Satellite signal interference identification method and device, equipment and storage medium

Abstract

The application discloses a satellite signal interference identification method, device and equipment and a storage medium. The method comprises the following steps: in response to the global navigation satellite system module of a target device acquiring ephemeris data of at least one satellite, performing a dead reckoning operation according to dead reckoning data of the target device to obtain a target reckoning position of the target device, wherein the ephemeris data of the at least one satellite comprises ephemeris data of at least one satellite; performing an interference identification operation on each satellite in the at least one satellite to obtain an interference identification result of each satellite, wherein the interference identification operation comprises the following steps: determining a theoretical pseudo-range of the satellite according to the target reckoning position and the ephemeris data of the satellite; and determining the interference identification result of the satellite according to the theoretical pseudo-range and a measured pseudo-range of the satellite, wherein the interference identification result is used to represent whether the satellite signal is interfered. In this way, redundant auxiliary data can be fully utilized to accurately identify whether the satellite signal is interfered.

Description

Technical Field

[0001] This application belongs to the field of navigation and positioning technology, and in particular relates to a method, device, equipment and storage medium for identifying interference with satellite signals. Background Technology

[0002] With the rapid development of navigation and positioning technology, the Global Navigation Satellite System (GNSS) is widely used in the navigation and positioning of various mobile devices such as vehicles, ships, and aircraft. In the GNSS positioning calculation process, the receiver calculates the pseudorange by measuring the propagation time of the satellite signal, and then determines the receiver's position through a set of analytical equations.

[0003] However, in complex scenarios, such as urban canyons, under overpasses, and tunnel entrances / exits, satellite signals are susceptible to obstruction, reflection, and multipath effects, leading to significant pseudorange errors in some satellite signals. Using interfering satellite signals for positioning calculations can cause problems such as jumps, deviations, or slow convergence in the positioning results, severely impacting the accuracy of navigation and positioning.

[0004] Therefore, how to accurately identify interference in satellite signals has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] This application provides a method, apparatus, device, and storage medium for identifying interference in satellite signals, which can accurately identify whether interference exists in satellite signals.

[0006] In a first aspect, embodiments of this application provide a method for identifying interference with satellite signals, comprising: In response to the target device's Global Navigation Satellite System module acquiring ephemeris data from at least one satellite, dead reckoning is performed based on the target device's dead reckoning data to obtain the target device's estimated position. An interference identification operation is performed on each of the at least one satellite to obtain an interference identification result for each satellite. The interference identification operation includes: determining the theoretical pseudorange of the satellite based on the estimated position of the target and the ephemeris data of the satellite; and determining the interference identification result of the satellite based on the theoretical pseudorange and the measured pseudorange of the satellite. The interference identification result is used to characterize whether there is interference in the satellite signal.

[0007] Secondly, embodiments of this application also provide a satellite signal interference identification device, comprising: The dead reckoning module is used to perform dead reckoning based on the dead reckoning data of the target equipment and obtain the target estimated position of the target equipment in response to the global navigation satellite system module of the target equipment acquiring ephemeris data of at least one satellite. An interference identification module is used to perform an interference identification operation on each of the at least one satellite to obtain an interference identification result for each satellite. The interference identification operation includes: determining the theoretical pseudorange of the satellite based on the estimated position of the target and the ephemeris data of the satellite; and determining the interference identification result of the satellite based on the theoretical pseudorange and the measured pseudorange of the satellite. The interference identification result is used to characterize whether there is interference in the satellite signal.

[0008] Thirdly, embodiments of this application also provide a satellite interference identification device, the satellite interference identification device comprising: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the satellite signal interference identification method as described in any of the first aspects.

[0009] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the satellite signal interference identification method as described in any one of the first aspects.

[0010] Fifthly, embodiments of this application also provide a computer program product, wherein instructions in the computer program product, when executed by the processor of a satellite interference identification device, cause the satellite interference identification device to implement the satellite signal interference identification method as described in any one of the first aspects.

[0011] In this embodiment, the ephemeris data of at least one satellite is acquired in response to the target device's Global Navigation Satellite System module. Dead reckoning is then performed based on the target device's dead reckoning data to obtain the target device's estimated position. Interference identification is then performed on each of the at least one satellite to obtain an interference identification result for each satellite. This interference identification operation includes: determining the satellite's theoretical pseudorange based on the estimated target position and the satellite's ephemeris data; and determining the satellite's interference identification result based on the satellite's theoretical and measured pseudoranges. The interference identification result is used to characterize whether interference exists in the satellite signal. Thus, in the target device's navigation and positioning, redundant auxiliary data is fully utilized to accurately identify whether interference exists in the satellite signal. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the architecture of the target device provided in the embodiments of this application; Figure 2 This is a flowchart illustrating an embodiment of the satellite signal interference identification method provided in this application; Figure 3 This is a schematic diagram of an embodiment of the satellite signal interference identification device provided in this application; Figure 4 This is a schematic diagram of an embodiment of the satellite interference identification device provided in this application. Detailed Implementation

[0014] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples of this application.

[0015] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0016] In related technologies, with the rapid development of navigation and positioning technology, the Global Navigation Satellite System (GNSS) is widely used in the navigation and positioning of various mobile devices such as vehicles, ships, and aircraft. In the GNSS positioning calculation process, the receiver calculates the pseudorange by measuring the propagation time of the satellite signal, and then determines the receiver's position through a set of analytical equations.

[0017] However, in complex scenarios, such as urban canyons, under overpasses, and tunnel entrances / exits, satellite signals are susceptible to obstruction, reflection, and multipath effects, leading to significant pseudorange errors in some satellite signals. Using interfering satellite signals for positioning calculations can cause abrupt changes, deviations, or slow convergence in the positioning results, severely impacting navigation and positioning accuracy. Therefore, accurately identifying interfering satellite signals has become a pressing technical problem to be solved in this field.

[0018] Based on this, embodiments of this application provide a method, apparatus, device, and storage medium for identifying interference in satellite signals. By combining the dead reckoning (DR) data of the target device with the ephemeris data of at least one satellite received by the GNSS module, the theoretical pseudorange of each satellite is calculated using the DR-calculated position as a reference. The theoretical pseudorange is then compared with the measured pseudorange to accurately identify interfering satellite signals. In this way, redundant auxiliary data is fully utilized in the navigation and positioning of the target device to accurately identify whether satellite signals are interfering. This allows subsequent positioning calculations to be optimized based on the identification results (e.g., removing or reducing the weight of interfering satellite signals during positioning calculations). This effectively reduces the occurrence of positioning jumps, slow convergence, and sharp drops in accuracy caused by satellite signal obstruction and multipath effects in complex scenarios (such as urban canyons, under overpasses, tunnel exits, initialization, etc.).

[0019] It should be noted that, as Figure 1 As shown, the target device can be any device equipped with a GNSS module and a DR module for acquiring DR data, such as a vehicle. This application primarily uses a vehicle as an example for illustration, but this application does not limit the scope of the application.

[0020] The GNSS module can be a receiver module of a single system or a combination of systems such as the Global Positioning System (GPS), BeiDou Navigation Satellite System, GLONASS, and Galileo.

[0021] The DR module may include an Inertial Measurement Unit (IMU) module and a Controller Area Network (CAN) module, etc. That is, DR data may include at least one of the following: odometer data, IMU data (such as angular velocity measured by gyroscope, acceleration measured by accelerometer), movement speed data, and azimuth angle data.

[0022] Please see Figure 2 This is a flowchart illustrating the satellite signal interference identification method provided in an embodiment of this application. Figure 2 As shown, the above-mentioned satellite signal interference identification method includes, but is not limited to, the following steps S201 to S202.

[0023] Step S201: In response to the target device's GNSS module acquiring ephemeris data from at least one satellite, dead reckoning is performed based on the DR data to obtain the target's estimated position.

[0024] In this step, the ephemeris data of the aforementioned at least one satellite may include ephemeris data received by the GNSS module at any sampling time (hereinafter referred to as the "first time"), which refers to the moment when the GNSS module successfully acquires the satellite signal and obtains valid ephemeris data. For example, the ephemeris data of the aforementioned at least one satellite may be ephemeris data received at the current or the most recent sampling time, that is, the aforementioned first time is the current or the most recent sampling time.

[0025] It should be noted that the ephemeris data of at least one satellite may also include ephemeris data received by the GNSS module at multiple sampling times, that is, the first time mentioned above includes the multiple sampling times. Since the processing of ephemeris data received at multiple sampling times is similar to that of ephemeris data received at any one sampling time, this application only uses the ephemeris data received at any time for explanation, and will not elaborate further here.

[0026] Ephemeris data describes the orbital and status parameters of a satellite. It can include Keplerian orbital parameters such as the satellite's semi-major axis, eccentricity, orbital inclination, right ascension of the ascending node, perigee distance, and mean perigee angle, as well as clock parameters such as clock bias and clock speed. GNSS modules can obtain this ephemeris data by parsing navigation messages.

[0027] DR data is data collected by the DR module that can be used for dead reckoning. DR data mainly consists of motion status data of the target equipment, which may include at least one of the following: odometer data, IMU data (such as angular velocity measured by a gyroscope, acceleration measured by an accelerometer), speed data, and azimuth data.

[0028] Target position estimation refers to predicting the position of a target device at the aforementioned first moment using dead reckoning. The basic principle of dead reckoning is to calculate the target device's position at the first moment based on its known position at the previous moment, combined with its motion state information (such as speed and heading angle).

[0029] For example, assuming the vehicle's position at time t0 is (x0, y0, z0), and its speed at time t1 (i.e., the first time) is v and its heading angle is θ, the position at time t1 (x1, y1, z1) can be calculated through integration.

[0030] Step S202: Perform interference identification operation for each of at least one satellite to obtain the interference identification result for each satellite. The interference identification operation includes: determining the theoretical pseudorange of the satellite based on the target's estimated position and the satellite's ephemeris data; determining the interference identification result of the satellite based on the satellite's theoretical pseudorange and measured pseudorange. The interference identification result is used to characterize whether there is interference in the satellite signal.

[0031] In this step, the theoretical pseudorange mentioned above refers to the theoretical pseudorange value calculated based on the target's estimated position and the satellite's position.

[0032] Specifically, the calculation process of this theoretical pseudorange may include: first, calculating the three-dimensional coordinate position of each satellite based on ephemeris data, wherein the orbital parameters in the ephemeris data can be used to solve for the satellite position using Kepler equations, and then corrected by combining the satellite clock error parameters to obtain the precise position of the satellite at the time of signal transmission; then, calculating the geometric distance between the target's estimated position and the position of each satellite, and adding the estimated values ​​of error terms such as receiver clock error, ionospheric delay, and tropospheric delay to obtain the theoretical pseudorange.

[0033] The aforementioned measured pseudorange refers to the pseudorange value actually measured by the GNSS module, which is the product of the satellite signal propagation time and the speed of light, plus the influence of error terms such as receiver clock bias.

[0034] The interference identification results described above are used to characterize whether the corresponding satellite signal is interfered with. Specifically, the difference between the theoretical pseudorange and the measured pseudorange (i.e., the pseudorange difference) can be calculated, and then this pseudorange difference can be compared with a preset threshold. If the pseudorange difference exceeds the threshold, the satellite signal is considered to be possibly interfered with; otherwise, the satellite signal is considered to be normal.

[0035] For example, assuming a satellite's theoretical pseudorange is ρcalc and its measured pseudorange is ρmeas, the pseudorange difference Δρ = |ρcalc - ρmeas|. If Δρ > TH (TH is a preset threshold), the satellite's signal is considered to be interfered with; if Δρ ≤ TH, the satellite's signal is considered to be normal. The threshold TH can be set empirically or adaptively determined based on the error characteristics of the scenario at the first moment.

[0036] It should be noted that after determining the identification results of each satellite, satellites whose satellite signals indicate interference can be removed, and only the ephemeris data of satellites whose satellite signals do not indicate interference can be used for positioning calculation to obtain the positioning result; or, in the positioning calculation, the weight of satellites whose satellite signals indicate interference can be reduced, thereby improving the positioning accuracy.

[0037] In this embodiment, the ephemeris data of at least one satellite is acquired in response to the target device's GNSS module. Dead reckoning (DR) data is then used to calculate the target device's estimated position. Interference identification is performed on each of the at least one satellite to obtain an interference identification result for each satellite. This interference identification operation includes: determining the theoretical pseudorange of the satellite based on the estimated target position and the satellite's ephemeris data; and determining the interference identification result based on the theoretical and measured pseudoranges of the satellite. The interference identification result is used to characterize whether interference exists in the satellite signal. Thus, redundant auxiliary data is fully utilized in the target device's navigation and positioning to accurately identify whether interference exists in the satellite signal.

[0038] In some embodiments, before performing step S202, the method may further include: determining the target scene where the target device is located based on the ephemeris data of at least one satellite and the map data of the target device; step S202 may specifically include: performing dead reckoning based on DR data to obtain an initial reckoning position; performing position optimization processing on the initial reckoning position corresponding to the target scene to obtain the target reckoning position associated with the target device and the target scene.

[0039] In this embodiment, the aforementioned target scenario refers to the environmental type in which the target device is located (i.e., the scenario in which it is located at the aforementioned first moment). Specifically, when the target device includes a vehicle, various typical scenarios can be predefined, such as an initialization scenario (the scenario in which the vehicle has just started or just completed initial positioning), a tunnel scenario (the scenario in which the vehicle is driving inside a tunnel), a tunnel exit scenario (the scenario in which the vehicle is about to exit or is exiting a tunnel), an under-elevation scenario (the scenario in which the vehicle is driving under an overpass), an overpass scenario (the scenario in which the vehicle is driving on an overpass), an urban canyon scenario (the scenario in which the vehicle is driving on a city street with tall buildings on both sides), and a tree-lined road scenario (the scenario in which the vehicle is driving on a road with dense trees on both sides), etc.

[0040] The aforementioned map data may include at least one of the following: high-precision maps and standard navigation maps, such as... Figure 1 As shown, this map data can be obtained through the map module. Map data typically includes information such as road type, road grade, road width, number of lanes, and surrounding infrastructure (e.g., tunnels, viaducts, buildings). By combining this data with ephemeris data from at least one satellite, including the number of visible satellites, their distribution geometry (e.g., DOP value), and signal quality, the target scene where the vehicle is located can be determined.

[0041] The initial estimated position mentioned above refers to the position result obtained directly from dead reckoning based on DR data without scene optimization processing. Position optimization processing refers to the process of correcting and optimizing the initial estimated position according to the characteristics of the target scene.

[0042] In this embodiment, by determining the target scene and optimizing the initial estimated position according to the scene characteristics, differentiated processing strategies can be adopted for different driving environments, thereby improving the accuracy and reliability of the estimated target position and thus improving the accuracy of interference identification.

[0043] In some embodiments, the location optimization process described above may employ different optimization methods depending on the target scenario. Specifically, when the target device includes a vehicle, the location optimization process may include at least one of the following optimization methods one to six.

[0044] Optimization Method 1: When the target scene includes the initial scene, perform sensor calibration and smoothing filtering on the initial estimated position; or, perform lane-level matching correction on the initial estimated position based on map data. That is, the above position optimization process can include sensor calibration and smoothing filtering or lane-level matching correction.

[0045] The aforementioned sensor calibration process is used to perform zero-bias calibration and scaling factor calibration on the inertial sensors in the DR module to eliminate systematic errors in the sensors.

[0046] The above-mentioned smoothing filtering process can use algorithms such as Kalman filtering and particle filtering to smooth the initial calculated position in order to eliminate the influence of random noise.

[0047] The lane-level matching correction process mentioned above refers to matching the initial calculated position with the lane-level road information in the high-precision map, and correcting the initial calculated position according to the relative position of the vehicle in the lane.

[0048] Optimization Method 2: When the target scenario includes a tunnel, the initial calculated position is matched with strong tunnel constraints based on the stable positioning of the tunnel entrance. In other words, the above position optimization process includes strong tunnel constraint matching.

[0049] The aforementioned tunnel strong constraint matching process refers to utilizing the geometric constraints of the roads inside the tunnel (such as limited tunnel width and relatively fixed road linearity) to constrain the initial calculated position within the road area inside the tunnel, preventing position divergence. For example, the tunnel centerline or lane lines can be used as a reference for position constraints to project and correct the initial calculated position.

[0050] Optimization Method 3: When the target scenario includes exiting a tunnel, the baseline inside the tunnel is inherited, and the initial calculated position is matched with the exit lane and continuously recursively calculated. That is, the above position optimization process includes exit lane matching and continuous recursion.

[0051] When exiting a tunnel, the GNSS signal may have just recovered or is still weak. Therefore, position correction can be performed based on the road structure characteristics at the tunnel exit. The aforementioned exit lane matching process refers to matching the initial calculated position with the lane layout at the tunnel exit to determine the vehicle's lane position. Continuous recursive processing refers to maintaining the continuity of position calculation during the tunnel exit process to avoid position jumps.

[0052] Optimization Method 4: When the target scenario includes an underpass scenario, perform lane-level map matching with strong elevation constraints on the initially calculated location. That is, the above location optimization process includes lane-level map matching with strong elevation constraints.

[0053] In scenarios where the viaduct is located, the overhead structure can severely obstruct GNSS signals and cause multipath effects. Therefore, elevation information can be used to strongly constrain the position. Specifically, the elevation component of the initially estimated position can be constrained to the elevation range of the road surface based on elevation data from a high-precision map, and then horizontal position can be matched and corrected by combining lane-level road information.

[0054] Optimization Method 5: When the target scene includes an elevated scene, perform high-speed map matching and smoothing filtering on the initial estimated position. That is, the above position optimization process includes high-speed map matching and smoothing filtering.

[0055] In elevated road scenarios, vehicle speeds are typically high, and the elevated structure may reflect and interfere with surrounding satellite signals. The aforementioned high-speed adaptation map matching process involves adjusting map matching parameters and strategies based on the curve characteristics of the elevated road and vehicle speed to meet the position correction requirements under high-speed conditions. Smoothing filtering is used to eliminate noise from sensor data during high-speed travel.

[0056] Optimization Method Six: When the target scene includes urban canyons or tree-lined roads, road constraint matching is performed on the initially calculated position to correct azimuth drift. That is, the position optimization process includes road constraint matching to correct azimuth drift.

[0057] In urban canyons and tree-lined roads, tall buildings or trees on both sides can obstruct GNSS signals and cause multipath effects. Simultaneously, the heading angle information in the DR data may accumulate errors due to gyroscope drift. The aforementioned road constraint matching process refers to using changes in road direction to constrain the vehicle's heading angle and correct azimuth drift. For example, when a vehicle is traveling on a straight road, the heading angle can be constrained to be near the road direction; when the vehicle is turning, the heading angle can be corrected based on the road's curvature.

[0058] In this embodiment, differentiated position optimization strategies are adopted for different types of driving scenarios. This can give full play to the constraints of available information in each scenario, effectively suppress the accumulation of DR estimation errors, improve the accuracy and robustness of the estimated target position in different scenarios, and thus improve the accuracy of interference identification.

[0059] In some embodiments, before performing interference identification operations for each of at least one satellite to obtain interference identification results for each satellite, the above method may further include: determining a confidence result of the target's estimated location, the confidence result being used to indicate whether the target's estimated location is reliable; and determining that the confidence result indicates the target's estimated location is reliable.

[0060] That is, the above-mentioned interference identification operation is performed for each of the at least one satellite to obtain the interference identification result for each satellite, including: in response to the confidence result indicating that the target's estimated position is reliable, the interference identification operation is performed for each of the at least one satellite to obtain the interference identification result for each satellite.

[0061] The aforementioned credibility results are used to assess the reliability of the estimated target location. Specifically, the credibility results may include a credibility level or a credibility score. For example, the credibility level can be divided into multiple levels, such as "high credibility," "medium credibility," and "low credibility"; the credibility score can be a value between 0 and 1, with the value closer to 1 indicating higher credibility.

[0062] The aforementioned confidence level indicates that the estimated target location is reliable. This can be achieved by reaching a preset confidence level (e.g., "medium confidence" or higher) or by having a confidence score exceeding a preset score threshold (e.g., 0.5 or higher). If the confidence level indicates that the estimated target location is unreliable, the interference identification process can be skipped, or alternative strategies can be adopted (e.g., expanding the threshold range to reduce identification sensitivity).

[0063] In this embodiment, by evaluating the credibility of the target's estimated location and only performing interference identification when the credibility result indicates that the target's estimated location is credible, the use of unreliable location references for interference identification can be avoided, thereby improving the accuracy and reliability of interference identification results and reducing false alarm rate and false negative rate.

[0064] In some embodiments, the determination of the confidence result of the estimated target position may include: determining the satellite pseudorange residual of at least one satellite based on the estimated target position and the ephemeris data of at least one satellite; and determining the confidence result of the estimated target position based on the position error range corresponding to the satellite pseudorange residual and the estimated target position.

[0065] The aforementioned satellite pseudorange residual refers to the difference between the theoretical pseudorange calculated based on the target's estimated position and the measured pseudorange. Specifically, for each visible satellite, the theoretical pseudorange ρcalc is first calculated based on the target's estimated position and the satellite's position (obtained from ephemeris data). Then, the measured pseudorange ρmeas measured by the GNSS module is obtained. Therefore, the satellite pseudorange residual is residual = ρcalc - ρmeas.

[0066] The aforementioned position error range refers to the uncertainty range of the target's estimated position, which can be represented as a spherical or ellipsoidal region centered on the estimated target position and with a certain error radius. The position error range can be determined comprehensively based on factors such as the estimation time of the DR data, sensor accuracy, and historical positioning accuracy.

[0067] Specifically, the statistical characteristics of all satellite pseudorange residuals can be calculated, such as the mean, standard deviation, and maximum value. If the distribution of pseudorange residuals is relatively concentrated (e.g., the standard deviation is small), and most pseudorange residuals fall within the reasonable range corresponding to the position error range, then the reliability of the estimated target position is considered high; conversely, if the distribution of pseudorange residuals is scattered or there are large outliers, then the reliability is considered low.

[0068] For example, the standard deviation σres of the pseudorange residual can be calculated. If σres < σthreshold (σthreshold is a preset standard deviation threshold), the estimated position of the target is determined to be highly reliable; if σthreshold ≤ σres < 2σthreshold, it is determined to be of medium reliability; if σres ≥ 2σthreshold, it is determined to be of low reliability.

[0069] It should be noted that the target estimated position mentioned above in this embodiment can be the initial estimated position without optimization, or the estimated position obtained after optimizing the initial estimated position. There is no limitation here.

[0070] In this embodiment, the reliability of the target's estimated position is evaluated by analyzing the statistical distribution characteristics of the satellite pseudorange residuals. This can directly reflect the quality of the position estimation from the perspective of observation data, and the evaluation results have high objectivity and accuracy.

[0071] In some embodiments, the determination of the confidence result of the target estimated location may include: determining the receiver clock error rate of the GNSS module based on the target estimated location; and determining the confidence result of the target estimated location based on the receiver clock error rate.

[0072] The aforementioned receiver clock bias rate of change refers to the rate at which the clock bias between the GNSS receiver clock and the satellite system time changes over time. In GNSS positioning calculations, the receiver clock bias is a parameter to be estimated. Using the target's estimated position as a known position, the receiver clock bias can be solved inversely. Then, by differentiating the receiver clock bias at different times, the rate of change of clock bias can be obtained.

[0073] Specifically, we assume that the geometric distance is calculated using the target's estimated position (x, y, z) and the satellite i's position (xi, yi, zi). By combining the measured pseudorange ρi, the receiver clock error can be calculated. Where Ii is the ionospheric delay, Ti is the tropospheric delay, and c is the speed of light. The rate of change of clock bias can be obtained by linearly fitting the clock bias over multiple epochs.

[0074] The receiver clock bias rate of change reflects the frequency offset characteristics of the receiver clock. Normal GNSS receivers typically employ a temperature-compensated crystal oscillator (TCXO) or an oven-controlled crystal oscillator (OCXO), whose clock bias rate of change is relatively stable. If the inverse kinematics clock bias rate of change is within a reasonable physical range (e.g., corresponding to the normal frequency offset range of the crystal oscillator), it indicates that the estimated target position is relatively reliable; if the clock bias rate of change is abnormal (e.g., exceeding the normal frequency offset range of the crystal oscillator), it indicates that the estimated target position may have a large error.

[0075] For example, the typical frequency offset range of a TCXO is ±0.5ppm to ±2.5ppm, corresponding to a clock error change rate range of approximately ±0.5ns / s to ±2.5ns / s. If the calculated clock error change rate falls within this range, the estimated target location is considered reliable; otherwise, it is considered unreliable.

[0076] In this embodiment, the reliability of the target's estimated location is evaluated by using the physical constraint of the receiver clock error rate. This allows for the physical verification of the rationality of the location estimation results and provides good sensitivity to detecting significant errors in DR estimation.

[0077] It should be noted that the reliability of the target's estimated position can be determined solely based on the position error range corresponding to the satellite pseudorange residual and the estimated target position, or based on the receiver clock error rate; or, a combination of both can be used to determine the reliability of the target's estimated position. No limitation is imposed here.

[0078] In some embodiments, before determining the interference identification result of the satellite based on the theoretical pseudorange and the measured pseudorange, the method further includes: determining a target threshold based on the position error range corresponding to the calculated position of the target; the determination of the interference identification result of the satellite based on the theoretical pseudorange and the measured pseudorange includes: determining the interference identification result of the satellite based on the comparison result between the pseudorange difference and the target threshold, wherein the pseudorange difference is the difference between the theoretical pseudorange and the measured pseudorange.

[0079] The aforementioned target threshold is used to determine whether the pseudorange difference exceeds the normal range and is a core parameter for interference identification. The target threshold can be determined based on the position error range. The position error range reflects the uncertainty of the target's estimated position. The larger the position error range, the lower the accuracy of the target's estimated position. In this case, the target threshold needs to be appropriately increased to avoid misjudgment; conversely, the smaller the position error range, the smaller the target threshold can be used to improve the identification sensitivity.

[0080] The above-mentioned comparison between the pseudorange difference and the target threshold determines the interference identification result corresponding to the satellite. If the pseudorange difference is greater than the target threshold, the satellite is determined to be interfered with; if the pseudorange difference is less than or equal to the target threshold, the satellite is determined to be normal.

[0081] For example, assuming the target threshold is TH_target, and the pseudorange difference of a certain satellite is Δρ, if Δρ>TH_target, then the satellite is determined to be interfered with; if Δρ≤TH_target, then the satellite is determined to be normal.

[0082] In this embodiment, by adaptively determining the target threshold based on the position error range, the interference identification algorithm can adapt to different positioning accuracy conditions, thereby reducing the risk of misjudgment while ensuring identification sensitivity and improving the robustness of interference identification.

[0083] In some embodiments, determining the target threshold based on the position error range corresponding to the target's estimated position may include: determining a first threshold coefficient based on the target scene in which the target device is located; and determining the target threshold based on the first threshold coefficient and the position error range corresponding to the target's estimated position.

[0084] The aforementioned first threshold coefficient is a correction coefficient determined based on the target scene. For example, it can be determined based on a preset mapping relationship between the scene and the threshold coefficient to identify the first threshold coefficient corresponding to the target scene.

[0085] Signal propagation characteristics and interference patterns differ in different scenarios, so different threshold coefficients can be used to adapt to the characteristics of each scenario. For example, in scenarios with severe signal obstruction (such as tunnels or urban canyons), pseudorange measurement errors may be large, requiring a larger threshold coefficient to avoid misjudgment; in scenarios with good signal conditions (such as open roads), a smaller threshold coefficient can be used to improve recognition sensitivity.

[0086] The above-mentioned determination of the target threshold based on the first threshold coefficient and the position error range corresponding to the target calculated position can be implemented according to preset rules or methods.

[0087] Specifically, the target threshold can be expressed as TH_target = k1 × σpos, where k1 is the first threshold coefficient and σpos is the position error range. The position error range can be represented by indicators such as standard deviation and confidence interval radius. The first threshold coefficient can be determined from a preset coefficient table based on the scene type.

[0088] For example, the following scenarios can be pre-defined to correspond to the first threshold coefficient: initialization scenario corresponds to k1=3.0, tunnel scenario corresponds to k1=4.0, exiting tunnel scenario corresponds to k1=3.5, under-elevation scenario corresponds to k1=3.5, on-elevation scenario corresponds to k1=2.5, urban canyon scenario corresponds to k1=3.0, tree-lined road scenario corresponds to k1=3.0, open scenario corresponds to k1=2.0, and so on.

[0089] In this embodiment, by determining the first threshold coefficient according to the target scenario, the threshold setting can be matched with the signal characteristics of the driving environment, so that appropriate interference recognition sensitivity can be obtained in different scenarios, thereby improving scenario adaptability.

[0090] In some embodiments, determining a target threshold based on the position error range corresponding to the target estimated position includes: determining a second threshold coefficient based on the confidence result of the target estimated position; and determining the target threshold based on the second threshold coefficient and the position error range corresponding to the target estimated position.

[0091] Specifically, the second threshold coefficient can be determined based on the confidence level or confidence score of the target's estimated location.

[0092] The credibility level and credibility score can be divided according to the credibility determination method in the aforementioned embodiments, for example, they can be divided into three levels: "high credibility", "medium credibility" and "low credibility".

[0093] The second threshold coefficient is a correction coefficient determined based on the confidence level. The lower the confidence level or confidence score, the worse the reliability of the target's estimated location. In this case, a larger second threshold coefficient is needed to avoid misjudgment. Conversely, the higher the confidence level or confidence score, the smaller the second threshold coefficient can be used to improve recognition sensitivity.

[0094] Specifically, the target threshold can be expressed as TH_target = k2 × σpos, where k2 is the second threshold coefficient and σpos is the position error range. For example, the following correspondence between confidence levels and the second threshold coefficient can be preset: high confidence corresponds to k2=2.0, medium confidence corresponds to k2=3.0, and low confidence corresponds to k2=4.0.

[0095] In this embodiment, by adaptively adjusting the second threshold coefficient according to the confidence level of the target's estimated location, the judgment criteria can be relaxed when the location reference reliability is low to avoid misjudgment, and the judgment criteria can be tightened when the location reference reliability is high to improve recognition sensitivity, thereby achieving adaptive optimization of recognition performance.

[0096] It should be noted that the second threshold coefficient determined based on the credibility level can also be determined by combining the credibility level and the target scene information; no limitation is imposed here.

[0097] In some embodiments, after performing interference identification operations on each of the at least one satellite to obtain interference identification results for each satellite, the method further includes: determining the number of target satellites based on the interference identification results of the at least one satellite, wherein the target satellites are satellites whose interference identification results indicate that the satellite signals are not interfered with; in response to the number of target satellites being less than a preset number, gradually increasing the target threshold with the position error range as the minimum step size, and re-performing the step of performing interference identification operations on each of the at least one satellite to obtain interference identification results for each satellite.

[0098] The aforementioned threshold refers to the minimum number of satellites required for positioning calculation. If the number of satellites deemed normal is insufficient to support positioning calculation (e.g., less than 4), the target threshold can be appropriately reduced, and interference identification can be performed again to increase the number of satellites available for positioning calculation.

[0099] In this embodiment, by setting a threshold adjustment guarantee mechanism, the threshold can be automatically adjusted and interference identification can be performed again in extreme cases (such as too many misjudgments or insufficient available satellites), ensuring the availability of the system in complex environments and avoiding positioning failure due to excessive satellite rejection.

[0100] In some embodiments, before determining the target threshold based on the position error range corresponding to the target estimated position, the method further includes: quantizing and summing all inherent error sources of the target estimated position to obtain an initial error range; and performing error range optimization processing on the initial error range corresponding to the target scene to obtain the position error range corresponding to the target estimated position.

[0101] The aforementioned inherent error sources refer to the various systematic and random errors present in the dead reckoning system. Inherent error sources may include, but are not limited to: IMU zero-bias error, IMU scale factor error, IMU random walk error, odometer scale factor error, and initial alignment error. For each error source, its contribution to the position reckoning results can be quantitatively estimated based on the sensor's specifications and operating status.

[0102] The above quantitative summation process can employ the error propagation law to synthesize the influence of each error source on the position estimation result. For example, assuming that the error sources are independent, the initial error range can be expressed as the square root of the sum of the squares of the contributions of each error source: , where σi is the contribution of the i-th error source to the position error.

[0103] The impact of each error source may vary in different scenarios, so the initial error range can be optimized according to the target scenario. For example, in a tunnel scenario, due to the lack of GNSS signal correction, the DR estimation error will accumulate rapidly over time, so it is necessary to increase the error range estimate; in an open scenario, if GNSS signal correction can be obtained occasionally, the error range estimate can be appropriately reduced.

[0104] In this embodiment, by quantifying and summing the inherent error sources and optimizing them in combination with scene characteristics, it is possible to obtain a position error range estimate that is adapted to the sensor state and driving environment, providing a reliable basis for threshold determination and credibility assessment.

[0105] In some embodiments, the error range optimization process described above may employ different optimization strategies depending on the target scenario. Specifically, when the target device includes a vehicle, the optimization strategy may include strategies one through six.

[0106] Strategy 1: When the target scenario includes the initial scenario, the sensor calibration error is superimposed on the initial error range, or the lane matching error is superimposed based on the map data. That is, the error range optimization process includes sensor calibration optimization or lane-level matching constraint optimization.

[0107] Sensor calibration optimization refers to calibrating the DR sensor using data from when the GNSS signal quality is good during the vehicle initialization phase. Calibrated sensors have reduced errors, thus appropriately reducing the initial error range. Lane-level matching constraint optimization refers to using lane-level map matching results to constrain the position; higher matching accuracy can reduce the estimated error range.

[0108] Strategy 2: When the target scenario includes a tunnel, take the stable positioning at the tunnel entrance as the benchmark, and add dead reckoning cumulative error, map matching error and slippage error to the initial error range. That is, the error range optimization process includes error boundary optimization under the strong constraints of the tunnel.

[0109] The road geometry within the tunnel is subject to strong constraints (such as limited road width and restricted driving direction). These constraints can be used to limit the spread of position errors and optimize the initial error range by boundary constraints.

[0110] Strategy 3: When the target scenario includes exiting a tunnel, inherit the benchmark inside the tunnel and, on the initial error range, superimpose the cumulative dead reckoning error and map matching error after exiting the tunnel. That is, the error range optimization process includes exit area recursive error correction optimization.

[0111] As the vehicle exits the tunnel, the GNSS signal gradually recovers. The partially recovered GNSS signal can be used to correct the DR recursion error, thereby optimizing the error range estimation.

[0112] Strategy 4: When the target scenario includes the underpass scenario, the map matching error with strong plane and elevation constraints is superimposed on the initial error range. That is, the error range optimization process includes error ellipsoid adjustment optimization under elevation constraints.

[0113] By using elevation data from high-precision maps to strongly constrain the elevation components of a location, the size of the error ellipsoid in the elevation direction can be compressed, thereby optimizing the error range estimation.

[0114] Strategy 5: When the target scenario includes elevated roads, a fixed map matching error and a high-speed driving dynamic error are superimposed on the initial error range. This means the error range optimization process includes high-speed dynamic error compensation optimization. Under high-speed driving conditions, the dynamic error characteristics of the sensors may change (e.g., the scaling factor nonlinearity error of the gyroscope increases), requiring error compensation for high-speed dynamic characteristics and optimization of the error range estimation accordingly.

[0115] Strategy 6: When the target scenario includes urban canyons or tree-lined roads, map matching error and start-stop-turn dynamic error are superimposed on the initial error range. That is, the error range optimization process includes horizontal error correction optimization under road constraints.

[0116] By utilizing the direction and curvature constraints of the road, the positional error in the horizontal direction is corrected, thereby optimizing the error range estimation.

[0117] In this embodiment, a differentiated error range optimization strategy is adopted for different scenarios. This strategy can fully consider the available constraint information and error characteristics in each scenario, obtain a more accurate and reasonable position error range estimate, and provide a reliable error benchmark for interference identification.

[0118] In some embodiments, the ephemeris data of the at least one satellite includes the ephemeris data acquired at the first moment; after performing interference identification operation on each of the at least one satellite to obtain the interference identification result for each satellite, the method further includes: determining the relative position change from the second moment to the first moment by performing dead reckoning on the target device, wherein the second moment and the first moment are adjacent sampling moments of the GNSS module; performing pseudorange processing on the target satellite to obtain the equivalent pseudorange of the target satellite at the first moment, wherein the pseudorange processing includes: determining the line-of-sight unit vector of the target satellite relative to the position of the target device at the second moment; projecting the relative position change onto the line-of-sight direction of the satellite corresponding to the line-of-sight unit vector to obtain the pseudorange compensation amount; compensating the measured pseudorange of the target satellite at the second moment according to the pseudorange compensation amount to obtain the equivalent pseudorange of the target satellite at the first moment; wherein the target satellite includes the satellite whose interference identification result indicates that the satellite signal is not interfered with among the at least one satellite; and performing positioning calculation based on the measured pseudorange and equivalent pseudorange of the target satellite at the first moment to obtain the first positioning result of the target device.

[0119] The aforementioned relative position change refers to the position change vector of the target device between two adjacent epochs. The velocity and direction changes of the target device can be obtained by integrating the acceleration and angular velocity in the DR data, and then the relative position change can be calculated.

[0120] For example, assuming that the vehicle's eastward speed changes by Δve, northward speed changes by Δvn, and skyward speed changes by Δvu within a time interval Δt, then the relative position change can be expressed as (Δve×Δt, Δvn×Δt, Δvu×Δt).

[0121] The aforementioned target satellites refer to qualified satellites that have been determined to be unaffected by interference after interference identification.

[0122] The aforementioned equivalent pseudorange refers to the pseudorange estimate for the current epoch calculated using the measured pseudorange of the previous epoch and the change in relative position. Specifically, assuming the measured pseudorange of the target satellite in the previous epoch is ρ(t-1), the change in relative position is Δp, and the unit vector of the satellite direction is e, the equivalent pseudorange can be expressed as ρ_equiv = ρ(t-1) + e·Δp, where e·Δp represents the projection of the change in relative position onto the satellite direction.

[0123] The difference between the measured pseudorange and the equivalent pseudorange can be used as the observation value to establish an observation equation for positioning calculation. The advantage of the tightly coupled method is that even if only a small number of satellites (less than 4) are available, the relative position constraints provided by the DR data can be used for positioning calculation, which improves the continuity and availability of positioning.

[0124] In this embodiment, DR data and GNSS data are fused and positioned using a tight coupling method, which can maintain positioning capability even when the number of satellites is insufficient, improving the availability and continuity of the positioning system. This method is particularly suitable for positioning applications in complex urban environments.

[0125] In some embodiments, the above-mentioned determination of the relative position change from the second time to the first time by performing dead reckoning on the target device includes: in response to the target scenario in which the target device is located at the first time being a first preset scenario, determining the relative position change from the second time to the first time by performing dead reckoning on the target device.

[0126] The first preset scenario refers to the scenario conditions suitable for using tightly coupled positioning calculation. Specifically, the first preset scenario may include at least one of the following: a scenario with insufficient number of visible satellites (such as the number of visible satellites being less than the minimum number required for positioning calculation), a scenario with severe signal obstruction (such as tunnels, urban canyons, tree-lined roads, etc.), and a scenario with generally poor GNSS signal quality (such as the signal-to-noise ratio of most satellite signals being lower than a preset threshold).

[0127] In this embodiment, by enabling tightly coupled positioning calculation in specific scenarios, it is possible to automatically switch to a more robust positioning mode when GNSS conditions deteriorate, thus ensuring the continuity of positioning services.

[0128] In scenarios other than the first preset scenario (such as open roads, scenarios with a sufficient number of visible satellites and good signal quality), loosely coupled or conventional positioning calculation methods can be used to make full use of the high-precision characteristics of GNSS data.

[0129] In some embodiments, after determining the interference identification result corresponding to each satellite, a quasi-tight coupling method can also be used for positioning calculation. That is, after performing the interference identification operation for each of at least one satellite and obtaining the interference identification result for each satellite, the method can further include: responding to the target scenario where the target device is located at the second time being a second preset scenario, determining the relative position change range of the target device between at least one adjacent sampling time of the GNSS module based on the DR data of the target device, wherein the at least one adjacent sampling time includes the adjacent sampling time corresponding to the first time and the second time; determining the positioning feasible region of the target device under dead reckoning constraints at the first time based on the relative position change range; and performing positioning calculation within the positioning feasible region based on the measured pseudorange of the target satellite to obtain the second positioning result of the target device.

[0130] The aforementioned relative position change range refers to the uncertainty range of the target device's position change between two adjacent epochs. Unlike the precise relative position change determined in the tightly coupled method, the quasi-tightly coupled method determines a range of changes. This range can be determined based on the error characteristics of the DR sensor and the calculation time.

[0131] For example, assuming the horizontal position error growth rate calculated by DR is 0.5 m / s and the calculation time is 1 second, the range of relative position change can be represented as a circular area (horizontal direction) with a radius of 0.5 meters centered on the calculated position change.

[0132] The aforementioned feasible region for positioning refers to the possible range of the target device's position at the first moment in the quasi-tightly coupled positioning solution. The feasible region can be determined based on the positioning result of the previous epoch and the range of relative position change. For example, assuming the positioning result of the previous epoch is P(t-1), and the range of relative position change is a spherical region centered at Δp with radius r, then the feasible region can be represented as a spherical region centered at P(t-1) + Δp with radius r.

[0133] By constraining the location solution within the feasible region, the computational space can be reduced, and the efficiency and reliability of the solution can be improved. Specifically, the feasible region can be added as a constraint to the optimization problem of the location solution, and the optimal solution that minimizes the sum of squared residuals can be found within the feasible region.

[0134] In this embodiment, a constraint on the range of relative position changes is introduced through quasi-tight coupling, which reduces computational complexity while maintaining a certain positioning accuracy. This approach is suitable for application scenarios where computing resources are limited or where rapid positioning response is required.

[0135] In some embodiments, before performing dead reckoning based on the dead reckoning (DR) data of the target device to obtain the target estimated position of the target device, the method further includes: inputting the DR data into a GNSS module to perform dead reckoning based on the DR data of the target device through the GNSS module to obtain the target estimated position of the target device; and performing an interference identification operation for each of at least one satellite to obtain the interference identification result for each satellite.

[0136] In this embodiment, inputting DR data to the GNSS module means transmitting the motion state data (such as acceleration, angular velocity, and movement speed) of the target device collected by the DR module back to the GNSS module for the GNSS module to perform signal interference identification.

[0137] In this embodiment, by inputting DR data into the GNSS module, independent motion state reference information can be provided to the GNSS module, which can assist the GNSS module in signal processing and positioning calculation, thereby improving the working performance of the GNSS module in complex environments and the reliability of positioning results. Based on the same inventive concept, such as Figure 3 As shown in the figure, this application embodiment also provides a satellite signal interference identification device 300, which includes: The dead reckoning module is used to perform dead reckoning based on the target equipment's DR data after the target equipment's GNSS module has acquired ephemeris data from at least one satellite, thereby obtaining the target equipment's estimated position. The interference identification module is used to perform interference identification operations for each of at least one satellite and obtain the interference identification result for each satellite. The interference identification operation includes: determining the theoretical pseudorange of the satellite based on the target's estimated position and the satellite's ephemeris data; and determining the interference identification result of the satellite based on the satellite's theoretical pseudorange and measured pseudorange. The interference identification result is used to characterize whether there is interference in the satellite signal.

[0138] In some embodiments, the device 300 further includes: The scene determination module is used to determine the target scene where the target device is located based on the ephemeris data of at least one satellite and the map data of the target device. The aforementioned dead reckoning module 301 is specifically used for: Based on the DR data, dead reckoning is performed to obtain the initial estimated position; The initial estimated position is optimized to correspond to the target scene to obtain the target estimated position associated with the target device and the target scene.

[0139] In some embodiments, the dead reckoning module 301 is specifically used for at least one of the following: In the case of the initialization scenario including the GNSS module in the target scenario, sensor calibration and smoothing filtering are performed on the initial estimated position, or lane-level matching correction is performed on the initial estimated position based on map data; When the target scenario includes a tunnel, the initial calculated position is matched with strong tunnel constraints based on the stable positioning at the tunnel entrance. In the case where the target scenario includes exiting a tunnel, the benchmark inside the tunnel is inherited, and the exit lane matching and continuous recursion are performed on the initially calculated position. When the target scene includes an underpass scene, lane-level map matching with strong elevation constraints is performed on the initially estimated position; In cases where the target scene includes an elevated area, high-speed map matching and smoothing filtering are performed on the initially estimated location; When the target scene includes urban canyons or tree-lined roads, road constraint matching is performed on the initially calculated position to correct azimuth drift.

[0140] In some embodiments, the device 300 further includes: The credibility determination module allows the user to determine the credibility result of the target estimated location. The credibility result is used to indicate whether the target estimated location is credible. Determining the credibility result indicates that the target estimated location is credible.

[0141] In some embodiments, the confidence determination module is specifically used for: Based on the estimated position of the target and the ephemeris data of at least one satellite, determine the satellite pseudorange residual of at least one satellite; The reliability of the target's estimated position is determined based on the position error range corresponding to the satellite pseudorange residual and the estimated target position.

[0142] In some embodiments, the confidence determination module is specifically used for: Based on the target's estimated location, determine the receiver clock error rate of the GNSS module; The reliability of the target's estimated position is determined based on the receiver clock error rate.

[0143] In some embodiments, the device 300 further includes: The threshold determination module is used to determine the target threshold based on the position error range corresponding to the target's calculated position. Interference identification module 302 is specifically used for: The interference identification result of the satellite is determined based on the comparison between the pseudorange difference and the target threshold, where the pseudorange difference is the difference between the theoretical pseudorange and the measured pseudorange.

[0144] In some embodiments, the threshold determination module is specifically used for: Determine the first threshold coefficient based on the target scenario in which the target device is located; The target threshold is determined based on the first threshold coefficient and the position error range corresponding to the calculated target position.

[0145] In some embodiments, the threshold determination module is specifically used for: Based on the reliability of the target's estimated location, determine the second threshold coefficient; The target threshold is determined based on the second threshold coefficient and the corresponding position error range of the target's estimated position.

[0146] In some embodiments, the device 300 further includes: The quantity determination module is used to determine the number of target satellites based on the interference identification results of at least one satellite. The target satellites are those whose signals are free from interference as indicated by the interference identification results. The threshold adjustment module is used to gradually increase the target threshold in steps with the position error range as the minimum step size in response to the number of target satellites being less than the preset number, and to re-execute the interference identification operation for each satellite in at least one satellite to obtain the interference identification result for each satellite.

[0147] In some embodiments, the device 300 further includes an error range optimization module, configured to: The initial error range is obtained by quantifying and summing all inherent error sources of the target's estimated location. The initial error range is optimized to correspond to the error range of the target scene to obtain the position error range corresponding to the estimated position of the target.

[0148] In some embodiments, the error range optimization module is specifically used for at least one of the following: In the initialization scenario of the GNSS module, the sensor calibration error is superimposed on the initial error range, or the lane matching error is superimposed based on the map data. In the tunnel scenario, based on the stable positioning at the tunnel entrance, the cumulative dead reckoning error, map matching error, and slippage error are superimposed on the initial error range. In the scenario of exiting a tunnel, the baseline inside the tunnel is inherited, and the cumulative dead reckoning error and map matching error after exiting the tunnel are superimposed on the initial error range. In the scenario under the elevated road, the map matching error with strong plane and elevation constraints is superimposed on the initial error range; In the elevated road scenario, a fixed map matching error and a high-speed driving dynamic error are superimposed on the initial error range; In urban canyon or tree-lined road scenarios, map matching error and start-stop / steering dynamic error are superimposed on the initial error range.

[0149] In some embodiments, the device 300 further includes a tightly coupled module for: By performing dead reckoning on the target equipment, the relative position change from the second moment to the first moment is determined. The second moment and the first moment are adjacent sampling moments of the GNSS module. Pseudorange processing is performed on the target satellite to obtain the equivalent pseudorange of the target satellite at the first moment. The pseudorange processing involves: determining the line-of-sight unit vector of the target satellite relative to the position of the target device at the second moment; projecting the relative position change onto the line-of-sight direction corresponding to the line-of-sight unit vector to obtain the pseudorange compensation amount; and compensating the measured pseudorange of the target satellite at the second moment based on the pseudorange compensation amount to obtain the equivalent pseudorange of the target satellite at the first moment. The target satellite includes at least one satellite whose interference identification results indicate that the satellite signal is free from interference. The first positioning result of the target device is obtained by calculating the positioning result based on the measured pseudorange and equivalent pseudorange of the target satellite at the first moment.

[0150] In some embodiments, the tightly coupled module is specifically used for: In response to the target scenario where the target device is located at the first moment being the first preset scenario, the relative position change from the second moment to the first moment is determined by dead reckoning of the target device.

[0151] In some embodiments, the device 300 further includes a quasi-tight coupling module for: In response to the target scenario where the target device is located at the second time being the second preset scenario, the relative position change range of the target device between at least one adjacent sampling time of the GNSS module is determined based on the DR data of the target device. The at least one adjacent sampling time includes the adjacent sampling time corresponding to the first time and the second time. The feasible region for positioning of the target equipment under dead reckoning constraints at the first moment is determined based on the range of relative position changes. The second positioning result of the target device is obtained by performing positioning calculations within the positioning feasible region based on the measured pseudorange of the target satellite.

[0152] In some embodiments, the device 300 further includes: The input module is used to input DR data into the GNSS module, so that the GNSS module can perform dead reckoning based on the DR data of the target device to obtain the target reckoning position of the target device, and perform interference identification operation for each of at least one satellite to obtain the interference identification result of each satellite.

[0153] The satellite signal interference identification device 300 provided in this application embodiment can execute the technical solution shown in the above-described satellite signal interference identification method embodiment. Its implementation principle and beneficial effects are similar, and will not be described again here.

[0154] Figure 4 This is a schematic diagram of the hardware structure of the satellite interference identification device provided in the embodiments of this application.

[0155] The satellite interference identification device may include a processor 401 and a memory 402 storing computer program instructions.

[0156] Specifically, the processor 401 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0157] Memory 402 may include mass storage for data or instructions. For example, and not limitingly, memory 402 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 402 is non-volatile solid-state memory.

[0158] In some embodiments, memory 402 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the interference identification method for satellite signals according to this application.

[0159] The processor 401 reads and executes computer program instructions stored in the memory 402 to implement the satellite signal interference identification method in the above embodiment.

[0160] In one example, the satellite interference identification device may also include a communication interface 403 and a bus 410. For example, Figure 4 As shown, the processor 401, memory 402, and communication interface 403 are connected through bus 410 and complete communication with each other.

[0161] The communication interface 403 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0162] Bus 410 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 410 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0163] The satellite's interference identification device can execute the satellite signal interference identification method in the embodiments of this application, thereby achieving a combination of Figures 1 to 3 The method and apparatus for identifying interference with satellite signals are described.

[0164] In addition, in conjunction with the satellite signal interference identification method in the above embodiments, this application also provides a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement the satellite signal interference identification method in the above embodiments.

[0165] In conjunction with the satellite signal interference identification method in the above embodiments, this application also provides a computer program product. When the instructions in the computer program product are executed by the processor of the satellite interference identification device, the satellite interference identification device implements the satellite signal interference identification method in the above embodiments.

[0166] It should be noted that the satellite interference identification device in the above embodiments can be the target device or a GNSS module. The target device can be any mobile device equipped with a GNSS module, such as a vehicle or other electronic device (vehicle terminal, mobile phone or smart wearable device, etc.).

[0167] The aforementioned vehicles can be private cars, such as sedans, SUVs, MPVs, or pickup trucks. Vehicles can also be commercial vehicles, such as vans, buses, small trucks, or large semi-trailers. Vehicles can be either gasoline-powered or new energy vehicles. When a vehicle is a new energy vehicle, it can be a hybrid or a pure electric vehicle.

[0168] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0169] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0170] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0171] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods (systems) and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0172] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for identifying interference in satellite signals, characterized in that, include: In response to the target device's Global Navigation Satellite System module acquiring ephemeris data from at least one satellite, dead reckoning is performed based on the target device's dead reckoning data to obtain the target device's estimated position. An interference identification operation is performed on each of the at least one satellite to obtain an interference identification result for each satellite. The interference identification operation includes: determining the theoretical pseudorange of the satellite based on the calculated position of the target and the ephemeris data of the satellite. Based on the theoretical pseudorange and measured pseudorange of the satellite, the interference identification result of the satellite is determined, and the interference identification result is used to characterize whether there is interference in the satellite signal.

2. The method according to claim 1, characterized in that, Before performing dead reckoning based on the dead reckoning data of the target equipment to obtain the target estimated position of the target equipment, the method further includes: Based on the ephemeris data of the at least one satellite and the map data of the target device, the target scene in which the target device is located is determined; The step of performing dead reckoning based on the dead reckoning data to obtain the target estimated position of the target equipment includes: Based on the dead reckoning data, dead reckoning is performed to obtain the initial estimated position; The initial estimated position is optimized to correspond to the target scene to obtain the target estimated position associated with the target device and the target scene.

3. The method according to claim 2, characterized in that, When the target device includes a vehicle, the position optimization processing of the initially calculated position corresponding to the target scene includes at least one of the following: In the case where the target scenario includes the initialization scenario of the Global Navigation Satellite System module, sensor calibration and smoothing filtering are performed on the initial estimated position, or lane-level matching correction is performed on the initial estimated position based on map data; When the target scenario includes a tunnel scenario, the initial estimated position is subjected to strong tunnel constraint matching based on the stable positioning of the tunnel entrance; In the case where the target scenario includes exiting a tunnel, the reference inside the tunnel is inherited, and the exit lane matching and continuous recursion are performed on the initial calculated position. In the case where the target scene includes an underpass scene, lane-level map matching with strong elevation constraints is performed on the initial estimated position; In the case where the target scene includes an elevated scene, the initial estimated position is subjected to high-speed adaptive map matching and smoothing filtering; In cases where the target scene includes an urban canyon or a tree-lined road, road constraint matching is performed on the initially calculated position to correct azimuth drift.

4. The method according to claim 1, characterized in that, Before performing interference identification operations on each of the at least one satellite to obtain the interference identification result for each satellite, the method further includes: Determine the credibility result of the estimated target location; The confidence level result indicates that the estimated location of the target is reliable.

5. The method according to claim 1, characterized in that, Before determining the interference identification result of the satellite based on its theoretical pseudorange and measured pseudorange, the method further includes: Based on the position error range corresponding to the calculated target position, determine the target threshold; The step of determining the interference identification result of the satellite based on the theoretical pseudorange and measured pseudorange includes: The interference identification result of the satellite is determined based on the comparison between the pseudorange difference and the target threshold, wherein the pseudorange difference is the difference between the theoretical pseudorange and the measured pseudorange.

6. The method according to claim 5, characterized in that, After performing interference identification operations on each of the at least one satellite and obtaining the interference identification result for each satellite, the method further includes: Based on the interference identification results of at least one satellite, the number of target satellites is determined, wherein the target satellites are those whose signals are free from interference as indicated by the interference identification results; In response to the fact that the number of target satellites is less than a preset number, the target threshold is gradually increased with the position error range as the minimum step size, and the step of performing interference identification operation for each of the at least one satellite to obtain the interference identification result for each satellite is re-executed.

7. The method according to claim 1, characterized in that, The ephemeris data of the at least one satellite includes the ephemeris data acquired at the first moment; After performing interference identification operations on each of the at least one satellite and obtaining the interference identification result for each satellite, the method further includes: By performing dead reckoning on the target device, the relative position change from the second time point to the first time point is determined; pseudorange processing is performed on the target satellite to obtain the equivalent pseudorange of the target satellite at the first time point; positioning calculation is performed based on the measured pseudorange of the target satellite at the first time point and the equivalent pseudorange to obtain the first positioning result of the target device; wherein, the second time point and the first time point are adjacent sampling times of the Global Navigation Satellite System module; the pseudorange processing includes: determining the line-of-sight unit vector of the target satellite relative to the position of the target device at the second time point; projecting the relative position change onto the line-of-sight direction corresponding to the line-of-sight unit vector to obtain a pseudorange compensation amount; compensating the measured pseudorange of the target satellite at the second time point based on the pseudorange compensation amount to obtain the equivalent pseudorange of the target satellite at the first time point; the target satellite includes the satellite whose interference identification result indicates that the satellite signal is not interfered with among the at least one satellite; and / or Based on the dead reckoning data of the target device, the range of relative position change of the target device between at least one adjacent sampling time of the Global Navigation Satellite System module is determined; based on the range of relative position change, the positioning feasible region of the target device under dead reckoning constraints at the first time is determined; based on the measured pseudorange of the target satellite, positioning calculation is performed within the positioning feasible region to obtain the second positioning result of the target device; wherein, the at least one adjacent sampling time includes the adjacent sampling time corresponding to the first time and the second time.

8. The method according to claim 1, characterized in that, Before performing dead reckoning based on the dead reckoning data of the target equipment to obtain the target estimated position of the target equipment, the method further includes: The dead reckoning data is input into the Global Navigation Satellite System (GNSS) module to perform the following steps: performing dead reckoning based on the dead reckoning data of the target device to obtain the target reckoning position of the target device; and performing the following steps: performing interference identification operation for each of the at least one satellite to obtain the interference identification result of each satellite.

9. A satellite interference identification device, characterized in that, The interference identification device for the satellite includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the satellite signal interference identification method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions; When the computer program instructions are executed by the processor, they implement the satellite signal interference identification method as described in any one of claims 1-8.

11. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the satellite interference identification device, the satellite interference identification device implements the satellite signal interference identification method as described in any one of claims 1-8.

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