Pseudolite signal environment identification method, positioning method and electronic device

Abstract

A pseudolite signal environment identification method, a positioning method and an electronic device. The method is applied to a GNSS receiver, comprising: respectively acquiring Doppler residual values of multiple satellite signal channels received by the GNSS receiver; and in the case that the Doppler residual values of the multiple satellite signal channels are determined to be consistent, determining that the current is in a pseudolite signal environment. The application scheme identifies the pseudolite environment based on the consistency of the Doppler residual values of the multiple satellite signal channels, improves the effectiveness of the pseudolite environment identification, provides an accurate basis for further positioning solution strategy adjustment based on the pseudolite environment, and improves the positioning accuracy of the receiver.

Description

Technical Field

[0001] This article relates to, but is not limited to, the field of satellite positioning technology, and in particular to a pseudo-satellite signal environment identification method, positioning method, and electronic equipment. Background Technology

[0002] Global Navigation Satellite System (GNSS) provides global users with three-dimensional, all-weather, high-quality positioning, navigation, and timing (PNT) services, making it a crucial positioning tool for industries such as transportation, communication, and surveying. However, due to the weak signal strength of GNSS, it cannot penetrate enclosed environments such as indoor spaces, tunnels, or underground spaces. Furthermore, it is susceptible to obstruction in complex scenarios like urban canyons, resulting in poor geometric configurations and making it difficult to meet the requirements for continuous, reliable, and high-precision positioning. Therefore, a pseudosatellite system solution has been proposed. The pseudosatellite solution deploys several signal transmitters with precisely known location coordinates on the ground. By emitting ranging signals with a structure similar to GNSS signals, receivers can perform pseudorange measurements as if receiving real satellite signals. Positioning is then calculated through spatial rendezvous, extending satellite navigation capabilities to blind spots not covered by satellite signals.

[0003] Although pseudo-satellites and real satellites both emit similar ranging signals, they differ fundamentally in their operating platforms, signal source characteristics, and spatiotemporal references. Therefore, accurately identifying whether a receiver is in a pseudo-satellite environment and adjusting data processing strategies in a timely manner are crucial for correct positioning calculations and truly realizing the supplementary and enhancing role of pseudo-satellite solutions in GNSS positioning. Summary of the Invention

[0004] This application provides a pseudo-satellite signal environment identification method, a positioning method, and an electronic device. It identifies pseudo-satellite environments based on the consistency of Doppler residual values ​​from multiple satellite signal channels, thereby improving the effectiveness of pseudo-satellite environment identification. This provides an accurate basis for further adjusting positioning solutions based on the pseudo-satellite environment and enhances the positioning accuracy of the receiver.

[0005] This application provides a method for identifying pseudosatellite signal environments, applied to a GNSS receiver, including: Obtain the Doppler residual values ​​of multiple satellite signal channels received by the GNSS receiver; If the Doppler residual values ​​of the multiple satellite signal channels are found to be consistent, it is determined that the current environment is a pseudo-satellite signal environment.

[0006] This application also provides a positioning method applied to a GNSS receiver, including: The pseudo-satellite signal environment identification method as described in any embodiment of this disclosure determines whether the current environment is a pseudo-satellite environment; When it is determined that the environment is a pseudo-satellite environment, the average velocity of the GNSS receiver is calculated based on the position information of the N historical sliding windows closest to the current epoch; N is greater than or equal to 1. Based on the average velocity and the position information of the previous epoch, the current predicted position is determined; Based on the pseudorange and Doppler observations of the current epoch, the velocity and the predicted position are corrected to obtain the velocity and position of the current epoch.

[0007] This application also provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the pseudo-satellite signal environment identification method as described in any embodiment of this disclosure; or to implement the positioning method as described in any embodiment of this disclosure.

[0008] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the application. Other advantages of this application can be realized and obtained by means of the embodiments described in the description and the accompanying drawings. Attached Figure Description

[0009] The accompanying drawings are used to provide an understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0010] Figure 1 A flowchart of a pseudo-satellite signal environment identification method provided in this embodiment of the disclosure; Figure 2 A flowchart of another pseudo-satellite signal environment identification method provided in this disclosure embodiment; Figure 3 A flowchart of another pseudo-satellite signal environment identification method provided in this disclosure embodiment; Figure 4 A flowchart of a positioning method provided in an embodiment of this disclosure; Figure 5 A flowchart of another positioning method provided in an embodiment of this disclosure. Detailed Implementation

[0011] This application describes several embodiments, but these descriptions are exemplary and not limiting, and it will be apparent to those skilled in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with, or may replace, any feature or element of any other embodiment.

[0012] This application includes and contemplates combinations of features and elements known to those skilled in the art. The embodiments, features, and elements disclosed in this application can also be combined with any conventional features or elements to form unique inventive solutions. Any feature or element of any embodiment can also be combined with features or elements from other inventive solutions to form another unique inventive solution. Therefore, it should be understood that any feature shown and / or discussed in this application can be implemented individually or in any suitable combination. Therefore, the embodiments are not limited except by the limitations imposed by the appended claims and their equivalents. Furthermore, various modifications and changes can be made within the scope of the appended claims.

[0013] Furthermore, in describing representative embodiments, the specification may have presented methods and / or processes as a specific sequence of steps. However, the method or process should not be limited to the specific order of steps described herein, to the extent that it does not depend on such a specific order. As will be understood by those skilled in the art, other sequences of steps are also possible. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation of the claims. Moreover, the claims concerning the method and / or process should not be limited to the steps performed in the written order, and those skilled in the art will readily understand that these orders can be varied and still remain within the spirit and scope of the embodiments of this application.

[0014] Before describing the embodiments, the relevant terms involved in the disclosure are explained as follows: Pseudolite: A device placed on the ground that can simulate the navigation signals emitted by GNSS satellites, used to provide enhanced or alternative positioning services in areas with poor satellite signal coverage.

[0015] Doppler shift: The change in signal frequency caused by the relative motion between the satellite and the receiver. In a pseudosatellite environment, because the simulated signal source may be fixed or have different motion patterns, its Doppler characteristics will differ significantly from those of a real satellite.

[0016] Kalman Filter: An optimized autoregressive data processing algorithm used to estimate the state of a dynamic system from a series of incomplete and noisy observations. It requires high continuity and stability of the model.

[0017] P matrix: The state covariance matrix in a Kalman filter, which describes a measure of the uncertainty of the estimate of the current state (such as position or velocity).

[0018] Q-matrix: The process noise covariance matrix in a Kalman filter, which describes the degree of confidence in the accuracy of the system's dynamics model.

[0019] Least Squares (LSQ) is a mathematical optimization technique that finds the best function match for data by minimizing the sum of squared errors. In localization, it solves for the position at each epoch independently, making it more robust but potentially less accurate than continuous filtering.

[0020] Pseudo-satellite positioning systems are commonly used to provide GNSS positioning services in environments where real GNSS signals are blocked (tunnels, indoors, etc.). In pseudo-satellite augmentation or indoor positioning systems, due to the fundamental differences between pseudo-satellites and real satellites, receivers need to be able to identify pseudo-satellite environments. If they cannot distinguish between the two, general-purpose receivers will experience positioning anomalies and discontinuous positioning under pseudo-satellite systems. Some feasible solutions utilize ephemeris information collected by multiple network devices outside the receiver for joint analysis to identify abnormal ephemeris information and thus identify pseudo-satellite data. This solution has high network requirements for the receiver; poor network conditions severely affect the identification effect and real-time performance. Furthermore, the strategy of identifying pseudo-satellites through ephemeris may fail if the ephemeris broadcast by pseudo-satellites is consistent with that broadcast by the current navigation system. Other feasible solutions are based on multi-antenna receivers, measuring the code phase or carrier phase of satellite signals at multiple antennas, calculating the phase difference, determining the signal's direction of arrival, and detecting pseudo-satellite signals accordingly. This scheme requires the receiver to be configured with a multi-antenna array and relies on the code phase information obtained from multiple antennas for analysis. The algorithm is complex, and ensuring real-time processing poses a significant challenge to receiver cost and device miniaturization.

[0021] This disclosure provides a method for identifying pseudosatellite signal environments and a positioning method for pseudosatellite environments, which can improve the positioning performance and reliability of GNSS receivers in pseudosatellite signal environments.

[0022] Research has found that due to the relative radial motion between GNSS satellites and GNSS receivers, the frequency of the carrier signal actually received by the receiver will deviate from the standard frequency transmitted by the satellite; this deviation is called the Doppler shift. GNSS satellite motion exhibits a periodic pattern. Since satellite orbits can be accurately described by ephemeris data, combined with the approximate position of the receiver, the Doppler shift caused by satellite motion can be calculated. The Doppler shift caused by the same satellite exhibits a smooth sinusoidal curve over a long period. Because the radial velocity of the satellite relative to the receiver's line-of-sight is relatively slow, the rate of change of the Doppler shift is small, typically within 1 Hz / s.

[0023] The magnitude of the Doppler frequency shift caused by receiver motion is directly related to the receiver's speed and direction of motion. Under normal sky-gazing conditions, the receiver moves at a speed... During motion, the Doppler frequency shift caused by the receiver's motion is approximately... , It is the wavelength of the satellite signal. It is the angle between the receiver's velocity vector and the satellite's line-of-sight vector, and the angle between the satellite signal at different azimuth and elevation angles. different.

[0024] The carrier frequency transmitted by the signal transmitter of the pseudo-satellite system is the frequency of the real carrier signal received at the position of the signal transmitter when the signal transmitter is stationary. The frequency of the pseudo-satellite carrier signal received by the receiver is the frequency after superimposing the Doppler frequency shift caused by the relative radial motion of the receiver and the pseudo-satellite signal transmitter on this frequency.

[0025] In a pseudosatellite signal environment, a pseudosatellite signal transmitter will simultaneously transmit signals from multiple satellites. The angle between the receiver's velocity vector and the line-of-sight vector between the receiver and the signal transmitter is the same. The Doppler frequency shift caused by the receiver's motion in all pseudosatellite signals is consistent. This characteristic can be used as the core basis for identifying pseudosatellite signal environments.

[0026] In pseudosatellite positioning systems, multiple pseudosatellite transmitters broadcast simulated signals from the same satellite, resulting in partial overlap in their coverage areas. This broadcasting characteristic, combined with multipath effects, means the same satellite signal reaches the receiver antenna via different paths and with varying intensities, leading to signal superposition and attenuation. This instability in the carrier-to-noise ratio (CNR) causes frequent large fluctuations in Doppler observations, with persistently large inter-epoch differences in Doppler observations. Under normal receiver movement, there are no persistently large inter-epoch differences in Doppler observations; this characteristic serves as a primary basis for identifying pseudosatellite signal environments.

[0027] Based on the aforementioned characteristics of the pseudo-satellite signal environment, the Doppler frequency shift values ​​caused by receiver motion of all satellites are obtained. Through consistency checks, the concentration of signal directions is confirmed, serving as the core basis for judging the pseudo-satellite signal environment. The difference between the smoothed Doppler value and the instantaneous value of a single satellite is calculated, and the abnormal Doppler state values ​​of a single satellite are adjusted. Then, the proportion of problematic satellites is statistically analyzed, serving as a subordinate basis for judging the pseudo-satellite signal environment.

[0028] This disclosure provides a method for identifying pseudo-satellite signal environments, applied to GNSS receivers, such as... Figure 1 As shown, it includes: Step 110: Obtain the Doppler residual values ​​of multiple satellite signal channels received by the GNSS receiver; Step 120: If the Doppler residual values ​​of the multiple satellite signal channels are consistent, it is determined that the current environment is a pseudo-satellite signal environment.

[0029] The offset of the carrier frequency transmitted by the signal transmitter of the pseudosatellite system from the standard frequency is as follows: (1); in, The offset of the carrier frequency transmitted by the signal source from the standard frequency, expressed in Hz; This refers to the actual transmission frequency of the satellite signal, measured in Hz. The actual three-dimensional velocity vector of the satellite is calculated from the satellite ephemeris. The vector represents the three-dimensional velocity vector of the pseudo-satellite signal transmitter. The transmitter is stationary and is represented by a zero vector. This represents the unit observation vector of the satellite at the pseudo-satellite signal source. c It is the speed of light. Therefore, a real satellite is a true satellite relative to a pseudo-satellite.

[0030] In a pseudosatellite signal environment, the Doppler observation of the receiver, which measures the carrier frequency of the satellite signal transmitted by the pseudosatellite signal source relative to the standard frequency, is as follows: (2); in, These are the Doppler observations from the receiver; The receiver's three-dimensional velocity vector; This represents the unit observation vector of the pseudosatellite signal transmitter at the receiver. To account for receiver clock frequency drift, it is necessary to estimate and ensure that the frequency remains stable over a short period of time.

[0031] It should be noted that a satellite can transmit one or more frequency signals, with each frequency corresponding to a satellite signal channel. In the embodiments of this disclosure, the satellite signal channel is used as the basic unit for satellite signal processing in the receiver. In the embodiments of a single-frequency receiver, the number of satellite signal channels is the same as the number of satellites, and the relevant data of one satellite signal channel is equivalent to the relevant data of one satellite. When applied to a multi-frequency receiver, the satellite signal channel can be used as the basic processing unit.

[0032] In real-world signal environments, velocity measurements can be used to calculate receiver clock frequency drift. In pseudosatellite systems, pseudorange positioning can be used to obtain the approximate location of the pseudosatellite signal source, and the direction vector from the satellite to the pseudosatellite signal source can be calculated. Based on the time information obtained after positioning, the satellite's three-dimensional velocity vector at the time of signal transmission is calculated. The only unknown in formula (2) is... and Formula (2) can be expressed as: (3); Indicates that the receiver is at The Doppler residual value caused by the motion component on the satellite, in a pseudosatellite signal environment, since all pseudosatellite signals are emitted from the same signal source, the Doppler residual values ​​of each satellite... Basically the same, receiver three-dimensional velocity vector It is consistent across all satellite channels. For all satellites... A consistency check is performed; if it passes, it indicates that the current satellite signals are consistent. The values ​​are basically consistent, which is consistent with the signal characteristics of a pseudo-satellite signal source.

[0033] In some exemplary embodiments, the multiple satellite signal channels in step 110 can be all the satellite signal channels of observable satellites, or multiple satellite signal channels consisting of one satellite signal channel from each observable satellite. Observable satellites are those whose signal quality meets the receiver's signal processing requirements; specific signal quality standards are not discussed in detail here.

[0034] In some exemplary embodiments, the Doppler residual value of a satellite signal channel is calculated according to the following method: ; in, The value represents the Doppler residual. These are Doppler observations. For clock frequency drift, The offset of the carrier frequency transmitted by the signal source from the standard frequency. f This refers to the actual satellite signal transmission frequency.c It is the speed of light.

[0035] In some exemplary embodiments, consistency detection is performed on the Doppler residual values ​​of the plurality of satellite signal channels. The coefficient of variation can be calculated based on the standard deviation and average value of the Doppler residual values ​​of the plurality of satellite signal channels, and consistency can be determined by the coefficient of variation. Alternatively, other methods can be used to determine the consistency of the Doppler residuals, which are not limited to specific aspects of the examples in this disclosure.

[0036] In some exemplary embodiments, such as Figure 2 As shown, the method further includes: Step 130: Obtain the Doppler anomaly values ​​of the multiple satellite signal channels respectively. And determine the Doppler anomaly values. The percentage of satellite signal channels exceeding the second state threshold Th2 The Doppler anomaly value indicates the cumulative jump amplitude of the Doppler observations in the satellite signal channel. Step 140, based on the proportion of the number of satellite signal channels Determine the abnormal environmental state value ; wherein, the environmental abnormal state value Indicates the percentage of satellite signal channels of the GNSS receiver. The cumulative amount exceeding the third proportional threshold Th3; Step 150: The Doppler residual values ​​of the multiple satellite signal channels are consistent, and the environmental anomaly value... If the value is greater than the fourth threshold Th4, it is determined that the current environment is a pseudo-satellite signal environment.

[0037] The Doppler abnormal state value Indicates the cumulative jump amplitude of Doppler observations for this satellite signal channel; Doppler anomaly values. The initial value is 0. Doppler anomaly state value. When the accumulated jump amplitude exceeds the second state threshold Th2, it indicates a Doppler anomaly in the satellite signal channel. The second state threshold Th2 is preset, representing the Doppler anomaly state value. The threshold will vary depending on the calculation method, and is not limited to any specific aspect.

[0038] Among them, the jump variables of Doppler observations (4); These are smoothed Doppler observations. These are Doppler observations.

[0039] In some exemplary embodiments, each satellite signal channel maintains a Doppler smoothing value smoothing time stamp. The initial value is 0. If the actual tracking time of the current channel... and If the difference exceeds the observation acquisition interval, it indicates that the smoothed Doppler observation values ​​for the current channel are invalid, and the smoothing time stamp needs to be adjusted. Smoothed values ​​of Doppler observations Perform initialization: (5); (6); If the current Doppler observation smoothing value is valid, or if it has been initialized in the case of invalid Doppler observations, then the Doppler observation smoothing value is updated using the Doppler observation value of the current epoch, as follows: (7); in, and Let be the weight coefficient, and satisfy... .

[0040] This yields the smoothed Doppler observation value for each tracking time, i.e., each epoch. Then, based on the Doppler observation value of that epoch, the jump variable for the current time / epoch of the Doppler observation value is calculated. .

[0041] In some exemplary embodiments, The larger the jump variable, the faster the accumulation rate; the smaller the jump variable, the slower the accumulation rate.

[0042] In some exemplary embodiments, Th1 is a preset threshold for the first jump variable, representing the acceptable range of jumps in normal Doppler observations. In other words, if the jump variable exceeds the acceptable range, the accumulated amount increases; if it falls below the acceptable range, the accumulated amount decreases. A jump variable greater than Th1 and an increased accumulated amount indicate an abnormal Doppler state for the satellite signal channel; a jump variable less than Th1 and a decreased accumulated amount indicate an improving Doppler state for the satellite signal channel.

[0043] In some exemplary embodiments, the Doppler abnormal state value Determined using the following method: Jump variables in Doppler observations If the value is greater than or equal to the first-hop variable threshold Th1, (8); Jump variables in Doppler observations If the value is less than the first-hop variable threshold Th1, For example, A=1, or other preset values. It can be seen that in jump variables... If the cumulative amount is greater than or equal to the first-hop variable threshold Th1, the cumulative amount is determined according to... The accumulation is performed, and if the value is less than Th1, the accumulated amount is reduced according to a preset value A. In some exemplary embodiments, A is set to a small value; for example, A is compared to most... If all values ​​are small, it means that in Doppler anomalous state values ​​when the jump range exceeds acceptable limits The increase was less than Within an acceptable range of jumps, Doppler anomalous state values The magnitude of the change is reduced. Therefore, by continuously tracking the jump variables, multiple out-of-range jumps can be effectively identified, while occasional no-jump or small-amplitude jumps can be avoided from affecting the overall anomaly identification.

[0044] As can be seen, the Doppler anomaly values ​​of each satellite signal channel Jump variables based on continuously tracked Doppler observations Sure, When the value is greater than Th2, the satellite signal channel is considered to be in an abnormal Doppler state; this process is also known as jump detection.

[0045] Step 130 is executed on multiple satellite signal channels. After the transition detection is completed, the Doppler anomaly values ​​of all satellite signal channels are analyzed. Perform statistical analysis, proceeding to step 140. In step 140, the environmental anomaly value... Indicates the percentage of satellite signal channels of the GNSS receiver. The cumulative amount exceeding the third proportional threshold Th3; that is, after completing step 130 of the anomaly detection based on jump variables for multiple satellite signal channels, a statistical value representing the overall state of multiple satellite signal channels is obtained, i.e., the anomaly state value of the environment in which the receiver is located. .

[0046] In some exemplary embodiments, environmental abnormal state values Equal to the Doppler anomaly state value in the current multiple satellite signal channels The percentage of satellite signal channels exceeding the second state threshold Th2 That is, the proportion The larger the value, the more abnormal the environmental state. The larger it is. It can be seen that at this point, the proportion of the current number of abnormal satellite signal channels... The comparison with the fourth threshold Th4 serves as an auxiliary criterion for determining the pseudo-satellite environment. At this point, = This can be considered as Th3 being 0, and the accumulated duration only includes the accumulated amount of the current epoch.

[0047] In some exemplary embodiments, the environmental abnormal state value Determined using the following method: Method 1: Percentage of satellite signal channels When the value is greater than the third proportional threshold Th3, (9); Where B is a preset constant, for example, B=1 or B=100.

[0048] Alternatively, method two: (9).

[0049] Alternatively, method three: In cases greater than Th3, (9); When it is less than Th3, .For example, B =100, C =1. C Set it to a smaller value, for example, C Compared to most If all values ​​are small, it means that in When the environmental abnormal state value exceeds the third proportional threshold Th3, The increase was less than Environmental anomaly value when not exceeding the third proportional threshold Th3 The magnitude of the reduction. Therefore, by continuously tracking the proportion of abnormal satellite channels, we can effectively identify pseudo-satellite environments while avoiding the impact of occasional small percentages of abnormal satellite channels on the overall identification of pseudo-satellite environments.

[0050] As can be seen, among the three calculation methods mentioned above, including formula (9), from The accumulation starts from 0 and continues continuously. The rate of increase or decrease in the accumulated amount varies depending on the method used. Accordingly, the fourth threshold should be determined accordingly when using different calculation methods.

[0051] This disclosure also provides a method for identifying pseudosatellite signal environments, applied to a single-frequency GNSS receiver, where the number of observable satellites is M, including satellites numbered 1 to M. Figure 3 As shown, it includes: Step 300, Data Initialization: Set the initial values ​​for each satellite: =0; =0; Step 310: Obtain the Doppler observation value of the i-th satellite; Step 320: Update the Doppler smoothing value of the i-th satellite; Step 330: Calculate the jump variables of the Doppler observations of the i-th satellite. ; Step 340, determine jump variables Is it greater than Th1? If it is, proceed to step 350; otherwise, proceed to step 360. Step 350: Increase the Doppler anomaly value of the i-th satellite. ; Step 360: Reduce the Doppler anomaly value of the i-th satellite. ; Step 370: Determine the Doppler residual value of the i-th satellite; Step 380: Determine if i is less than M; if yes, proceed to step 3100; otherwise, i = i + 1, and proceed to steps 310-380 again. Step 3100: Based on the Doppler anomaly values ​​of all satellites... Statistical analysis of Doppler abnormal state values Percentage of satellite signal channels greater than Th2 ; Step 3110, based on the current quantity percentage Update environmental abnormal status values ; Step 3120: Determine whether the Doppler residual values ​​of the M satellites are consistent. If they are, proceed to step 3130; otherwise, determine that the satellites are not in a pseudo-satellite environment. Step 3130: Determine the abnormal environmental state value. If the value is greater than Th4, then it is determined that the environment is in a pseudo-satellite environment; otherwise, it is determined that the environment is not in a pseudo-satellite environment. Step 3140, wait for the next recognition: i is initialized to 1, and the above recognition steps are executed again starting from step 310.

[0052] In some exemplary embodiments, Figure 3 The pseudo-satellite signal environment identification method shown can also be applied to multi-frequency GNSS receivers. Accordingly, steps 310-380 are executed sequentially for the M1 satellite signal channels of M observable satellites. In step 380, the signal is compared with M1 to determine whether all satellite signal channels have been processed. Then, steps 3100-3140 are executed based on the Doppler anomaly values ​​of all M1 satellite signal channels. Further processing is then performed. Where M1 is greater than or equal to M.

[0053] It should be noted that the pseudo-satellite signal environment identification scheme provided in this embodiment is executed periodically according to the set execution interval. The execution time point can be consistent with the satellite observation tracking time point, or a larger execution interval can be used compared to the satellite observation tracking interval. During the operation of the GNSS receiver, the execution interval can also be flexibly adjusted as needed, and is not limited to specific aspects.

[0054] according to Figure 1 , Figure 2 or Figure 3 The method shown can determine whether the receiver is currently in a pseudosatellite signal environment. Accurately identifying the receiver's current satellite signal environment allows for targeted adjustments to the positioning algorithm. In a normal, real-world satellite signal environment, receiver motion typically uses the previous moment's positioning and velocity measurement results to predict the current position, then corrects the position and velocity using the actual measured pseudorange and Doppler readings. In a pseudosatellite signal environment, pseudosatellite signals are all emitted from the same source, rendering the original velocity measurement method based on the actual satellite distribution ineffective. The receiver cannot obtain the correct velocity magnitude and direction from Doppler observations. Pseudorange frequently exhibits errors of tens to hundreds of meters, but can be used for positioning after satellite selection and weighting.

[0055] In pseudosatellite signal environments, to reduce the impact of invalid Doppler observations on positioning, it is necessary to adjust the state prediction and state estimation methods to accurately estimate position information. When outputting positioning information, stricter quality checks and adjustments to the positioning reliability are required.

[0056] This disclosure also provides a positioning method applied to a GNSS receiver, such as... Figure 4 As shown, it includes: Step 410: Determine whether the current environment is a pseudo-satellite environment; Step 420: If it is determined that the environment is a pseudo-satellite environment, calculate the average velocity of the GNSS receiver based on the position information of the N historical sliding windows closest to the current epoch; N is greater than or equal to 1; Step 430: Determine the current predicted position based on the average velocity and the position information of the previous epoch; Step 440: Based on the pseudorange and Doppler observations of the current epoch, correct the velocity and the predicted position to obtain the velocity and position of the current epoch.

[0057] In step 410, the determination is made according to the pseudo-satellite environment identification method provided in any embodiment of this disclosure.

[0058] In some exemplary embodiments, the positioning method further includes: Step 4100: If it is determined that the environment is not a pseudo-satellite environment, the current position is predicted using the positioning and velocity measurement results of the previous moment, and then the position and velocity are corrected using the pseudorange and Doppler observations of the current epoch.

[0059] Therefore, based on the effective identification of the pseudo-satellite environment, the positioning strategy (algorithm) was adjusted, which can effectively improve the positioning accuracy in response to the characteristics of the pseudo-satellite signal environment.

[0060] In a pseudosatellite environment, the receiver cannot obtain the correct velocity magnitude and direction from Doppler observations. To obtain a usable predicted position, the average velocity of the GNSS receiver can be calculated based on the position information of the previous N historical sliding windows closest to the current epoch, where each sliding window includes n epochs. In some exemplary embodiments, N=2, that is, the average velocity is calculated using the position information of each of the previous two historical sliding windows (n epochs). Specifically, the receiver's average velocity is calculated using the average positioning position determined within the last 1 to n epochs and the average positioning position within the last n+1 to 2n seconds, and the average velocity is used to predict the position.

[0061] (10); in, The average velocity of the GNSS receiver, also known as the predicted position change vector, This represents historical location information, where the subscript number i indicates the location information of the most recent epoch.

[0062] Alternatively, other algorithms may be used to calculate the average velocity based on the position information determined by the epochs included in the first N historical sliding windows, not limited to N=2 in the example above, and the method shown in formula (10).

[0063] Based on the average velocity and the position information of the previous epoch (time), the position at the current time is predicted, and the current predicted position is obtained.

[0064] In some exemplary embodiments, step 440 employs one of the following strategies to correct the velocity and position at the current epoch (time): a Doppler observation weight reduction strategy, a Doppler observation not participating in position and velocity correction strategy, or a position and velocity correction strategy using the least squares method.

[0065] In some exemplary embodiments, a Doppler observation weight reduction strategy is adopted, in which Kalman filtering is used for state estimation in the solution, but the weights of model predictions and Doppler observations are reduced.

[0066] Step 430 adjusts the position prediction method. The accuracy of this predicted position will be slightly lower than that based on velocity measurements under normal actual satellite conditions. Therefore, empirical values ​​need to be appropriately added to the position and velocity dimensions of the process noise matrix (Q matrix). The off-diagonal values ​​of the error covariance matrix (P matrix) represent the correlation between state errors. Clearing the values ​​in the position-velocity and position-acceleration dimensions of the P matrix reduces the confidence in the velocity state. By increasing the observation noise variance of Doppler observations, the weight of Doppler observations is reduced, thus implementing the strategy of reducing the weight of Doppler observations in the correction process.

[0067] In some exemplary embodiments, a strategy is adopted in which Doppler observations are not used for position and velocity correction, also known as a strategy in which Doppler observations are not used for state update. That is, Kalman filtering is used for state estimation, but Doppler observations are not used to update the state. The method is as follows: The position and velocity dimensions of the process noise matrix (Q matrix) are appropriately increased with empirical values, and the submatrices corresponding to the Doppler observations are removed from the observation vector, observation matrix, and observation noise matrix. Only the valid pseudorange observations are used for position and velocity state updates.

[0068] In some exemplary embodiments, a position and velocity correction strategy using the least squares method is employed, i.e., the least squares method (LSQ) is used for state estimation. Position parameters are estimated using only pseudorange observations, ensuring that the position parameters are not affected by invalid Doppler observations. The pseudorange observations are as follows: (11); in, Indicates the pseudorange measurement value; Indicates the geometric distance between the satellite and the receiver; Indicates receiver clock bias; Indicates satellite clock bias; Indicates tropospheric error; Indicates ionospheric error; This indicates the noise level being measured; all units are meters.

[0069] As can be seen, in the case of identifying pseudo-satellite environments, in order to reduce the impact of invalid Doppler observations on positioning, one of the above three strategies is used to adjust the position and velocity correction algorithm (also known as the receiver state estimation algorithm) under normal actual satellite environments, thereby improving the accuracy of position and velocity calculation.

[0070] In some exemplary embodiments, such as Figure 5 As shown, the positioning method further includes: Step 450: Obtain the residual statistics for the current epoch, wherein the residual statistics include at least one of the following: the sum of squared residuals after the apostulate. The average of the absolute values ​​of the observed residuals ; Step 460: If the current positioning accuracy is determined to be substandard based on the residual statistics, output control is performed on the corrected state data of the current epoch. The state data includes at least one of the following: speed and position; the output control includes one of the following: no output, output invalid value, or reduce the confidence level of the output data.

[0071] In some exemplary embodiments, a positioning accuracy is determined to be substandard if any of the following conditions are met: Sum of Squared Residuals After Verification Greater than the fifth threshold; The average of the absolute values ​​of the observed residuals It is greater than the sixth threshold.

[0072] In some exemplary embodiments, after the position and velocity corrections are completed in step 440, the sum of squared residuals is calculated: (12); in, For the observed residual vector, This is the weight matrix.

[0073] In some exemplary embodiments, when Greater than the predetermined threshold 5Th5, or, When the value exceeds a predetermined threshold of 6Th6, it indicates that the observed values ​​generally contain errors, the positioning accuracy is poor, and the accuracy requirement is not met. In this case, output control is applied to the velocity and / or position obtained in step 440. In some exemplary implementations, output control includes: not outputting the position externally and setting the externally output velocity to an invalid value.

[0074] In some exemplary embodiments, when Greater than Th5, and, When the value is greater than Th6, output control of the velocity and / or position obtained in step 440 includes: reducing the confidence level of the output data. For example, for the output... Value multiplied by l This is repeated several times to reduce the confidence level of the positioning results.

[0075] Accordingly, if neither of the above two conditions is met, and the positioning accuracy is determined to be up to standard, then the position and velocity obtained after correction in step 440 will be output.

[0076] This disclosure also provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the pseudo-satellite signal environment identification method as described in any embodiment of this disclosure.

[0077] This disclosure also provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the positioning method as described in any embodiment of this disclosure.

[0078] In some exemplary embodiments, the electronic device is a GNSS receiver. This includes smartphones, vehicle-mounted terminals, mobile IoT devices, drones, robots, etc.

[0079] The identification scheme provided in this disclosure enables GNSS receivers to promptly perceive environmental changes and accurately and quickly identify when they are in a pseudo-satellite signal environment, providing a reliable basis for subsequent data processing based on different environmental scenarios. Based on the identified pseudo-satellite environment, targeted adjustments to the positioning algorithm strategy are adopted, effectively optimizing the anomalies and discontinuities in positioning and velocity measurement results caused by pseudo-satellite signals, ensuring that the receiver can still provide usable and reliable positioning results in a pseudo-satellite signal environment.

[0080] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term "computer storage medium" includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0081] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for identifying pseudo-satellite signal environments, applied to a GNSS receiver, characterized in that, include: Obtain the Doppler residual values ​​of multiple satellite signal channels received by the GNSS receiver; If the Doppler residual values ​​of the multiple satellite signal channels are found to be consistent, it is determined that the current environment is a pseudo-satellite signal environment.

2. The method according to claim 1, characterized in that, The method further includes: acquiring the Doppler anomaly values ​​of the plurality of satellite signal channels respectively. And determine the Doppler anomaly values. The percentage of satellite signal channels exceeding the second state threshold Th2 ; wherein, the Doppler abnormal state value Indicates the cumulative jump amplitude of Doppler observations for this satellite signal channel; Based on the proportion of the number of satellite signal channels Determine the abnormal environmental state value ; wherein, the environmental abnormal state value Indicates the percentage of satellite signal channels of the GNSS receiver. The cumulative amount exceeding the third proportional threshold Th3; The Doppler residual values ​​of the multiple satellite signal channels are consistent, and the environmental anomaly values ​​are... If the value is greater than the fourth threshold Th4, it is determined that the current environment is a pseudo-satellite signal environment.

3. The method according to claim 1 or 2, characterized in that, The Doppler residual value of the satellite signal channel is calculated according to the following method: ； in, The value represents the Doppler residual. These are Doppler observations. For clock frequency drift, The offset of the carrier frequency transmitted by the signal source from the standard frequency. f This refers to the actual satellite signal transmission frequency. c It is the speed of light.

4. The method according to claim 2, characterized in that, The Doppler abnormal state value Determined using the following method: Jump variables in Doppler observations If the value is greater than or equal to the first-hop variable threshold Th1, ; Jump variables in Doppler observations If the value is less than the first-hop variable threshold Th1, ; in, , These are smoothed Doppler observations. The values ​​are Doppler observations, and A is a preset constant.

5. The method according to claim 4, characterized in that, The smoothed value of the Doppler observations Update using the following method: ； in, and Let be the weight coefficient, and satisfy... .

6. The method according to claim 2, characterized in that, The environmental abnormal state value Determined using the following method: Percentage of satellite signal channels When the value is greater than the third proportional threshold Th3, B is a preset constant.

7. A positioning method applied to a GNSS receiver, characterized in that, include: Determine whether the current environment is a pseudo-satellite environment according to the method described in any one of claims 1-6; When it is determined that the environment is a pseudo-satellite environment, the average velocity of the GNSS receiver is calculated based on the position information of the N historical sliding windows closest to the current epoch. N is greater than or equal to 1; Based on the average velocity and the position information of the previous epoch, the current predicted position is determined; Based on the pseudorange and Doppler observations of the current epoch, the velocity and the predicted position are corrected to obtain the velocity and position of the current epoch.

8. The method according to claim 7, characterized in that, The corrected velocity and the predicted position are used to obtain the velocity and position of the current epoch, employing one of the following strategies: The strategies include reducing the weight of Doppler observations, excluding Doppler observations from position and velocity correction, and using the least squares method for position and velocity correction.

9. The method according to claim 7 or 8, characterized in that, The method further includes: obtaining the residual statistics for the current epoch, wherein the residual statistics include at least one of the following: the sum of squared residuals after the apostulate. The average of the absolute values ​​of the observed residuals ; If the current positioning accuracy is determined to be substandard based on the residual statistics, the corrected state data of the current epoch is used for output control. The state data includes at least one of the following: speed and position; the output control includes one of the following: no output, output invalid value, or reduce the confidence level of the output data.

10. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the pseudo-satellite signal environment identification method as described in any one of claims 1-6; or to implement the positioning method as described in any one of claims 7-9.

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