Parameter determination method and apparatus, electronic device, storage medium, and product

Abstract

This application discloses a parameter determination method, apparatus, electronic device, storage medium, and product. The method includes: constructing a double-difference observation equation indicating inter-receiver and inter-satellite communication; wherein the unknown parameters of the double-difference observation equation indicate ambiguity information and delay information; processing the double-difference observation equation using a preset estimation algorithm and preset elimination coefficients to obtain floating-point ambiguity indicating ionosphere-free conditions and estimated values ​​related to tropospheric delay; wherein the preset elimination coefficients are used to eliminate ionospheric influence; determining the corresponding wide-lane ambiguity and narrow-lane ambiguity based on the floating-point ambiguity; updating the estimated values ​​using the wide-lane ambiguity and narrow-lane ambiguity; and determining the current unknown parameters of the double-difference observation equation using the updated estimated values ​​as constraints. Embodiments of this application can be applied to the fields of mapping and transportation. This application can improve the efficiency of relevant parameter determination and can balance the efficiency and accuracy of ambiguity determination.

Description

Technical Field

[0001] This application relates to the field of Internet communication technology, and in particular to a parameter determination method, apparatus, electronic device, storage medium and product. Background Technology

[0002] With the development of internet communication technology, various internet products have emerged, and the services they provide to users have become increasingly diverse. Location services, or positioning services, are one such service. To ensure the accuracy and real-time performance of location services, Real-Time Kinematic (RTK) technology can be used. When applying RTK technology, the observation data obtained from relevant base stations can be processed to obtain correction values ​​that can be used to improve positioning accuracy. In related technologies, when processing the observation data obtained from relevant base stations, the ambiguity values ​​on the baselines between base stations are obtained through multi-epoch smoothing. However, this method is often time-consuming. Summary of the Invention

[0003] To address the problems of long processing time and low efficiency in existing technologies for ambiguity determination, this application provides a parameter determination method, apparatus, electronic device, storage medium, and product:

[0004] According to a first aspect of this application, a parameter determination method is provided, the method comprising:

[0005] Construct a double-difference observation equation to indicate inter-receiver and inter-satellite observations; wherein the unknown parameters of the double-difference observation equation indicate ambiguity information and delay information;

[0006] The double-difference observation equation is processed using a preset estimation algorithm and a preset elimination coefficient to obtain the floating-point ambiguity indicating the absence of the ionosphere and the estimated value of the tropospheric delay; wherein, the preset elimination coefficient is used to eliminate the influence of the ionosphere;

[0007] The corresponding wide-lane ambiguity and narrow-lane ambiguity are determined based on the floating-point ambiguity;

[0008] The estimated value is updated using the wide alley ambiguity and the narrow alley ambiguity;

[0009] Using the updated estimates as constraints, the current unknown parameters of the double-difference observation equation are determined.

[0010] According to a second aspect of this application, a parameter determining apparatus is provided, the apparatus comprising:

[0011] Equation construction module: used to construct double-difference observation equations between receivers and between satellites; wherein, the unknown parameters of the double-difference observation equations indicate ambiguity information and delay information;

[0012] Equation processing module: used to process the double-difference observation equation using a preset estimation algorithm and preset elimination coefficients to obtain the floating-point ambiguity indicating the absence of ionosphere and the estimated value of the tropospheric delay; wherein, the preset elimination coefficients are used to eliminate the influence of ionosphere;

[0013] Ambiguity determination module: used to determine the corresponding wide-lane ambiguity and narrow-lane ambiguity based on the floating-point ambiguity;

[0014] Estimated value update module: used to update the estimated value using the wide alley ambiguity and the narrow alley ambiguity;

[0015] Parameter determination module: used to determine the current unknown parameters of the double-difference observation equation with the updated estimated values ​​as constraints.

[0016] According to a third aspect of this application, an electronic device is provided, the electronic device including a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the parameter determination method as described in the first aspect.

[0017] According to a fourth aspect of this application, a computer-readable storage medium is provided, wherein at least one instruction or at least one program is stored therein, the at least one instruction or the at least one program being loaded and executed by a processor to implement the parameter determination method as described in the first aspect.

[0018] According to a fifth aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the parameter determination method as described in the first aspect.

[0019] The parameter determination method, apparatus, electronic device, storage medium, and product provided in this application have the following technical advantages:

[0020] This application constructs a double-difference observation equation indicating inter-satellite and inter-receiver relationships. Then, it processes the double-difference observation equation using a pre-defined estimation algorithm and pre-defined elimination coefficients to obtain floating-point ambiguities indicating the absence of an ionosphere and estimates of tropospheric delays. Furthermore, it determines the corresponding wide-lane and narrow-lane ambiguities based on the floating-point ambiguities. Next, it updates the estimates using the wide-lane and narrow-lane ambiguities, thus using the updated estimates as constraints to determine the current unknown parameters of the double-difference observation equation. This application considers that the troposphere is relatively stable to a certain extent and introduces an updated tropospheric delay as a constraint to continue solving the double-difference observation equation, which can improve the efficiency of determining relevant parameters. The determined relevant parameters can include ambiguity values ​​on the inter-base station baseline (corresponding to floating-point ambiguities), balancing the efficiency and accuracy of ambiguity determination, thereby increasing the speed of determining corrections that can be used to improve positioning accuracy and thus improving the location service experience for relevant objects. Attached Figure Description

[0021] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0022] Figure 1 This is a schematic diagram of an application environment provided in an embodiment of this application;

[0023] Figure 2 This is a flowchart illustrating a parameter determination method provided in an embodiment of this application;

[0024] Figure 3 This is a schematic diagram of a process for obtaining the second equation provided in an embodiment of this application;

[0025] Figure 4 This is a schematic diagram of the double-difference observation model provided in the embodiments of this application;

[0026] Figure 5 This is a block diagram of a parameter determination device provided in an embodiment of this application;

[0027] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0028] 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 scope of protection of this application.

[0029] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products or devices.

[0030] Please see Figure 1 , Figure 1 This is a schematic diagram of an application environment provided in an embodiment of this application. This application environment may include a client 10 and a server 20, which can be directly or indirectly connected via wired or wireless communication. The client 10 or server 20 can construct a double-difference observation equation indicating inter-receiver and inter-satellite observations. Then, it processes the double-difference observation equation using a preset estimation algorithm and preset elimination coefficients to obtain a floating-point ambiguity indicating the absence of an ionosphere and an estimate of the tropospheric delay. Furthermore, it determines the corresponding wide-lane ambiguity and narrow-lane ambiguity based on the floating-point ambiguity. Next, it updates the estimate using the wide-lane ambiguity and narrow-lane ambiguity, thereby using the updated estimate as a constraint to determine the current unknown parameters of the double-difference observation equation. It should be noted that... Figure 1 This is just one example.

[0031] Client 10 can be a physical device such as a smartphone, computer (e.g., desktop computer, tablet, laptop), augmented reality (AR) / virtual reality (VR) device, digital assistant, smart voice interaction device (e.g., smart speaker), smart wearable device, smart home appliance, in-vehicle terminal, etc., or it can be software running on the physical device, such as a computer program. The operating system corresponding to the client can be Android, iOS (a mobile operating system developed by Apple), Linux (an operating system), Microsoft Windows, etc.

[0032] The server-side component 20 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. The server may include network communication units, processors, and memory, etc. The server-side component can provide backend services to the corresponding clients.

[0033] In practical applications, the parameter determination method provided in this application embodiment can be executed independently by the client, independently by the server, or by interaction between the client and the server.

[0034] A parameter determination system can be constructed by both the client and server sides, and this system can belong to intelligent transportation systems, etc. Intelligent Transportation Systems (ITS), also known as Intelligent Transportation Systems, effectively integrate advanced science and technology (information technology, computer technology, data communication technology, sensor technology, electronic control technology, automatic control theory, operations research, artificial intelligence, etc.) into transportation, service control, and vehicle manufacturing, strengthening the connection between vehicles, roads, and users, thereby forming a comprehensive transportation system that ensures safety, improves efficiency, improves the environment, and saves energy.

[0035] The parameter determination of the double-difference observation equation can be used to participate in the implementation and optimization of location service functions in relevant internet products. These relevant internet products can include cloud technology products, artificial intelligence products, smart transportation products, assisted driving products, live streaming products, online office products, e-commerce products, gaming products, local lifestyle products, instant messaging products, and social networking products.

[0036] Location services can be used to determine the current location of relevant objects (such as users, simulators, etc.). It should be noted that for current location points associated with user information, when this application embodiment is applied to specific products or technologies, user permission or consent is required, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0037] The following describes a specific embodiment of a parameter determination method according to this application. Figure 2This is a flowchart illustrating a parameter determination method provided in an embodiment of this application. This application provides the operational steps of the method described in the embodiment or flowchart, but based on conventional or non-inventive methods, more or fewer operational steps may be included. The order of steps listed in the embodiment is merely one possible execution order among many and does not represent the only possible execution order. In actual system or product execution, the method can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment) as shown in the embodiment or drawings. Specifically, as... Figure 2 As shown, the method may include:

[0038] S201: Construct a double-difference observation equation to indicate inter-receiver and inter-satellite observations; wherein, the unknown parameters of the double-difference observation equation indicate ambiguity information and delay information;

[0039] In this embodiment, the client or server constructs a double-difference observation equation indicating the inter-satellite relationship between receivers. The client or server can construct this equation using observation data obtained from relevant receivers (base stations). It is understood that the client and server are components of the parameter determination system. The client can independently execute steps S201-S205, and the server can also independently execute steps S201-S205. The client and server can also interact to execute steps S201-S205. The client and server can also be receivers.

[0040] The following section introduces the double-difference observation equations between receivers and satellites. The observation data obtained by the receivers (base stations) can originate from observations of satellite systems such as the Global Positioning System (GPS), BeiDou Navigation Satellite System (BDS), GLONASS (referring to the Russian satellite navigation system), and Galileo. In practical applications, observation data from GPS, BDS, and Galileo can be selected because these three systems are all code division multiple access systems, allowing satellites to be distinguished by code type. When performing double-difference calculations, the corresponding inter-frequency offsets do not need to be considered, and the algorithms are generally simple to implement and highly robust.

[0041] See Figure 4 The diagram involves receiver r, receiver b, satellite j, and satellite k. The double-difference observation equations indicating inter-receiver and inter-satellite observations can be obtained through the following steps:

[0042] 1) Double difference equations can be constructed from the original observation equations of the Global Navigation Satellite System (GNSS).

[0043] Taking receiver r as an example, the original phase observation equation is as follows:

[0044]

[0045] The original pseudorange observation equation is as follows:

[0046]

[0047] Where r represents the receiver, s represents the satellite or satellite number, and i represents the frequency. This represents the phase observation value, in meters. λ represents the pseudorange observation, in meters. i The wavelength representing the carrier phase, measured in meters. dt represents the geometric distance between the satellites, in meters. c represents the speed of light, in m / s. r dt represents the receiver clock bias, in seconds. s This indicates the satellite clock bias, expressed in seconds. This indicates ionospheric delay, measured in meters. d represents tropospheric delay, measured in meters. r，i This indicates the hardware delay at the receiving end, measured in meters. This indicates the hardware latency at the satellite end, measured in meters. This indicates the initial phase at the receiver, measured in cycles. This represents the initial phase at the satellite end, measured in cycles. δ r，i This indicates the phase hardware delay at the receiver, measured in cycles. This indicates the phase hardware delay at the satellite end, measured in weeks. This represents the multipath, noise, and other errors in the phase observations, expressed in meters. This represents the multipath, noise, and other errors in pseudorange observations, expressed in meters.

[0048] 2) Assuming that at the same moment, receiver r (which can be a rover station) and receiver b (which can be a base station) simultaneously observe satellite k, referring to 1) above, we can obtain the original phase observation equations involving receiver r and satellite k, as well as the original phase observation equations involving receiver b and satellite k. The difference between these two equations can be used to obtain the inter-station (inter-receiver) single-difference phase observation equations; referring to 1) above, we can obtain the original pseudorange observation equations involving receiver r and satellite k, as well as the original pseudorange observation equations involving receiver b and satellite k. The difference between these two equations can be used to obtain the inter-station (inter-receiver) single-difference pseudorange observation equations.

[0049] Compared to the original phase observation equation and the original pseudorange observation equation, the common errors at the satellite end in the inter-station single-difference phase observation equation and the inter-station single-difference pseudorange observation equation are eliminated due to the inter-station single-difference. If the distance between the rover station and the base station is short, the corresponding ionospheric and tropospheric errors will also be greatly reduced.

[0050] 3) Assuming that at the same moment, receiver r (which could be a rover station) and receiver b (which could be a base station) simultaneously observe satellite j, referring to 2) above, the inter-station (inter-receiver) single-difference equation for satellite j can be obtained. If satellite k is chosen as the reference satellite, then the inter-station (inter-receiver) double-difference phase observation equation originates from the difference between the "inter-station single-difference equation for satellite j" and the "inter-station single-difference equation for satellite k":

[0051]

[0052] Correspondingly, the pseudorange observation equation for inter-station double difference is derived from the difference between the "inter-station single difference equation for satellite j" and the "inter-station single difference equation for satellite k":

[0053]

[0054] Where b and r indicate the receiver. k and j represent the satellite or satellite number. i represents the frequency. This represents the phase observation value under the inter-station double-difference dimension, in meters. This represents the pseudorange observation value in the inter-station double-difference dimension, in meters. λ represents the geometric distance between stations in the double-difference dimension, in meters. i The wavelength representing the carrier phase, measured in meters. This represents the floating-point ambiguity in the inter-station double-difference dimension. This represents the ionospheric delay in the inter-station double-difference dimension, in meters. This represents the tropospheric delay in the inter-station double-difference dimension, in meters. This represents the multipath, noise, and other errors in phase observations under the inter-station double-difference dimension, expressed in meters. This represents the multipath, noise, and other errors in pseudorange observations under the inter-station double-difference dimension, expressed in meters.

[0055] Therefore, it can be seen that most of the errors can be eliminated through double difference. The double difference observation equation between receivers and between satellites constructed here can be referred to Equation 3. Wherein, Indicates ambiguity information, and The delay information is indicated by parameters that are unknown since the receiver's coordinates are known.

[0056] S202: The double-difference observation equation is processed using a preset estimation algorithm and a preset elimination coefficient to obtain the floating-point ambiguity indicating the absence of the ionosphere and the estimated value of the tropospheric delay; wherein, the preset elimination coefficient is used to eliminate the influence of the ionosphere;

[0057] In this embodiment, the client or server uses a preset estimation algorithm and preset elimination coefficients to process the double-difference observation equation, obtaining a floating-point ambiguity indicating ionospheric ambiguity and an estimate of the tropospheric delay. Considering that the delay information indicated by the currently unknown parameters involves ionospheric delay, and this embodiment focuses on accelerating the parameter convergence of the equation using tropospheric delay as a constraint, the parameter characterization in the ionospheric state can be determined first. When determining the parameter characterization in the ionospheric state, a preset estimation algorithm and preset elimination coefficients can be used. The preset estimation algorithm can be an algorithm suitable for optimal estimation, such as the Kalman filter algorithm. In practical applications, the estimated value of the tropospheric delay can indicate the wet term delay (residual).

[0058] In an exemplary embodiment, the step of processing the double-difference observation equation using a preset estimation algorithm and a preset elimination coefficient to obtain a floating-point ambiguity indicating the absence of the ionosphere and an estimate of the tropospheric delay may include the following steps: First, processing the double-difference observation equation using the preset elimination coefficient to obtain a first equation with the ionospheric delay eliminated; then, processing the first equation using the preset estimation algorithm to obtain a floating-point ambiguity indicating the absence of the ionosphere and an estimate of the tropospheric delay.

[0059] Based on the aforementioned principle that "the phase observation equation for inter-station double difference originates from the difference between the inter-station single difference equation for satellite j and the inter-station single difference equation for satellite k," we can define the indicator frequency 1 for the inter-station single difference equation for satellite j and the indicator frequency 2 for the inter-station single difference equation for satellite k. Then, we can assign an elimination coefficient to each of them to eliminate the ionospheric delay.

[0060] For example, the preset elimination coefficients can be a pair of elimination coefficients l and m:

[0061]

[0062]

[0063] The process of obtaining the first equation can be found in equations seven and eight below:

[0064]

[0065]

[0066] Where b and r indicate the receiver. k and j represent the satellite or satellite number. f1 indicates frequency 1, f2 indicates frequency 2. IF indicates an ionospheric-free combination. This indicates the phase observation value indicating the ionospheric state in the inter-station (inter-receiver) double-difference dimension. This represents the phase observation value corresponding to frequency 1 in the single-difference dimension between stations (between receivers). This represents the phase observation value corresponding to frequency 2 in the single difference dimension between stations (between receivers). It represents the geometric distance between stations (receivers) in the double-difference dimension. This represents the ionospheric floating-point ambiguity in the double-difference dimension between stations (receivers). This represents the tropospheric delay in the double-difference dimension between stations (receivers). This represents the multipath, noise, and other errors in phase observations indicating the ionospheric state under the inter-station (inter-receiver) double-difference dimension. λ IF The formula for representing the wavelength in the absence of an ionosphere is as follows:

[0067]

[0068] Where c represents the speed of light.

[0069] Therefore, a preset estimation algorithm can be used to process the first equation (see Formula 8 above) to obtain the floating-point ambiguity indicating ionosphere absence and the estimated values ​​of tropospheric delay. The preset elimination coefficient improves the efficiency and convenience of obtaining parameter representations in the ionosphere-free state. Thus, the floating-point ambiguity indicating ionosphere absence and the estimated values ​​of tropospheric delay obtained using the preset estimation algorithm can serve as the basis for subsequently obtaining the tropospheric delay used as a constraint, ensuring the validity of the basic data. Compared to the double-difference observation equation, when using the preset estimation algorithm to estimate the parameters of the first equation, there are fewer unknown parameters when the known parameters are the same, which is beneficial to improving the accuracy and efficiency of parameter estimation. In practical applications, when using the preset estimation algorithm to process the first equation, a corresponding Kalman filter equation can be constructed based on the first equation for solution. Alternatively, a corresponding Kalman filter equation can be constructed based on the double-difference observation equation, and the preset elimination coefficient can be incorporated into the solution process of this Kalman filter equation.

[0070] Further, such as Figure 3 As shown, before processing the first equation using the preset estimation algorithm to obtain the floating-point ambiguity indicating the ionosphere-free state and the estimated value related to the tropospheric delay, the method further includes:

[0071] S301: Obtain the initial value of the zenith tropospheric delay for the target receiver; wherein, the target receiver is any one of the two receivers corresponding to the double-difference observation equation;

[0072] S302: Obtain a preset tropospheric mapping function; wherein, the preset tropospheric mapping function is used to establish the correlation between the estimated value of the tropospheric delay and the zenith tropospheric delay based on the satellite elevation angle;

[0073] S303: Based on the first equation, the preset tropospheric mapping function, and the initial value of the zenith tropospheric delay, the second equation is obtained;

[0074] To further improve the efficiency of obtaining floating-point ambiguity indicators for ionosphere-free systems and estimates of tropospheric delay, the original parameters related to multipath, noise, and other errors can be eliminated by introducing a preset tropospheric mapping function and an initial value for zenith tropospheric delay.

[0075] For example, if the receivers involved in the double-difference observation equation are receiver r and receiver b, and here receiver r is chosen as the target receiver, RZTD r This represents the initial (relative) zenith tropospheric delay value corresponding to receiver r. The initial (relative) zenith tropospheric delay value RZTD is used... r The second equation can be obtained by processing the first equation using the pre-defined tropospheric mapping function MF (see equations seven and eight above):

[0076]

[0077] Where l and m are a pair of cancellation coefficients. b and r indicate the receiver. j represents the satellite number used as the reference satellite. 1, ..., n represent the satellite numbers. This represents the phase observation value corresponding to frequency 1 in the single difference dimension between stations (between receivers), where the value of i ranges from 1 to n. This represents the phase observation value corresponding to frequency 2 in the single difference dimension between stations (between receivers). This represents the geometric distance between stations (receivers) in the double-difference dimension, where the value of i ranges from 1 to n. λ represents the tropospheric delay in the inter-station double-difference dimension. IF This represents the wavelength in the absence of an ionosphere. This represents the ionospheric floating-point ambiguity in the double-difference dimension between stations (receivers), where the value of i ranges from 1 to n.

[0078] (Relative) Initial zenith tropospheric delay (RZTD) r This can be obtained through relevant tropospheric models, such as the Saastamoinen model. By introducing a preset tropospheric mapping function MF, the inter-station double-difference tropospheric delay indicated on the left side of the equation can be represented by the product of the (relative) zenith tropospheric delay and the preset tropospheric mapping function MF. Correspondingly, the preset estimation algorithm can be used to process the second equation (Formula 10) to obtain the floating-point ambiguity indicating the ionosphere-free state and the estimated value of the tropospheric delay. In this embodiment, the estimated value of the tropospheric delay obtained here is used as the basis for the tropospheric delay. After fixing the wide-lane ambiguity and narrow-lane ambiguity in subsequent steps, this value can be updated to obtain a more accurate tropospheric delay. In practical applications, the estimated value of the tropospheric delay can indicate the wet term delay (residual), and correspondingly, the initial value of the zenith tropospheric delay involved here also indicates the wet term delay (residual).

[0079] The following sections will discuss the Kalman filter algorithm and the initial (relative) zenith tropospheric delay (RZTD). r Introducing the topic:

[0080] 1) Regarding the Kalman filter algorithm:

[0081] The Kalman filter algorithm generally consists of two modules: a time update module and a measurement update module. The relevant formulas are as follows:

[0082]

[0083]

[0084] Among them, Formula 11 is the equation for one-step prediction, and Formula 12 is the observation equation at time k. For the state predicted in one step at epoch k (time), Φ k,k-1 Let k be the state transition matrix from time k-1 to time k. G is the measured and updated parameter for epoch k-1. k Let w be the system noise driving matrix at time k. k Let z be the system noise at time k. k H is the observation value at time k. k The design matrix for the observation equation, Let e ​​be the state parameter at time k. k This represents the observation error. The complete formulas for the Kalman filter from time k-1 to time k can be summarized as follows:

[0085]

[0086]

[0087]

[0088]

[0089] P k (+)=(IK k H k )P k (-) (Formula Seventeen)

[0090] Among them, P k (-) is the matrix for one-step prediction, Q k For the system noise array, K k Let R be the gain matrix. k P is the noise matrix of the observations. k (+) represents the variance-covariance matrix corresponding to the updated state parameters.

[0091] Taking the second equation (see Formula 10 above) as an example, the corresponding Kalman filter equation can be constructed based on the first equation (see Formulas 7 and 8 above). The estimator corresponding to this Kalman filter equation is as follows:

[0092]

[0093] Where 'r' indicates the receiver. RZTD r This represents the estimated (relative) zenith tropospheric delay for receiver r. This represents the ionospheric floating-point ambiguity in the double-difference dimension between stations (receivers), where the value of i ranges from 1 to n, as shown in Formula 10 above.

[0094] 2) Regarding the initial value of (relative) zenith tropospheric delay RZTD r :

[0095] (Relative) Initial zenith tropospheric delay (RZTD) r The zenith tropospheric delay can be generated through the following steps: First, obtain multiple zenith tropospheric delay values; then, determine the mean and variance corresponding to the multiple zenith tropospheric delay values; next, filter the multiple zenith tropospheric delay values ​​according to the mean and variance to obtain multiple target zenith tropospheric delay values; finally, determine the mean corresponding to the multiple target zenith tropospheric delay values ​​as the initial value of the zenith tropospheric delay.

[0096] Each zenith tropospheric delay value is determined based on the solution results of the corresponding double-difference observation equation. It can be understood that after determining that the relevant unknown parameters of each double-difference observation variance have been solved, a zenith tropospheric delay value can be determined using these parameters, which can be combined with Equation 26 described later. However, the (relative) zenith tropospheric delay value involved in Equation 26 is relative to the target receiver.

[0097] The process of filtering multiple zenith tropospheric delay values ​​based on their mean and variance involves determining a comparison parameter based on the variance, then comparing each zenith tropospheric delay value with the comparison parameter. If the zenith tropospheric delay value is less than or equal to the comparison parameter, it is retained; otherwise, it is discarded. Finally, the mean of the retained zenith tropospheric delay values ​​is calculated as the initial zenith tropospheric delay value. The comparison parameter can be three times the standard deviation (corresponding to the aforementioned variance). This variance can also be used as an empirical threshold and incorporated into a pre-defined estimation algorithm as a constraint equation, such as in the filter of an indexed Cartesian filter algorithm. Using the mean and variance to filter multiple zenith tropospheric delay values ​​effectively removes zenith tropospheric delay values ​​with biases, thus ensuring the applicability of the initial zenith tropospheric delay value.

[0098] The process of obtaining multiple zenith tropospheric delay values ​​and determining the average value corresponding to multiple target zenith tropospheric delay values ​​can be performed in real time. The obtained multiple zenith tropospheric delay values ​​can originate from the double-difference observation equations involved in the most recent preset time period. The aforementioned initial value of (relative) zenith tropospheric delay, RZTD... r The average of the latest determined target zenith tropospheric delay values ​​can be taken. This can improve the (relative) initial zenith tropospheric delay value RZTD. r The timeliness of the data. In practical applications, the estimated value of the tropospheric delay can indicate the wet term delay (residual), and correspondingly, the initial value of the zenith tropospheric delay and the zenith tropospheric delay value involved here also indicate the wet term delay (residual).

[0099] Furthermore, a pre-defined model can be used to filter multiple zenith tropospheric delay values ​​to obtain results indicating multiple target zenith tropospheric delay values. This pre-defined model is obtained through machine learning training on multiple samples, each carrying a label indicating whether it is an outlier. By training a pre-defined model with high generalization ability using relevant machine learning models, the filtering capability for a large number of zenith tropospheric delay values ​​can be improved, thereby significantly increasing the efficiency and reliability of determining the mean of multiple target zenith tropospheric delay values. The relevant machine learning model used can employ algorithms such as the local anomaly factor algorithm and clustering algorithms.

[0100] S203: Determine the corresponding wide-lane ambiguity and narrow-lane ambiguity based on the floating-point ambiguity;

[0101] In this embodiment, the client or server determines the corresponding wide-lane ambiguity and narrow-lane ambiguity based on floating-point ambiguity. Since floating-point ambiguity does not have integer characteristics, it can be decomposed to obtain fixed wide-lane ambiguity and narrow-lane ambiguity in order to improve the solution efficiency of the double-difference observation equation.

[0102] In an exemplary embodiment, determining the corresponding wide-lane ambiguity and narrow-lane ambiguity based on the floating-point ambiguity may include the following steps: First, decomposing the floating-point ambiguity to determine a first type of information indicating the wide lane and a second type of information indicating the narrow lane; then, processing the first type of information using a preset cycle slip detection method to obtain the wide-lane ambiguity; furthermore, determining the narrow-lane floating-point ambiguity based on the wide-lane ambiguity and the second type of information, and rounding the narrow-lane floating-point ambiguity to obtain the narrow-lane ambiguity.

[0103] The first type of information indicating wide alleys and the second type of information indicating narrow alleys can be obtained using the following formulas:

[0104] N WL =N1-N2 (Formula 19)

[0105]

[0106]

[0107] Where, N WL N represents the wide-lane ambiguity. N1 indicates the ambiguity at frequency 1, which can be considered as the narrow-lane ambiguity. N2 indicates the ambiguity at frequency 2. IF This represents the floating-point ambiguity without an ionosphere. f1 represents frequency 1, and f2 represents frequency 2. The decomposition indicates the floating-point ambiguity N indicating the absence of an ionosphere. IF The first type of information indicating the width of the alley can be obtained. and the second type of information indicating narrow alleys It is understandable that the floating-point ambiguity N indicating the absence of an ionosphere is... IF The wide-lane ambiguity N can be obtained by decomposition. WL And the ambiguity N1 between the wide and narrow alleyways. It should be noted that the wavelength corresponding to the wide alleyway is greater than the wavelength corresponding to the narrow alleyway. Wavelength is generally determined by frequency.

[0108] 1) Determining the ambiguity of the wide alley:

[0109] By processing the first type of information regarding the indicated wide lane using a pre-defined cycle slip detection method, the wide lane ambiguity can be fixed. The pre-defined cycle slip detection method can be a linear combination of dual-frequency P-code and phase observations (MW method). The formula for calculating the wide lane ambiguity is as follows:

[0110]

[0111] in, For the phase observation value indicating frequency 1, This indicates the phase observation value at frequency 2. f1 represents frequency 1, and f2 represents frequency 2. For pseudorange observations indicating frequency 1, λ represents the pseudorange observation indicating frequency 2. WL The wavelength of the wide lane is calculated as follows:

[0112]

[0113] Where c represents the speed of light. Wide-lane ambiguity, due to its longer wavelength, can be directly rounded after multi-epoch smoothing.

[0114] 2) Determining the ambiguity of narrow alleyways:

[0115] The above 1) can fix the wide-lane ambiguity, and the narrow-lane floating-point ambiguity can be determined using the determined wide-lane ambiguity and the second type of information indicating the narrow-lane. Although the determined wide-lane ambiguity is a fixed solution, combined with the floating-point ambiguity on the left side of the above formula 21, what can be determined is still the floating-point solution of the narrow-lane ambiguity. On this basis, the narrow-lane floating-point ambiguity can be further rounded to obtain the narrow-lane ambiguity (the fixed solution of the narrow-lane ambiguity).

[0116] Specifically, this involves: first, determining the integer solution corresponding to the narrow-lane floating-point ambiguity; then, when the difference between the narrow-lane floating-point ambiguity and the integer solution is less than a first preset threshold, and the variance corresponding to the narrow-lane floating-point ambiguity is less than a second preset threshold, the integer solution is used as the narrow-lane ambiguity.

[0117] Please refer to Formula 24 below:

[0118]

[0119] One of the formulas indicates the ambiguity of the floating-point fuzziness in the narrow alleyway. And integer solutions round The absolute value of the difference is less than 0.2 (corresponding to the first preset threshold), and the other formula indicates the standard deviation corresponding to the narrow-lane floating-point ambiguity. Less than 0.2 (corresponding to the second preset threshold).

[0120] Furthermore, to ensure the validity and usability of the determined narrow-lane ambiguity, it can be verified. The verification process may include the following steps: First, obtain the two associated ambiguities corresponding to the narrow-lane ambiguity; then, concatenate the feature vector corresponding to the narrow-lane ambiguity and the feature vectors corresponding to the two associated ambiguities in a predetermined order to obtain a reference vector; furthermore, when the reference vector equals a third preset threshold, it is determined that the narrow-lane ambiguity meets the preset verification requirements. If the determined narrow-lane ambiguity is valid and usable, it can be used in step S204 to update the estimated value of the tropospheric delay. Considering that the determined narrow-lane ambiguity involves two receivers, and receivers often belong to a multi-receiver region, by verifying the narrow-lane ambiguity involving other receivers, the applicability of the determined narrow-lane ambiguity to the multi-receiver region can be improved. If the determined narrow-lane ambiguity is not valid or usable, step S202 can be repeated to redetermine the floating-point ambiguity indicating the absence of an ionosphere and the estimated value of the tropospheric delay. Then, based on the floating-point ambiguity, the corresponding wide-lane and narrow-lane ambiguities are determined, and the determined narrow-lane ambiguity is verified. Alternatively, the verification process of two correlated ambiguities that meet preset verification requirements can be reviewed, such as the preset threshold used in the process.

[0121] The set of receivers corresponding to the two associated ambiguities and the receiver corresponding to the narrow alley ambiguity includes three receivers, and the two associated ambiguities meet the preset verification requirements. It can be understood that the two associated ambiguities and the narrow alley ambiguity each correspond to three baselines, and the three baselines can form a triangle. If the receivers involved in the double-difference observation equation are receiver A and receiver B, then the receivers corresponding to the narrow alley ambiguity are receiver A and receiver B. The union of the receivers corresponding to the two associated ambiguities, receiver A, and receiver B is three receivers, so the other receiver besides receivers A and B can be taken as receiver C. Therefore, one associated ambiguity can correspond to receiver A and receiver C, and another associated ambiguity can correspond to receiver B and receiver C. See Formula Twenty-Five below:

[0122]

[0123] In this context, A, B, and C indicate the receiver. i and j represent the satellite or satellite number. The feature vector representing the indicator narrow alley ambiguity corresponding to the baseline between receiver A and receiver B. The feature vector representing the indicator narrow alley ambiguity corresponding to the baseline between receiver B and receiver C. This represents the feature vector indicating the narrow alleyway ambiguity corresponding to the baseline between receiver C and receiver A. The 0 vector represents the third preset threshold.

[0124] S204: Update the estimated value using the wide alley ambiguity and the narrow alley ambiguity;

[0125] In this embodiment, the client or server updates the estimated value using wide-lane ambiguity and narrow-lane ambiguity. By using fixed wide-lane and narrow-lane ambiguities, a fixed ambiguity solution indicating the absence of an ionosphere can be obtained. Then, based on the double-difference observation equation (or the aforementioned first and second equations), the tropospheric delay is back-calculated, updating the estimated value of the tropospheric delay with a new, more accurate tropospheric delay. In practical applications, the estimated value of the tropospheric delay can indicate the wet term delay (residual), and correspondingly, the updated object and the updated estimated value also indicate the wet term delay (residual).

[0126] S205: Determine the current unknown parameters of the double-difference observation equation using the updated estimated values ​​as constraints.

[0127] In this embodiment, the client or server determines the current unknown parameters of the double-difference observation equation using the updated estimated value as a constraint. Using the new, more accurate tropospheric delay as a constraint is equivalent to adding a constraint equation to the solution of the double-difference observation equation, thereby accelerating the convergence of relevant parameters in the double-difference observation equation. Combining the ambiguity information and delay information of the unknown parameters in the double-difference observation equation mentioned in step S201, the determination of unknown parameters may not be based on a single solution; therefore, parameters with existing solution results can still be considered current unknown parameters. Current unknown parameters may include parameters such as the fixed solution indicating ambiguity without ionosphere. Thus, the tropospheric delay is considered a currently known parameter to continue solving the fixed solution indicating ambiguity without ionosphere.

[0128] The determination of the currently unknown parameters can be achieved using the aforementioned preset estimation parameters. Taking the Karl filter parameters as the preset estimation parameters as an example, and combining them with Equation 10 above, we can obtain the following formula:

[0129]

[0130] Where l and m are a pair of cancellation coefficients. b and r indicate the receiver. j represents the satellite number used as the reference satellite. 1, ..., n represent the satellite numbers. This represents the phase observation value corresponding to frequency 1 in the single difference dimension between stations (between receivers), where the value of i ranges from 1 to n. This represents the phase observation value corresponding to frequency 2 in the single difference dimension between stations (between receivers). This represents the geometric distance between stations (receivers) in the double-difference dimension, where the value of i ranges from 1 to n. λ represents the tropospheric delay in the inter-station double-difference dimension.IF This represents the wavelength in the absence of an ionosphere. RZTD represents the ionospheric-free floating-point ambiguity in the inter-station (inter-receiver) double-difference dimension, where i ranges from 1 to n. MF represents the preset tropospheric mapping function. r This represents the (relative) zenith tropospheric delay value corresponding to receiver r.

[0131] In practical applications, the embodiments of this application can be used to determine ambiguity values ​​on long baselines (generally 80km and above) between base stations, thereby improving the fixation rate and reliability of ambiguity in medium and long baselines. The tropospheric delay used as a constraint can indicate the wet term delay (residual), and correspondingly, the zenith tropospheric delay value and the initial zenith tropospheric delay value also indicate the wet term delay (residual). In long baseline RTK calculation, because the distance between two base stations is relatively long, the correlation of atmospheric errors between base stations is relatively weak, and the influence of the double-difference tropospheric wet delay error is difficult to eliminate. The embodiments of this application, after fixing the wide-lane ambiguity and narrow-lane ambiguity, back-calculate the double-difference tropospheric wet delay error, and use this as a constraint condition to accelerate the convergence of floating-point ambiguity in long baselines, thereby achieving rapid fixation of floating-point ambiguity. This bypasses the aforementioned calculation difficulties, achieving floating-point ambiguity fixation while improving fixation efficiency.

[0132] Multiple base stations can be established within a region to form a mesh coverage. Correction parameters that improve positioning accuracy can be calculated and broadcast using one or more of these base stations as a reference, thereby enabling real-time positioning correction for users within the region. The parameter determination scheme provided in this application can be used for relevant positioning corrections within this region. By using a new tropospheric delay with higher accuracy as a constraint, the convergence of parameters related to newly added satellites can be accelerated, thereby achieving rapid convergence of the overall network RTK.

[0133] As can be seen from the technical solutions provided in the embodiments of this application above, the embodiments of this application construct a double-difference observation equation indicating the inter-satellite and inter-receiver relationships; then, using a preset estimation algorithm and preset elimination coefficients to process the double-difference observation equation, the floating-point ambiguity indicating the absence of the ionosphere and the estimated value of the tropospheric delay are obtained; furthermore, the corresponding wide-lane ambiguity and narrow-lane ambiguity are determined based on the floating-point ambiguity; then, the estimated value is updated using the wide-lane ambiguity and narrow-lane ambiguity, thereby using the updated estimated value as a constraint to determine the current unknown parameters of the double-difference observation equation. This application considers that the troposphere is relatively stable to a certain extent, and introduces an updated tropospheric delay that can be used as a constraint to continue solving the double-difference observation equation, which can improve the efficiency of determining relevant parameters. The determined relevant parameters may include ambiguity values ​​on the inter-base station baseline (corresponding to floating-point ambiguity), which can balance the efficiency and accuracy of ambiguity determination, thereby improving the speed of determining correction numbers that can be used to improve positioning accuracy and thus improving the location service experience of relevant objects.

[0134] This application also provides a parameter determination device, such as... Figure 5 As shown, the parameter determining device 50 includes:

[0135] Equation construction module 501: used to construct double-difference observation equations indicating inter-receiver and inter-satellite observations; wherein, the unknown parameters of the double-difference observation equations indicate ambiguity information and delay information;

[0136] Equation processing module 502: used to process the double-difference observation equation using a preset estimation algorithm and a preset elimination coefficient to obtain the floating-point ambiguity indicating the absence of the ionosphere and the estimated value of the tropospheric delay; wherein, the preset elimination coefficient is used to eliminate the influence of the ionosphere;

[0137] Ambiguity determination module 503: used to determine the corresponding wide-lane ambiguity and narrow-lane ambiguity based on the floating-point ambiguity;

[0138] Estimation update module 504: used to update the estimated value using the wide alley ambiguity and the narrow alley ambiguity;

[0139] Parameter determination module 505: used to determine the current unknown parameters of the double-difference observation equation with the updated estimated values ​​as constraints.

[0140] It should be noted that the apparatus and method embodiments described in the device embodiments are based on the same inventive concept.

[0141] This application provides an electronic device including a processor and a memory. The memory stores at least one instruction or at least one program segment, which is loaded and executed by the processor to implement the parameter determination method provided in the above method embodiments.

[0142] Further, Figure 6 A schematic diagram of the hardware structure of an electronic device for implementing the parameter determination method provided in the embodiments of this application is shown. The electronic device may participate in or include the parameter determination apparatus provided in the embodiments of this application. Figure 6 As shown, the electronic device 100 may include one or more processors 1002 (shown as 1002a, 1002b, ..., 1002n in the figure) (processor 1002 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 1004 for storing data, and a transmission device 1006 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 6 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, the electronic device 100 may also include... Figure 6 The more or fewer components shown, or having the same Figure 6 The different configurations shown.

[0143] It should be noted that the aforementioned one or more processors 1002 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be wholly or partially integrated into any other element within the electronic device 100 (or mobile device). As involved in the embodiments of this application, the data processing circuit serves as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0144] The memory 1004 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the parameter determination method described in the embodiments of this application. The processor 1002 executes various functional applications and data processing by running the software programs and modules stored in the memory 1004, thereby implementing the aforementioned parameter determination method. The memory 1004 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 1004 may further include memory remotely located relative to the processor 1002, and these remote memories can be connected to the electronic device 100 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0145] The transmission device 1006 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the electronic device 100. In one example, the transmission device 1006 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In one embodiment, the transmission device 1006 may be a radio frequency (RF) module for wireless communication with the Internet.

[0146] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows a user to interact with the user interface of the electronic device 100 (or mobile device).

[0147] Embodiments of this application also provide a computer-readable storage medium, which can be disposed in an electronic device to store at least one instruction or at least one program related to implementing a parameter determination method in the method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the parameter determination method provided in the above method embodiment.

[0148] Optionally, in this embodiment, the storage medium may be located in at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0149] Embodiments of this application also provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the parameter determination method provided in the above-described method embodiments.

[0150] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are also possible or may be advantageous.

[0151] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and electronic device embodiments are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0152] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0153] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for determining parameters, characterized in that, The method includes: Construct a double-difference observation equation between receivers and between satellites, wherein the unknown parameters of the double-difference observation equation indicate ambiguity information and delay information; The double-difference observation equation is processed using a preset elimination coefficient to obtain a first equation with ionospheric delay eliminated. The preset elimination coefficient is used to eliminate the influence of the ionosphere. The first equation is processed using a preset estimation algorithm to obtain the floating-point ambiguity indicating the absence of an ionosphere and the estimated value of the tropospheric delay. The corresponding wide-lane ambiguity and narrow-lane ambiguity are determined based on the floating-point ambiguity; The estimated value is updated using the wide alley ambiguity and the narrow alley ambiguity; Using the updated estimated values ​​as constraints, determine the current unknown parameters of the double-difference observation equation; Before processing the first equation using the preset estimation algorithm to obtain the floating-point ambiguity indicating the ionosphere and the estimated value of the tropospheric delay, the method further includes: obtaining an initial value of the zenith tropospheric delay for an indicated target receiver, wherein the target receiver is any one of the two receivers corresponding to the double-difference observation equation; obtaining a preset tropospheric mapping function, which is used to establish a correlation between the estimated value of the tropospheric delay and the zenith tropospheric delay based on the satellite elevation angle; and obtaining a second equation based on the first equation, the preset tropospheric mapping function, and the initial value of the zenith tropospheric delay. The step of processing the first equation using the preset estimation algorithm to obtain the floating-point ambiguity indicating the absence of the ionosphere and the estimated value of the tropospheric delay includes: processing the second equation using the preset estimation algorithm to obtain the floating-point ambiguity indicating the absence of the ionosphere and the estimated value of the tropospheric delay.

2. The method according to claim 1, characterized in that, The method further includes: Multiple zenith tropospheric delay values ​​are obtained; wherein each zenith tropospheric delay value is determined based on the solution result of the corresponding double-difference observation equation; Determine the mean and variance corresponding to the plurality of zenith tropospheric delay values; The plurality of zenith tropospheric delay values ​​are filtered based on the mean and the variance to obtain a plurality of target zenith tropospheric delay values. The mean value corresponding to the plurality of target zenith tropospheric delay values ​​is determined to be the initial value of the zenith tropospheric delay.

3. The method according to claim 1, characterized in that, The determination of the corresponding wide-lane ambiguity and narrow-lane ambiguity based on the floating-point ambiguity includes: The floating-point ambiguity is decomposed to determine the first type of information indicating wide alleyways and the second type of information indicating narrow alleyways; The wide-lane ambiguity is obtained by processing the first type of information using a preset cycle slip detection method; The narrow alley floating-point ambiguity is determined based on the wide alley ambiguity and the second type of information, and the narrow alley ambiguity is obtained by rounding down the narrow alley floating-point ambiguity.

4. The method according to claim 3, characterized in that, The process of rounding down the floating-point ambiguity of the narrow alleyway to obtain the narrow alleyway ambiguity includes: Determine the integer solution corresponding to the narrow alley floating-point ambiguity; When the difference between the narrow-lane floating-point ambiguity and the integer solution is less than a first preset threshold, and the variance corresponding to the narrow-lane floating-point ambiguity is less than a second preset threshold, the integer solution is used as the narrow-lane ambiguity.

5. The method according to any one of claims 1, 3, or 4, characterized in that, The method further includes: Obtain the two associated ambiguities corresponding to the narrow alley ambiguity; wherein, the set constructed by the receivers corresponding to the two associated ambiguities and the receivers corresponding to the narrow alley ambiguity includes three receivers, and the two associated ambiguities meet the preset verification requirements; The feature vectors corresponding to the narrow alley ambiguity and the feature vectors corresponding to the two associated ambiguities are concatenated in a predetermined order to obtain a reference vector. When the reference vector is equal to the third preset threshold, the narrow alley ambiguity is determined to meet the preset verification requirements.

6. A parameter determining device, characterized in that, The device includes: Equation construction module: used to construct double-difference observation equations between receivers and between satellites; wherein, the unknown parameters of the double-difference observation equations indicate ambiguity information and delay information; Equation processing module: used to process the double-difference observation equation using a preset elimination coefficient to obtain a first equation with ionospheric delay eliminated, wherein the preset elimination coefficient is used to eliminate the influence of the ionosphere; and to process the first equation using a preset estimation algorithm to obtain a floating-point ambiguity indicating the absence of the ionosphere and an estimated value related to the tropospheric delay. Ambiguity determination module: used to determine the corresponding wide-lane ambiguity and narrow-lane ambiguity based on the floating-point ambiguity; Estimated value update module: used to update the estimated value using the wide alley ambiguity and the narrow alley ambiguity; Parameter determination module: used to determine the current unknown parameters of the double-difference observation equation with the updated estimated values ​​as constraints; The device is further configured to: acquire an initial value of the zenith tropospheric delay for a target receiver, wherein the target receiver is any one of the two receivers corresponding to the double-difference observation equation; acquire a preset tropospheric mapping function, wherein the preset tropospheric mapping function is used to establish a correlation between the estimated value of the tropospheric delay and the zenith tropospheric delay based on the satellite elevation angle; and obtain a second equation based on the first equation, the preset tropospheric mapping function, and the initial value of the zenith tropospheric delay. The step of processing the first equation using the preset estimation algorithm to obtain the floating-point ambiguity indicating the absence of the ionosphere and the estimated value of the tropospheric delay includes: processing the second equation using the preset estimation algorithm to obtain the floating-point ambiguity indicating the absence of the ionosphere and the estimated value of the tropospheric delay.

7. The apparatus according to claim 6, characterized in that, The device is also used for: Multiple zenith tropospheric delay values ​​are obtained; wherein each zenith tropospheric delay value is determined based on the solution result of the corresponding double-difference observation equation; Determine the mean and variance corresponding to the plurality of zenith tropospheric delay values; The plurality of zenith tropospheric delay values ​​are filtered based on the mean and the variance to obtain a plurality of target zenith tropospheric delay values. The mean value corresponding to the plurality of target zenith tropospheric delay values ​​is determined to be the initial value of the zenith tropospheric delay.

8. The apparatus according to claim 6, characterized in that, The step of determining the corresponding wide-lane ambiguity and narrow-lane ambiguity based on the floating-point ambiguity includes: decomposing the floating-point ambiguity to determine a first type of information indicating the wide lane and a second type of information indicating the narrow lane; processing the first type of information using a preset cycle slip detection method to obtain the wide-lane ambiguity; determining the narrow-lane floating-point ambiguity based on the wide-lane ambiguity and the second type of information; and rounding the narrow-lane floating-point ambiguity to obtain the narrow-lane ambiguity.

9. The apparatus according to claim 8, characterized in that, The step of rounding the narrow-lane floating-point ambiguity to obtain the narrow-lane ambiguity includes: determining the integer solution corresponding to the narrow-lane floating-point ambiguity; when the difference between the narrow-lane floating-point ambiguity and the integer solution is less than a first preset threshold, and the variance corresponding to the narrow-lane floating-point ambiguity is less than a second preset threshold, the integer solution is taken as the narrow-lane ambiguity.

10. The apparatus according to any one of claims 6, 8 or 9, characterized in that, The device is also used for: Obtain the two associated ambiguities corresponding to the narrow alley ambiguity; wherein, the set constructed by the receivers corresponding to the two associated ambiguities and the receivers corresponding to the narrow alley ambiguity includes three receivers, and the two associated ambiguities meet the preset verification requirements; The feature vectors corresponding to the narrow alley ambiguity and the feature vectors corresponding to the two associated ambiguities are concatenated in a predetermined order to obtain a reference vector. When the reference vector is equal to the third preset threshold, the narrow alley ambiguity is determined to meet the preset verification requirements.

11. An electronic device, characterized in that, The electronic device includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the parameter determination method as described in any one of claims 1-5.

12. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction or at least one program segment, which is loaded and executed by a processor to implement the parameter determination method as described in any one of claims 1-5.

13. A computer program product, characterized in that, The computer program product includes at least one instruction or at least one program segment, which is loaded and executed by a processor to implement the parameter determination method as described in any one of claims 1-5.

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