and receiver errors.

[0014] Another possible implementation involves mapping the ionospheric composite index to the variance of ionospheric pseudo-observations, including: determining the difference between a first configuration parameter and a second configuration parameter, wherein the first configuration parameter is greater than the second configuration parameter; determining the product between the difference and the ionospheric composite index; and adding the product to the second configuration parameter to obtain the variance of the ionospheric pseudo-observations.

[0015] Secondly, this application provides a receiver location positioning device, comprising: an acquisition module for acquiring dual-frequency observations generated by the receiver in response to satellite signals, wherein the dual-frequency observations include pseudorange observations and carrier phase observations measured at a first frequency and a second frequency, respectively; a generation module for generating an ionospheric composite index based on the dual-frequency observations, wherein the ionospheric composite index represents the overall interference level of ionospheric disturbances on satellite signals; a mapping module for mapping the ionospheric composite index to the variance of ionospheric pseudo-observations, wherein the variance of the ionospheric pseudo-observations represents the constraint weights of parameters to be estimated, the parameters to be estimated including at least the receiver's position parameters and ionospheric delay parameters; and a positioning module for positioning the receiver according to the variance of the ionospheric pseudo-observations in an ionospheric weighted model to obtain a positioning result; wherein the ionospheric weighted model represents the method of allocating constraint weights to the parameters to be estimated, and the positioning result represents the optimal estimate of the parameters to be estimated.

[0016] Thirdly, this application provides an electronic device comprising: a processor and a memory; the memory storing processor-executable instructions; when the processor is configured to execute the instructions, causing the electronic device to implement the method of the first aspect described above.

[0017] Fourthly, this application provides a computer-readable storage medium comprising: computer software instructions; which, when executed in an electronic device, cause the electronic device to implement the method described in the first aspect.

[0018] Fifthly, this application provides a computer program product comprising a computer program; when the computer program is run in an electronic device, it causes the electronic device to implement the method described in the first aspect.

[0019] The beneficial effects of the second to fifth aspects mentioned above are described in the corresponding description of the first aspect and will not be repeated here. Attached Figure Description

[0020] Figure 1 A flowchart illustrating a receiver location positioning method provided in this application; Figure 2 A flowchart illustrating an adaptive localization method for ionospheric parameters driven by the comprehensive ionospheric index provided in this application; Figure 3 This is a schematic diagram illustrating the positioning effect of a conventional method. Figure 4 A schematic diagram illustrating the positioning effect of the positioning method provided in this application; Figure 5 A schematic diagram illustrating the composition of a receiver position positioning device provided in this application; Figure 6 This is a schematic diagram of the composition of an electronic device provided in this application. Detailed Implementation

[0021] The following is a detailed description of a call detail record (CDR) data recording method provided in this application, with reference to the accompanying drawings.

[0022] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0023] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0024] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0025] It should be noted that in the embodiments of this application, the words "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplarily" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0026] To facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish the same or similar items with essentially the same function and effect. Those skilled in the art can understand that the terms "first" and "second" are not intended to limit the quantity or execution order.

[0027] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0028] Currently, the advantage of high-precision positioning services lies in their ability to provide unified high-precision positioning capabilities over a wide area, eliminating the need for end-users to build their own reference stations. Furthermore, they can meet the real-time and accuracy requirements of various applications, including surveying, engineering layout, mechanical control, and vehicle and drone navigation. To further improve positioning performance, the user end typically combines correction information provided by the network side to complete the positioning calculation, thereby improving convergence efficiency and positioning accuracy.

[0029] However, existing NRTK or PPP-RTK technologies have the following shortcomings in handling ionospheric errors: Ionospheric constraint patterns are rigid, employing a single mode of network-side modeling and fixed constraints at the user end, which cannot match the dynamic characteristics of ionospheric changes. In ionospheric disturbance scenarios, strong constraints easily introduce model bias, and in calm scenarios, there is a lack of flexibility to further optimize constraint strength; Ionospheric activity information acquisition relies on external support, and the user end cannot independently perceive the ionospheric state, relying entirely on activity indicators broadcast by the network side or dedicated monitoring systems, while the supply of such refined information is severely insufficient under existing service models; Solving robustness and positioning performance are difficult to balance. Fixed constraint modes easily lead to problems such as solution bias and mis-fixed ambiguity in disturbed scenarios, while blindly weakening constraints sacrifices convergence speed and accuracy in calm scenarios, failing to achieve optimal performance across all scenarios.

[0030] Based on this, this application generates an ionospheric composite index using dual-frequency observations, and then maps this index to the variance of ionospheric pseudo-observations. This allows for high-precision positioning within the ionospheric weighted model based on the variance of the pseudo-observations, yielding the positioning result. In other words, this application constructs an ionospheric composite index using the user terminal's own observation data, achieving real-time, autonomous quantitative assessment of ionospheric activity without relying on external ionospheric monitoring networks. Furthermore, the ionospheric composite index is mapped to constraint weights for ionospheric parameters, forming an adaptive adjustment mechanism with weak constraints in disturbed scenarios and strong constraints in calm scenarios. Ultimately, this suppresses risks such as solution deviations and mis-fixing of ambiguities during ionospheric disturbances, and ensures positioning convergence speed and accuracy during calm ionospheric conditions, thus solving the technical problem of low positioning accuracy and achieving the technical effect of improving positioning accuracy.

[0031] The embodiments provided in this application will now be described in detail with reference to the accompanying drawings. See also... Figure 1 This is a flowchart illustrating a receiver location positioning method provided in an embodiment of this application. Figure 1 As shown, the receiver location positioning method provided in this application specifically includes the following steps S101~S104: S101. Obtain the dual-frequency observation values ​​generated by the receiver in response to the satellite signal.

[0032] The dual-frequency observations include pseudorange observations and carrier phase observations measured at the first and second frequencies, respectively. The first frequency can be used... To represent, the second frequency can be used In this context, the first and second frequencies typically need to have a sufficiently large frequency gap to effectively eliminate ionospheric errors, while also considering hardware implementation and system compatibility. Pseudorange observations can be distance estimates obtained by the receiver measuring the propagation time of the ranging code transmitted by the navigation satellite from the satellite to the receiver and multiplying it by the speed of light. Carrier phase observations can be high-precision distance measurements obtained by the receiver tracking the phase changes of the satellite signal carrier. Satellite signals can be the radio signals transmitted by the navigation satellite to the ground.

[0033] For example, dual-frequency observations can be the raw measurement data output after baseband signal processing, obtained by a receiver simultaneously tracking satellite signals transmitted by the same navigation satellite on two different frequencies. These dual-frequency observations can include at least the pseudorange and carrier phase for each frequency.

[0034] S102. Generate a comprehensive ionospheric index based on dual-frequency observations.

[0035] The ionospheric composite index is used to represent the overall interference level of ionospheric disturbances on satellite signals, and can be represented by D.

[0036] For example, ionospheric disturbance characteristics can be calculated based on the user's own observation data (i.e., dual-frequency observations), and then an ionospheric comprehensive index can be constructed based on these ionospheric disturbance characteristics, thereby realizing real-time, autonomous quantitative assessment of ionospheric activity.

[0037] In some embodiments, generating an ionospheric composite index based on dual-frequency observations includes: calculating ionospheric disturbance characteristics based on dual-frequency observations; and generating an ionospheric composite index based on the ionospheric disturbance characteristics.

[0038] Among them, ionospheric disturbance features are used to represent the degree of interference of different characteristic components of ionospheric disturbances to satellite signals. Ionospheric disturbance features can include at least the amplitude scintillation index (which can be represented by S4C) and the rate of change of total electron content (ROTI).

[0039] The amplitude scintillation index S4C can be used to represent the amplitude scintillation of satellite signals caused by the ionosphere, and can also be called the amplitude scintillation index.

[0040] The Rate of Change in Total Electron Content (ROTI) index can be used to represent phase path disturbances in satellite signals caused by the ionosphere.

[0041] For example, the signal-to-noise ratio density and total electron content (TEC) can be calculated first using dual-frequency observations. Then, the S4C can be calculated based on the signal-to-noise ratio density, and the ROTI can be calculated based on the TEC. The ionospheric perturbation characteristics obtained by the above calculations can quickly characterize the perturbation enhancement caused by ionospheric scintillation and irregularities.

[0042] This application constructs a comprehensive ionospheric index based on ionospheric disturbance characteristics, enabling localized and real-time perception of ionospheric disturbances without relying on external ionospheric monitoring networks, using only conventional terminal observation data, thereby supporting real-time high-precision positioning.

[0043] In some embodiments, an ionospheric composite index is generated based on ionospheric disturbance characteristics, including fusing the amplitude scintillation index and the total electron content change rate index to obtain the ionospheric composite index.

[0044] For example, after generating the amplitude scintillation index based on the signal-to-noise ratio density and the total electron content change rate index based on the total electron content, the total electron content change rate index can be standardized first, and then the standardized total electron content change rate index can be fused with the amplitude scintillation index to obtain the ionospheric comprehensive index.

[0045] For example, to facilitate the fusion of S4C and ROTI, ROTI can first be linearly normalized to the range of 0 to 1 (values ​​outside this range can be treated as 0 or 1). ROTI can be standardized using the following formula:

[0046] in, The value p can be used to represent the normalized result of ROTI, and p can be used to represent the current ROTI value. and It can be used to represent preset parameters, for example, You can take 0.5. You can choose 2.

[0047] The S4C and ROTI indices are further fused using the following formula to generate a comprehensive ionospheric index:

[0048] in, It can be used to represent the S4C index, and D can be used to represent the ionospheric composite index.

[0049] This application standardizes the ROTI and integrates it with S4C to generate a comprehensive ionospheric index. This enables multi-dimensional, highly sensitive, and adaptive quantitative assessment of ionospheric disturbances, effectively taking into account two typical ionospheric anomaly characteristics: drastic phase changes (reflected by ROTI) and signal amplitude flicker (reflected by S4C). Thus, without the need for an external monitoring network, real-time perception of local ionospheric disturbances can be achieved solely through the terminal's own dual-frequency observation data, significantly improving the robustness and reliability of high-precision positioning and navigation in complex ionospheric environments.

[0050] In some embodiments, the ionospheric disturbance characteristics are calculated based on dual-frequency observations, including: calculating the signal-to-noise ratio density and total electron content based on the dual-frequency observations; generating an amplitude scintillation index based on the signal-to-noise ratio density; and generating a total electron content change rate index based on the total electron content.

[0051] The signal-to-noise ratio (SNR) density is used to represent the quality of the satellite signal received by the receiver; it can also be called the linear signal-to-noise ratio, which can be expressed as... The Total Electron Content (TEC) is used to represent the total number of free electrons in the path of a satellite signal as it travels through the ionosphere.

[0052] For example, the carrier-to-noise ratio (CNR) of each satellite can be extracted from dual-frequency observations, and then converted into a signal-to-noise ratio (SNR) density. To eliminate slowly varying path fading and antenna gain changes, the SNR density is further smoothed and detrended, and the S4C can be calculated based on the processing result.

[0053] For example, pseudorange observations corresponding to the first frequency and the second frequency can be extracted from the dual-frequency observations. These pseudorange observations are then combined to obtain combined observations. The TEC can then be calculated based on these combined observations.

[0054] For example, after calculating the total electron content TEC, ROTI can be defined using the following formula:

[0055] in, It can be used to represent the TEC of the k-th epoch. It can be used to represent the time interval between adjacent epochs. The horizontal line in the above formula can be used to represent the average value within a sliding time window of length n (for example, a 5-minute time window corresponds to n epochs).

[0056] The terminal in this application extracts parameters such as signal-to-noise ratio and total electron content (TEC) from dual-frequency observations, and further calculates the ionospheric scintillation index (S4C) and ROTI. This enables real-time, autonomous, and highly sensitive monitoring and quantification of local ionospheric disturbances without relying on external monitoring networks, significantly improving the terminal's positioning assessment and adaptive anti-interference performance during periods of ionospheric activity.

[0057] In some embodiments, calculating the signal-to-noise ratio density based on dual-frequency observations includes: extracting the carrier-to-noise ratio from the dual-frequency observations; and converting the carrier-to-noise ratio to obtain the signal-to-noise ratio density.

[0058] The carrier-to-noise ratio (CNR) is used to represent the relative strength of the useful carrier power to the noise power in a satellite signal. It can be expressed as: It is expressed in decibels-hertz (dB·Hz).

[0059] For example, the carrier-to-noise ratio of each satellite is extracted from dual-frequency observations. The carrier-to-noise ratio in dB·Hz can then be expressed using the following formula. Converted to linear signal-to-noise ratio :

[0060] Where k can be used to represent the epoch number. It can be used to represent the signal-to-noise ratio density at the k-th epoch. It can be used to represent the carrier-to-noise ratio density at the k-th epoch. It can be used to represent noise power spectral density. If the noise power spectral density is measured over a short period of approximately 1 minute... If approximately constant, then the signal strength It is directly proportional. Therefore, it can be directly... As the input sequence for subsequent detrending signal strength, for Smoothing and detrending processes are performed to obtain the detrended signal strength. The S4C can then be calculated based on this detrended signal strength.

[0061] This application converts the carrier-to-noise ratio in dB·Hz form into a linear signal-to-noise ratio, enabling a dimensionless and calculable characterization of signal quality. This facilitates subsequent precise mathematical calculations and fusion processing, thereby improving the receiver's robustness and positioning accuracy in dynamic or interference environments.

[0062] In some embodiments, generating an amplitude flicker index based on the signal-to-noise ratio density includes: smoothing and detrending the signal-to-noise ratio density to obtain a detrended signal strength; and determining the amplitude flicker index based on the mean and second moment of the detrended signal strength.

[0063] The detrended signal strength is used to represent the signal obtained after filtering out the slowly varying background in the satellite signal and retaining the high-frequency scintillation component. It can be represented by... This can be represented as follows. The mean of the detrended signal strength can be expressed as... The second moment of the trend signal strength can be represented as... To express.

[0064] For example, to eliminate slowly varying path fading and antenna gain changes, the signal strength sequence needs to be smoothed and detrended. Assuming a sampling interval of 1 second, within a sliding time window of length n (where n=60, corresponding to 1 minute), the detrended signal strength for each epoch k can be calculated using the following formula:

[0065] in, This can be used to represent the strength of the detrending signal. The formula above is used to represent the current epoch's... Normalized to the first minute The time average value is used to retain high-frequency flicker components and filter out slowly changing background.

[0066] Furthermore, within the same time window of length n, the detrending signal strength can be calculated using the following formula. Calculate the mean and second moment:

[0067] in, It can be used to represent the mean of the detrending signal strength. The second moment, which can be used to represent the strength of the detrended signal, is based on... The amplitude scintillation index S4C can be defined by the following formula:

[0068] in, It can be used to represent the amplitude scintillation index S4C.

[0069] It should be noted that the above implementation uses 60 seconds as a statistical unit, outputting an S4C value for each satellite. Further spatial distribution mapping based on puncture point locations allows for the formation of a continuous S4C index time series.

[0070] This application performs smoothing and detrending processing on the signal strength sequence to eliminate slowly varying path fading and antenna gain changes. Then, based on the mean and second moment of the detrended signal strength, the amplitude scintillation index S4C is calculated. This can effectively separate the effects of rapid amplitude scintillation caused by the ionosphere from slow environmental changes, thereby significantly improving the sensitivity, accuracy, and stability of S4C to ionospheric disturbances (especially equatorial or high-latitude scintillation). This provides key support for terminals to autonomously and in real-time monitor ionospheric anomalies, assess signal quality, and ensure the reliability of high-precision positioning.

[0071] In some embodiments, calculating the total electron content based on dual-frequency observations includes: extracting pseudorange observations corresponding to a first frequency and pseudorange observations corresponding to a second frequency from the dual-frequency observations; determining the difference between the pseudorange observations corresponding to the first frequency and the pseudorange observations corresponding to the second frequency as a combined observation; and determining the total electron content based at least on the combined observation, satellite errors, and receiver errors.

[0072] Among them, the pseudorange observation value corresponding to the first frequency can be used To represent this, the pseudorange observation value corresponding to the second frequency can be used... To represent, combined observations can be used This is represented. Satellite errors can be deviations generated by the navigation satellite itself, including satellite clock errors, orbital (ephemeris) errors, and hardware delays at the signal transmitter. This can be represented as follows. Receiver error can be the observation bias caused by the receiver and its local environment, and can include receiver clock bias, hardware delay, multipath effects, measurement noise, and antenna phase center deviation, etc. It can be expressed as... To express.

[0073] For example, pseudorange observations corresponding to the first frequency can be extracted from dual-frequency observations. pseudorange observations corresponding to the second frequency Furthermore, the pseudorange observations corresponding to the first frequency... pseudorange observations corresponding to the second frequency By performing subtraction, we obtain the combined observations. . That is, Further based on combined observations Satellite error and receiver error TEC is calculated.

[0074] For example, the propagation paths of dual-frequency satellite signals between the satellite and the receiver can be considered identical, including errors such as clock bias, tropospheric distortion, and multipath propagation, which can be directly canceled out. Furthermore, based on the ionospheric dispersion characteristics, TEC can be defined using the following formula:

[0075] in, It can be used to represent the error of a satellite. This can be used to represent receiver errors, which are eliminated by subtracting between epochs. Superscript It can be used to represent satellites, subscripts It can be used to represent a receiver. It can be used to represent the pseudorange observation value corresponding to the first frequency. It can be used to represent the pseudorange observation value corresponding to the second frequency. It can be used to represent combined observations, and can also be called dual-frequency pseudorange ionospheric observations.

[0076] This application extracts pseudoranges of two frequencies from dual-frequency observations and constructs their difference as a combined observation. Combined with modeling or correction of satellite and receiver errors, it enables real-time, autonomous, and high temporal resolution inversion of ionospheric TEC relying solely on dual-frequency observation data. No external ionospheric model or monitoring network support is required, thereby providing terminals with local ionospheric state awareness capabilities and effectively supporting applications such as ionospheric delay correction in high-precision positioning.

[0077] S103. Map the ionospheric composite index to the variance of ionospheric pseudo-observations.

[0078] In this context, the variance of ionospheric pseudo-observations is used to represent the constraint weights of the parameter to be estimated, which can be expressed as follows: The parameters to be estimated include at least the receiver position parameters and the ionospheric delay parameters, and can also be referred to as the position parameters to be estimated (which can be represented by...). (to be represented) and the ionospheric delay parameters to be estimated (which can be represented by...) (To be expressed).

[0079] For example, after constructing the comprehensive ionospheric index, configurable parameters can be used to map the comprehensive ionospheric index to the variance of ionospheric pseudo-observations. These configurable parameters can include at least a first configuration parameter and a second configuration parameter.

[0080] In some embodiments, mapping the ionospheric composite index to the variance of ionospheric pseudo-observations includes: determining the difference between a first configuration parameter and a second configuration parameter; determining the product between the difference and the ionospheric composite index; and adding the product to the second configuration parameter to obtain the variance of the ionospheric pseudo-observations.

[0081] Wherein, the first configuration parameter is greater than the second configuration parameter, and the first configuration parameter can be used. To express, for example, The second configuration parameter can be used To express, for example, .

[0082] For example, the ionospheric composite index can be mapped to the variance of ionospheric pseudo-observations using the following formula;

[0083] in, It can be used to represent the variance of ionospheric pseudo-observations. and It can be used to represent configurable parameters, generally , It is used to control the strength and variation curve of constraints during calm and active periods.

[0084] This application maps the ionospheric composite index to the variance of ionospheric pseudo-observations, enabling adaptive adjustment of observation weights based on real-time ionospheric disturbance intensity. This dynamically reflects the uncertainty of ionospheric errors in positioning calculations, thereby improving the robustness, convergence speed, and positioning reliability of high-precision navigation systems under active ionospheric conditions, and enhancing adaptive suppression of ionospheric anomalies.

[0085] S104. In the ionospheric weighted model, the receiver is located according to the variance of the ionospheric pseudo-observations to obtain the location result.

[0086] The ionospheric weighted model represents the method of allocating constraint weights to the parameters to be estimated. The positioning result can be used to represent the optimal estimate of the parameters to be estimated. This positioning result is the weighted optimal solution obtained by adaptively adjusting the prior variance of the ionospheric after fusing the real-time ionospheric disturbance information perceived by the user terminal (using the ionospheric comprehensive index constructed through S4C, ROTI, etc.). Statistically, it represents the minimum variance unbiased estimate (or maximum a posteriori estimate) of the joint estimation of the position parameter and the ionospheric delay parameter under the current observation conditions. That is, it is the best approximation of the true state while taking into account both observation accuracy and the credibility of the ionospheric environment.

[0087] For example, an ionospheric constraint equation is constructed, and the location solution is completed in the ionospheric weighted model based on the variance of the ionospheric pseudo-observations.

[0088] For example, the following ionospheric weighted model can be constructed:

[0089] in, It can be used to represent the first Frequency double-difference carrier phase observations, It can be used to represent the first Frequency double-difference pseudorange observations, It can be used to represent the first Wavelength of frequency It can be used to represent the first Frequency ambiguity, It can be used to represent the first The ionospheric amplification factor of the frequency, It can be used to represent the direction cosine coefficient matrix. It can be used to represent the location parameter to be estimated. It can be used to represent ionospheric pseudo-observations. , The parameter to be estimated is the ionospheric delay. The variance of the ionospheric pseudo-observation measurement noise is taken as . Subsequently, RTK or PPP-RTK localization was performed based on the aforementioned ionospheric weighted model to obtain the localization results.

[0090] This application dynamically adjusts the prior constraint strength based on the variance of ionospheric pseudo-observations in the ionospheric weighted model, which can synchronously and robustly estimate the user's location and ionospheric delay parameters in the positioning solution, effectively balancing observation information and environmental priors, and significantly improving the convergence speed, accuracy stability and anti-interference capability of high-precision positioning under ionospheric disturbance conditions.

[0091] The technical solutions provided by the above embodiments bring at least the following beneficial effects. The receiver location positioning method provided in this application generates an ionospheric comprehensive index through dual-frequency observations, and then maps the ionospheric comprehensive index to the variance of ionospheric pseudo-observations. This allows for high-precision positioning in the ionospheric weighted model based on the variance of the ionospheric pseudo-observations, resulting in a positioning result. In other words, this application constructs an ionospheric comprehensive index using the user terminal's own observation data, achieving real-time, autonomous quantitative assessment of ionospheric activity without relying on an external ionospheric monitoring network. Furthermore, the ionospheric comprehensive index is mapped to constraint weights for ionospheric parameters, forming an adaptive adjustment mechanism with weak constraints in disturbed scenarios and strong constraints in calm scenarios. Ultimately, this suppresses risks such as solution deviations and ambiguity misfixation during ionospheric disturbances, and ensures positioning convergence speed and accuracy during calm ionospheric conditions, thereby solving the technical problem of low positioning accuracy and achieving the technical effect of improving positioning accuracy.

[0092] The method described above in this application embodiment is illustrated below with a specific example. The specific implementation process of this method is as follows: Figure 2 As shown. Figure 2 This application provides a flowchart illustrating an adaptive ionospheric parameter localization method driven by the comprehensive ionospheric index. The flowchart of this adaptive ionospheric parameter localization method may include the following steps: S201, acquire dual-frequency observations.

[0093] S202 extracts the signal-to-noise ratio and TEC from dual-frequency observations.

[0094] For example, the terminal can extract metrics such as signal-to-noise ratio (SNR) and TEC from dual-frequency observations. Specifically, the carrier-to-noise ratio (CNR) of each satellite... Furthermore, observational data with an elevation angle above 30 degrees were used for scintillation analysis to suppress low elevation angle effects such as multipath propagation. The carrier-to-noise ratio in dB·Hz form was converted to a linear signal-to-noise ratio using the following formula:

[0095] Where k can be used to represent the epoch number. It can be used to represent the signal-to-noise ratio density at the k-th epoch. It can be used to represent the carrier-to-noise ratio density at the k-th epoch. It can be used to represent noise power spectral density. If the noise power spectral density is measured over a short period of approximately 1 minute... If approximately constant, then the signal strength It is directly proportional. Therefore, it can be directly... This serves as the input sequence for the subsequent detrending signal strength.

[0096] For example, the propagation paths of dual-frequency signals between the satellite and the receiver can be considered identical, including errors such as clock bias, tropospheric distortion, and multipath propagation, which can be directly canceled out. Furthermore, based on the ionospheric dispersion characteristics, TEC can be defined using the following formula:

[0097] in, It can be used to represent the error of a satellite. This can be used to represent receiver errors, which are eliminated by subtracting between epochs. Superscript It can be used to represent satellites, subscripts It can be used to represent a receiver. It can be used to represent the first frequency. It can be used to represent the second frequency. It can be used to represent the pseudorange observation value corresponding to the first frequency. It can be used to represent the pseudorange observation value corresponding to the second frequency. It can be used to represent combined observations, and can also be called dual-frequency pseudorange ionospheric observations.

[0098] S203, based on signal-to-noise ratio, calculates S4C, and based on TEC, calculates ROTI.

[0099] For example, the terminal extracts indicators such as signal-to-noise ratio (SNR) and TEC from dual-frequency observations and calculates indicators such as S4C and ROTI. Specifically, to eliminate slowly varying path fading and antenna gain changes, the signal strength sequence needs to be smoothed and detrended. Assuming a sampling interval of 1 second, within a sliding time window of length n (where n=60, corresponding to 1 minute), the detrended signal strength is calculated for each epoch k using the following formula:

[0100] in, This can be used to represent the strength of the detrending signal. The formula above is used to represent the current epoch's... Normalized to the first minute The high-frequency flicker components are preserved and the slowly varying background is filtered out by averaging the time values. Within the same time window of length n, the detrended signal strength is calculated using the following formula. Calculate the mean and second moment:

[0101] in, It can be used to represent the mean of the detrending signal strength. The second moment, which can be used to represent the strength of the detrended signal, is based on... The amplitude scintillation index S4C can be defined by the following formula:

[0102] in, It can be used to represent the amplitude scintillation index S4C. In the implementation, a statistical unit of 60 seconds is used to output an S4C value for each satellite. Subsequently, it is spatially distributed and mapped according to the puncture point location, and a continuous time series of S4C indexes can be formed in time.

[0103] For example, after calculating TEC, ROTI can be defined using the following formula:

[0104] in, It can be used to represent the TEC of the k-th epoch. It can be used to represent the time interval between adjacent epochs. The horizontal line can be used to represent the average value calculated over a sliding time window of length n (e.g., a 5-minute time window, corresponding to n epochs).

[0105] S204 is a fusion of S4C and ROTI to generate the comprehensive ionospheric index.

[0106] For example, the S4C and ROTI indices are standardized and fused to generate a comprehensive ionospheric index. To facilitate the fusion of S4C and ROTI, ROTI can be linearly normalized to the range of 0 to 1 (values ​​outside this range can be treated as 0 or 1) before constructing the comprehensive ionospheric index. ROTI can be standardized using the following formula:

[0107] in, and It can be used to represent preset parameters, for example, You can take 0.5. A value of 2 can be chosen. Further, the S4C and ROTI indices are fused using the following formula to generate a comprehensive ionospheric index:

[0108] in, It can be used to represent the S4C metric. It can be used to represent the standardized results of ROTI, and D can be used to represent the comprehensive ionospheric index.

[0109] S205 maps the ionospheric composite index to the variance of ionospheric pseudo-observations.

[0110] For example, the composite index can be mapped to the variance of ionospheric pseudo-observations using the following formula;

[0111] in, and It can be used to represent configurable parameters, generally , It is used to control the strength and variation curve of constraints during calm and active periods. It can be used to represent the variance of ionospheric pseudo-observations.

[0112] S206, construct the ionospheric constraint equation, and complete the location solution based on the variance of the ionospheric pseudo-observations in the ionospheric weighted model.

[0113] For example, the following ionospheric weighted model can be constructed:

[0114] in, It can be used to represent the first Frequency double-difference carrier phase observations, It can be used to represent the first Frequency double-difference pseudorange observations, It can be used to represent the first Wavelength of frequency It can be used to represent the first Frequency ambiguity, It can be used to represent the first The ionospheric amplification factor of the frequency, It can be used to represent the direction cosine coefficient matrix. It can be used to represent the location parameter to be estimated. It can be used to represent ionospheric pseudo-observations. , The parameter to be estimated is the ionospheric delay. The variance of the ionospheric pseudo-observation measurement noise is taken as . Subsequently, RTK or PPP-RTK localization is performed based on the aforementioned ionospheric weighted model.

[0115] The ionospheric parameter adaptive positioning method driven by the aforementioned ionospheric composite index in this application relates to the field of high-precision positioning and navigation technology for Global Navigation Satellite System (GNSS), and is geared towards high-precision positioning user terminals such as network RTK or PPP-RTK. It constructs an ionospheric composite index by calculating ionospheric disturbance indices such as S4C and ROTI using user terminal observations. The ionospheric composite index is mapped to the variance of ionospheric pseudo-observations (or prior ionospheric parameters), thereby adaptively adjusting the ionospheric constraint strength in the ionospheric weighted model. In scenarios with enhanced ionospheric activity or scintillation disturbances, this application can suppress solution bias and ambiguity misfixation caused by excessively strong ionospheric constraints, while maintaining strong constraints in calm scenarios to improve convergence speed and accuracy, thereby enhancing the reliability, continuity, and availability of high-precision positioning.

[0116] This application overcomes the dual limitations of relying on external information and fixed constraint patterns in related technologies, providing a user-terminal-driven adaptive positioning scheme for ionospheric parameters. It extracts ionospheric disturbance features from user-terminal observation data and constructs a comprehensive ionospheric index, enabling real-time, autonomous quantitative assessment of ionospheric activity. Based on this comprehensive ionospheric index, it dynamically maps the constraint weights of ionospheric parameters, forming an adaptive adjustment mechanism with weak constraints in disturbed scenarios and strong constraints in calm scenarios. Ultimately, it suppresses the risk of solution deviation and ambiguity misfixation during ionospheric disturbances, ensures positioning convergence speed and accuracy during calm ionospheric conditions, and reduces reliance on additional network-side information services, comprehensively improving the reliability, continuity, and all-scenario availability of high-precision positioning.

[0117] For example, Figure 3 This is a schematic diagram illustrating the positioning effect of a conventional method. Figure 4 A schematic diagram illustrating the positioning effect of the positioning method provided in this application, as shown below. Figure 3 and Figure 4As shown, the horizontal axis represents time, and the vertical axis represents the east-west, north-south, and up-down directions, respectively, with units in meters (m). AVE can be the average error, STD can be the standard deviation, and RMS can be the root mean square error.

[0118] Figure 3 The coordinates ORI = 32.057303170°N, 118.785185530°E, and 2.8555m can be used to represent the location of the origin or reference point (ORI), with geographic coordinates of 32.057303170°N, 118.785185530°E, and elevation of 2.8555m. Figure 4 The coordinates ORI = 32.057303138°N, 118.785185578°E, and 2.8659m can be used to represent the location of ORI. Its geographical coordinates are: 32.057303138 degrees north latitude, 118.785185578 degrees east longitude, and 2.8659 meters elevation.

[0119] from Figure 3 and Figure 4 As can be seen, in the three directions of EW(m), NS(m), and UD(m), the average error AVE of the positioning method provided in this application is equal to the average error AVE of the conventional method, i.e., AVE = 0.0000m. However, in all three directions, the standard deviation (STD) and root mean square error (RMS) of the positioning method provided in this application are smaller than those of the conventional method. In other words, the positioning effect of the positioning method provided in this application is superior to that of the conventional method.

[0120] Specifically, in the EW(m) direction, the standard deviation (STD) of the conventional method is 0.0289m, which is greater than the standard deviation (STD) of the positioning method provided in this application (STD = 0.0050m); the root mean square error (RMS) of the conventional method is also 0.0289m, which is greater than the root mean square error (RMS) of the positioning method provided in this application (RMS = 0.0050m). In the NS(m) direction, the STD of the conventional method is 0.0367m, which is greater than the STD of the positioning method provided in this application (STD = 0.0059m); the RMS of the conventional method is also 0.0367m, which is greater than the RMS of the positioning method provided in this application (RMS = 0.0058m). In the UD(m) direction, the STD of the conventional method is 0.0575m, which is greater than the STD of the positioning method provided in this application (STD = 0.0201m); the RMS of the conventional method is also 0.0575m, which is greater than the RMS of the positioning method provided in this application (RMS = 0.0201m).

[0121] In summary, addressing the lack of a unified and real-time source of ionospheric activity information for terminals in active ionospheric scenarios in related technologies, this application proposes a technical solution where the terminal directly extracts and uses its own observation data to drive adaptive processing. Compared with ionospheric constraint methods in related technologies that use fixed variance or empirical weighting based solely on elevation angle or baseline length, this application offers the following advantages: perturbation is perceptible, with the user terminal extracting indicators such as S4C and ROTI in real time, enabling rapid characterization of perturbation enhancement caused by ionospheric scintillation and irregularities; constraints are adaptive, automatically increasing the ionospheric pseudo-observation variance to weaken constraints and suppress bias and incorrect fixation when perturbation intensifies; and automatically decreasing variance to strengthen constraints during calm periods, improving convergence speed and positioning accuracy; deployment costs are low, requiring no external ionospheric monitoring network and utilizing only the terminal's conventional observations; and compatibility is strong, applicable to various high-precision positioning systems such as NRTK and PPP-RTK, supporting multi-system and multi-frequency observations.

[0122] As can be seen, the above mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the embodiments of this application provide corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the modules and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0123] In some embodiments, this application also provides a receiver location positioning device. This receiver location positioning device may include one or more functional modules for implementing the receiver location positioning method of the above method embodiments.

[0124] For example, Figure 5 This is a schematic diagram illustrating the composition of a receiver location positioning device provided in an embodiment of this application. Figure 5 As shown, the positioning device 500 for the receiver position includes: The acquisition module 501 is used to acquire dual-frequency observations generated by the receiver in response to satellite signals. The dual-frequency observations include pseudorange observations and carrier phase observations measured at the first frequency and the second frequency, respectively.

[0125] The generation module 502 is used to generate an ionospheric composite index based on dual-frequency observations. The ionospheric composite index is used to represent the overall interference of ionospheric disturbances on satellite signals.

[0126] The mapping module 503 is used to map the ionospheric composite index to the variance of the ionospheric pseudo-observations, wherein the variance of the ionospheric pseudo-observations is used to represent the constraint weights of the parameters to be estimated, which include at least the receiver position parameters and the ionospheric delay parameters.

[0127] The positioning module 504 is used to locate the receiver in the ionospheric weighted model according to the variance of the ionospheric pseudo-observations and obtain the positioning result; wherein, the ionospheric weighted model is used to represent the way of allocating the constraint weights of the parameter to be estimated, and the positioning result is used to represent the optimal estimate of the parameter to be estimated.

[0128] In some embodiments, the generation module 502 includes: a calculation unit for calculating ionospheric disturbance characteristics based on dual-frequency observations, wherein the ionospheric disturbance characteristics are used to represent the degree of interference of different characteristic components of ionospheric disturbances to satellite signals; and a generation unit for generating an ionospheric composite index based on the ionospheric disturbance characteristics.

[0129] In other embodiments, the ionospheric disturbance characteristics include at least an amplitude scintillation index and a total electron content change rate index. The calculation unit includes: a calculation subunit for calculating the signal-to-noise ratio density and total electron content based on dual-frequency observations, wherein the signal-to-noise ratio density represents the quality of the satellite signal received by the receiver, and the total electron content represents the total number of free electrons in the propagation path of the satellite signal through the ionosphere; a first generation subunit for generating the amplitude scintillation index based on the signal-to-noise ratio density; and a second generation subunit for generating the total electron content change rate index based on the total electron content. The amplitude scintillation index represents the amplitude scintillation of the satellite signal caused by the ionosphere, and the total electron content change rate index represents the phase path disturbance of the satellite signal caused by the ionosphere.

[0130] In some other embodiments, the generating unit includes: a fusion subunit that fuses the amplitude scintillation index and the total electron content change rate index to obtain the ionospheric composite index.

[0131] In some other embodiments, the computation subunit is further configured to: extract the carrier-to-noise ratio from dual-frequency observations, wherein the carrier-to-noise ratio represents the relative strength of the useful carrier power to the noise power in the satellite signal; and convert the carrier-to-noise ratio to obtain the signal-to-noise ratio density.

[0132] In some other embodiments, the first generation subunit is further configured to: perform smoothing and detrending processing on the signal-to-noise ratio density to obtain a detrended signal strength, wherein the detrended signal strength is used to represent the signal obtained after filtering out the slowly varying background in the satellite signal and retaining the high-frequency scintillation component; and determine the amplitude scintillation index based on the mean and second moment of the detrended signal strength.

[0133] In some other embodiments, the computation subunit is further configured to: extract pseudorange observations corresponding to a first frequency and pseudorange observations corresponding to a second frequency from the dual-frequency observations; determine the difference between the pseudorange observations corresponding to the first frequency and the pseudorange observations corresponding to the second frequency as a combined observation; and determine the total electron content based at least on the combined observations, the satellite error, and the receiver error.

[0134] In some other embodiments, the mapping module 503 includes: a first determining unit, configured to determine the difference between a first configuration parameter and a second configuration parameter, wherein the first configuration parameter is greater than the second configuration parameter; a second determining unit, configured to determine the product between the difference and the ionospheric composite index; and a processing unit, configured to add the product to the second configuration parameter to obtain the variance of the ionospheric pseudo-observations.

[0135] In the case of implementing the functions of the integrated modules described above in hardware, this embodiment of the invention provides a possible structural schematic diagram of the electronic device involved in the above embodiments. For example... Figure 6 As shown, the electronic device 600 includes: a processor 602, a communication interface 603, and a bus 604. Optionally, the electronic device 600 may also include a memory 601.

[0136] Processor 602 may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 602 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 602 may also be a combination that implements computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0137] Communication interface 603 is used to connect to other devices via a communication network. This communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0138] The memory 601 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0139] In one possible implementation, the memory 601 can exist independently of the processor 602. The memory 601 can be connected to the processor 602 via a bus 604 and is used to store instructions or program code. When the processor 602 calls and executes the instructions or program code stored in the memory 601, it can implement the receiver location positioning method provided in this embodiment of the invention.

[0140] In another possible implementation, the memory 601 can also be integrated with the processor 602.

[0141] Bus 604 can be an extended industry standard architecture (EISA) bus, etc. Bus 604 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0142] Through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the service calling device can be divided into different functional modules to complete all or part of the functions described above.

[0143] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by computer instructions instructing related hardware. The program can be stored in the aforementioned computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be any of the foregoing embodiments or memory. The aforementioned computer-readable storage medium can also be an external storage device of the aforementioned service invocation device, such as a plug-in hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the aforementioned service invocation device. Further, the aforementioned computer-readable storage medium can include both internal storage units of the aforementioned service invocation device and external storage devices. The aforementioned computer-readable storage medium is used to store the aforementioned computer program and other programs and data required by the aforementioned service invocation device. The aforementioned computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0144] This application also provides a computer program product, which includes a computer program that, when run on a computer, causes the computer to execute any of the receiver location positioning methods provided in the above embodiments.

[0145] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for locating the position of a receiver, characterized in that, include: Acquire dual-frequency observations generated by the receiver in response to satellite signals, wherein the dual-frequency observations include pseudorange observations and carrier phase observations measured at a first frequency and a second frequency, respectively; Based on the dual-frequency observations, an ionospheric composite index is generated, wherein the ionospheric composite index is used to represent the overall interference level of ionospheric disturbances on the satellite signal; The ionospheric composite index is mapped to the variance of ionospheric pseudo-observations, wherein the variance of the ionospheric pseudo-observations is used to represent the constraint weights of the parameters to be estimated, and the parameters to be estimated include at least the receiver position parameters and the ionospheric delay parameters. In the ionospheric weighted model, the receiver is located according to the variance of the ionospheric pseudo-observations to obtain a location result; wherein, the ionospheric weighted model is used to represent the way of allocating the constraint weights of the parameter to be estimated, and the location result is used to represent the optimal estimate of the parameter to be estimated.

2. The method according to claim 1, characterized in that, The generation of the ionospheric composite index based on the dual-frequency observations includes: Based on the dual-frequency observations, ionospheric disturbance characteristics are calculated, wherein the ionospheric disturbance characteristics are used to represent the degree of interference of different characteristic components of ionospheric disturbance to the satellite signal; The comprehensive ionospheric index is generated based on the ionospheric perturbation characteristics.

3. The method according to claim 2, characterized in that, The ionospheric disturbance characteristics include at least the amplitude scintillation index and the total electron content change rate index. The calculation of the ionospheric disturbance characteristics based on the dual-frequency observations includes: Based on the dual-frequency observations, the signal-to-noise ratio density and total electron content are calculated, wherein the signal-to-noise ratio density is used to represent the quality of the satellite signal received by the receiver, and the total electron content is used to represent the total number of free electrons in the propagation path of the satellite signal through the ionosphere; The amplitude flicker index is generated based on the signal-to-noise ratio density; Based on the total electron content, the total electron content change rate index is generated; The amplitude scintillation index is used to represent the amplitude scintillation of the satellite signal caused by the ionosphere; the total electron content change rate index is used to represent the phase path disturbance of the satellite signal caused by the ionosphere.

4. The method according to claim 3, characterized in that, The generation of the comprehensive ionospheric index based on the ionospheric perturbation characteristics includes: The ionospheric comprehensive index is obtained by fusing the amplitude scintillation index and the total electron content change rate index.

5. The method according to claim 3, characterized in that, The calculation of the signal-to-noise ratio density based on the dual-frequency observations includes: The carrier-to-noise ratio is extracted from the dual-frequency observations, wherein the carrier-to-noise ratio is used to represent the relative strength of the useful carrier power to the noise power in the satellite signal; The carrier-to-noise ratio is converted to obtain the signal-to-noise ratio density.

6. The method according to claim 3, characterized in that, The step of generating the amplitude flicker index based on the signal-to-noise ratio density includes: The signal-to-noise ratio density is smoothed and detrended to obtain the detrended signal strength, wherein the detrended signal strength is used to represent the signal obtained after filtering out the slowly varying background in the satellite signal and retaining the high-frequency scintillation component; The amplitude flicker index is determined based on the mean and second moment of the detrended signal intensity.

7. The method according to claim 3, characterized in that, The calculation of the total electron content based on the dual-frequency observations includes: Extract the pseudorange observation value corresponding to the first frequency and the pseudorange observation value corresponding to the second frequency from the dual-frequency observation values; The difference between the pseudorange observation value corresponding to the first frequency and the pseudorange observation value corresponding to the second frequency is determined as the combined observation value; The total electron content is determined based at least on the combined observations, satellite errors, and receiver errors.

8. The method according to claim 1, characterized in that, The mapping of the ionospheric composite index to the variance of ionospheric pseudo-observations includes: Determine the difference between the first configuration parameter and the second configuration parameter, wherein the first configuration parameter is greater than the second configuration parameter; Determine the product between the difference and the separation composite index; The product is added to the second configuration parameter to obtain the variance of the ionospheric pseudo-observations.

9. An electronic device, characterized in that, It includes a processor and a memory, the processor being coupled to the memory; the memory is used to store computer instructions, which are loaded and executed by the processor to enable the computer device to implement the receiver location positioning method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes computer-executable instructions that, when executed on a computer, cause the computer to perform the receiver location positioning method according to any one of claims 1 to 8.

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