Satellite navigation fusion weight determination method and device and storage medium

Abstract

A fusion weight determination method and device for satellite navigation and a storage medium are disclosed. The method comprises: obtaining a first positioning result of a satellite navigation system, determining a positioning noise of the satellite navigation system based on the first positioning result; performing chi-square test on the first positioning result to determine a positioning chi-square result; obtaining a satellite navigation system characteristic value, a positioning quality and a speed quality corresponding to the first positioning result; determining whether an inertial navigation system is in a stable state; when the inertial navigation system is in the stable state, determining a fusion weight based on the positioning noise, the positioning chi-square result, the satellite navigation system characteristic value, the positioning quality and the speed quality; when the inertial navigation system is in a non-stable state, determining the fusion weight based on the satellite navigation system characteristic value, the positioning quality and the speed quality; and the fusion weight is used to determine a trust degree of the first positioning result in combined navigation. The application can determine the fusion weight of the satellite navigation system in combined navigation.

Description

Technical Field

[0001] This disclosure relates to the field of integrated navigation and positioning technology, and in particular to a method, apparatus and storage medium for determining the fusion weights of satellite navigation. Background Technology

[0002] Integrated navigation technology, combining Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS), has been widely applied in fields such as autonomous driving, robot localization, and high-precision surveying due to its complementary advantages. In integrated navigation systems, GNSS serves as the primary absolute positioning observation source, and the reliability of its data directly determines the accuracy of the final positioning result. However, due to complex geographical environments such as urban canyons, tree-lined roads, and tall buildings obstructing the view, GNSS signals are highly susceptible to multipath effects or non-line-of-sight errors. Currently, existing technologies for integrated navigation fusion often lack real-time, comprehensive evaluation methods for GNSS positioning status, resulting in the system's inability to adjust its trust level in GNSS data in a timely manner when GNSS signal quality deteriorates. This not only leads to insufficient accuracy in integrated navigation positioning results but may even cause severe positioning drift. Therefore, the issue of weighting satellite navigation systems in integrated navigation deserves attention. Summary of the Invention

[0003] In view of this, the present disclosure provides a method, apparatus and storage medium for determining the fusion weights of satellite navigation, in order to achieve quality assessment of the positioning results of the satellite navigation and positioning system in the application scenario of integrated navigation.

[0004] Firstly, a method for determining fusion weights in satellite navigation is provided for integrated navigation of a satellite navigation system and an inertial navigation system. The method includes: acquiring a first positioning result from the satellite navigation system and determining the positioning noise of the satellite navigation system based on the first positioning result; performing a chi-square test on the first positioning result to determine the positioning chi-square result; acquiring the satellite navigation system's characteristic value, positioning quality, and velocity stability quality corresponding to the first positioning result, wherein the positioning quality is determined based on the trajectory smoothness of the satellite navigation system, and the velocity stability quality is determined based on the speed and wheel speed differences of the satellite navigation system; determining whether the inertial navigation system is in a stable state; when the inertial navigation system is in a stable state, determining fusion weights based on the positioning noise, the positioning chi-square result, the satellite navigation system's characteristic value, the positioning quality, and the velocity stability quality; when the inertial navigation system is in an unstable state, determining fusion weights based on the satellite navigation system's characteristic value, the positioning quality, and the velocity stability quality; wherein the fusion weights are used to determine the confidence level of the first positioning result in the integrated navigation.

[0005] The above-described method for determining fusion weights in satellite navigation addresses the problem of overly singular dimensions in traditional evaluation methods by constructing a multi-dimensional evaluation logic that includes trajectory smoothness and cross-sensor velocity consistency. This significantly improves the positioning and identification capabilities of the integrated navigation system for satellite navigation and positioning systems. By introducing the inertial navigation system's stable state as a criterion, adaptive switching of the weight evaluation strategy is achieved under different system operating conditions. This ensures both high-precision dynamic control of the system after inertial navigation convergence and system stability during the instability phase of inertial navigation. Furthermore, this application, through the reconstruction of fusion weights, effectively suppresses positioning drift in complex environments, improving the positioning accuracy and reliability of the integrated navigation system.

[0006] Optionally, the stable state of the inertial navigation system is determined by at least one of the following methods: determining whether the first duration of continuous positioning of the satellite navigation system exceeds a first time threshold; determining whether the peak value of the gyroscope Z-axis zero offset within a first preset time window is less than a first peak value threshold; determining whether the second duration of entering integrated navigation exceeds a second time threshold; and determining whether the status position of the inertial navigation system is normal.

[0007] Optionally, determining the speed control quality includes: when the wheel speed of the carrier carrying the integrated navigation system is detected to be valid, within a second preset time window, calculating the speed difference between the speed of the satellite navigation system and the wheel speed at the first continuous positioning time, and forming a speed difference sequence; based on the speed difference sequence, determining the speed mean and speed standard deviation; and based on the speed mean and speed standard deviation, determining the speed control quality.

[0008] Optionally, determining the positioning quality includes: calculating the trajectory of the satellite navigation system based on the first positioning results at consecutive time intervals; calculating the change of the trajectory within a third preset time window to determine the trajectory sequence; calculating the dispersion of the trajectory and the maximum trajectory deviation based on the trajectory sequence; and determining the trajectory smoothness based on the dispersion of the trajectory and the maximum trajectory deviation.

[0009] Optionally, before calculating the trajectory of the satellite navigation system based on the first positioning results at consecutive time points, the method further includes: initiating the calculation of the trajectory when the first positioning result and the second positioning result of the inertial navigation system meet preset validity conditions; wherein, the preset validity conditions include: when the wheel speed of the carrier carrying the integrated navigation is valid, the wheel speed is greater than a first speed threshold, the latitude and longitude in the positioning result at the previous positioning time are both greater than zero, and the time difference between adjacent detection epochs meets the preset satellite navigation system sampling interval; when the wheel speed is invalid, the first speed result in the first positioning result or the second speed result in the second positioning result is greater than the first speed threshold, the latitude and longitude in the positioning result at the previous positioning time are both greater than zero, and the time difference between adjacent detection epochs meets the preset satellite navigation system sampling interval.

[0010] Optionally, determining trajectory smoothness based on the dispersion of the trajectory and the maximum trajectory deviation includes: matching the dispersion of the trajectory and the maximum trajectory deviation with multiple preset dispersion thresholds and multiple maximum trajectory deviation thresholds based on the fixed solution type of the first positioning result, and determining the corresponding trajectory quality level as the trajectory smoothness.

[0011] Optionally, the satellite navigation system characteristic values ​​include at least one of the following parameter types: positioning solution type, standard deviation, signal-to-noise ratio, number of satellites, horizontal accuracy factor, carrier and pseudorange residuals.

[0012] Optionally, the first positioning result includes velocity result and position result; the positioning noise includes velocity noise and position noise; the positioning chi-square result includes velocity chi-square value and position chi-square value.

[0013] Secondly, a fusion weight determination device for satellite navigation is provided for integrated navigation of a satellite navigation system and an inertial navigation system, comprising: a first acquisition unit for acquiring a first positioning result of the satellite navigation system and determining the positioning noise of the satellite navigation system based on the first positioning result; a chi-square unit for performing a chi-square test on the first positioning result to determine the positioning chi-square result; a second acquisition unit for acquiring the satellite navigation system feature value, positioning quality, and velocity stability quality corresponding to the first positioning result, wherein the positioning quality is determined based on the trajectory smoothness of the satellite navigation system, and the velocity stability quality is determined based on the speed and wheel speed difference of the satellite navigation system; and a weight determination unit for determining whether the inertial navigation system is in a stable state; when the inertial navigation system is in a stable state, determining the fusion weight based on the positioning noise, the positioning chi-square result, the satellite navigation system feature value, the positioning quality, and the velocity stability quality; and when the inertial navigation system is in an unstable state, determining the fusion weight based on the satellite navigation system feature value, the positioning quality, and the velocity stability quality; wherein the fusion weight is used to determine the confidence level of the first positioning result in integrated navigation.

[0014] Thirdly, a computer-readable storage medium is provided, on which instructions are stored, which, when read by a processor, implement the satellite navigation fusion weight determination method provided in the first aspect above. Attached Figure Description

[0015] The accompanying drawings used in the description of the embodiments of this disclosure are briefly introduced below: Figure 1 The diagram shows a flowchart of a satellite navigation fusion weight determination method provided in some embodiments of this application; Figure 2 A flowchart illustrating a method for determining positioning quality provided in some embodiments of this application is shown. Figure 3A flowchart illustrating another method for determining positioning quality provided in some embodiments of this application is shown. Figure 4 The diagram shows a structural schematic of a satellite navigation fusion weight determination device provided in some embodiments of this application. Detailed Implementation

[0016] To more clearly illustrate the technical solutions in the embodiments of this disclosure, examples of implementation methods of this disclosure will be described below with reference to the accompanying drawings. The accompanying drawings described below are merely some embodiments of this disclosure. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without creative effort. Adjustments and improvements made without departing from the concept of this disclosure are all within the protection scope of this disclosure.

[0017] To keep the drawings simple, each figure only schematically shows the parts relevant to the embodiment, and they do not represent the actual structure of the product. In addition, for the sake of clarity and ease of understanding, some figures only schematically show parts of components with the same structure or function, and there may actually be more or fewer components with the same structure or function.

[0018] In this disclosure, unless otherwise expressly specified and limited, ordinal numbers, such as “first”, “second”, etc., are used only to distinguish and describe related objects, and should not be construed as indicating or implying the relative importance or order between related objects; furthermore, they do not represent the quantity of related objects. “Multiple” includes two or more, and other quantifiers are similar.

[0019] Global Navigation Satellite Systems (GNSS), as crucial infrastructure for acquiring position and velocity information, have become indispensable components in fields such as autonomous driving and industrial robotics. To compensate for the vulnerability of GNSS systems to signal blockage, they are typically deeply integrated with Inertial Navigation Systems (INS), leveraging the smooth calculation capabilities of INS over short periods to mitigate fluctuations in GNSS performance. However, in real-world applications, particularly in complex conditions such as busy urban areas, densely packed elevated roads, and tunnel entrances, the positioning performance of GNSS systems faces significant challenges. Existing quality control methods often have significant limitations: firstly, current fusion algorithms typically rely solely on the standard deviation or solution type directly output by the GNSS receiver as a quality assessment indicator, neglecting the actual motion of the vehicle and the data consistency between different positioning sensors. This results in a single evaluation dimension, making it difficult to accurately identify hidden anomalies and drifts in the GNSS system. Secondly, existing technologies often fail to adequately consider the impact of the INS's own state on the fusion logic, and directly introducing complex chi-square tests or noise estimation algorithms before the INS has stabilized may introduce additional errors. Because the positioning and velocity determination quality of GNSS in complex dynamic environments cannot be accurately assessed, integrated navigation systems cannot achieve optimal fusion weight allocation when faced with poor-quality GNSS data. Under extreme conditions, this blind reliance or delayed evaluation can significantly reduce the positioning accuracy and stability of integrated navigation, and even threaten the operational safety of the system. Therefore, how to construct a multi-dimensional GNSS quality evaluation system and, in conjunction with the stability state of the inertial navigation system, accurately determine the fusion weights of each positioning result in integrated navigation is a pressing technical problem in the field of integrated navigation technology. The following description, with reference to the accompanying figures, illustrates this issue. Figure 1 The diagram illustrates a flowchart of a satellite navigation fusion weight determination method provided in some embodiments of this application. This fusion weight determination method is used for combined navigation of a satellite navigation system and an inertial navigation system, and includes at least the following steps: S110: Obtain the first positioning result of the satellite navigation system and determine the positioning noise of the satellite navigation system based on the first positioning result; S120: Perform a chi-square test on the first positioning result to determine the positioning chi-square result; S130: Obtain the satellite navigation system feature value, positioning quality, and velocity stability quality corresponding to the first positioning result, wherein the positioning quality is determined based on the trajectory smoothness of the satellite navigation system, and the velocity stability quality is determined based on the speed and wheel speed difference of the satellite navigation system; S140: Determine whether the inertial navigation system is in a stable state; S141: When the inertial navigation system is in a stable state, the fusion weights are determined based on the positioning noise, positioning chi-square result, satellite navigation system eigenvalues, positioning quality, and velocity stability quality. S142: When the inertial navigation system is in an unstable state, the fusion weights are determined based on the satellite navigation system's characteristic values, positioning quality, and velocity stability quality; wherein, the fusion weights are used to determine the confidence level of the first positioning result in the integrated navigation.

[0020] The carrier in the above embodiments can be a platform capable of carrying integrated navigation equipment, such as a car, ship, robot, aircraft, or other equipment requiring positioning using integrated navigation equipment. During the operation of integrated navigation, a first positioning result from the satellite navigation system can be obtained. This first positioning result reflects the spatial state information of the carrier at the current moment, including, but not limited to, parameters such as the carrier's position coordinates or velocity. While acquiring this data, the positioning noise of the satellite navigation system can be determined simultaneously. This positioning noise can be a variance or covariance matrix representing the measurement error, which can be used to quantitatively evaluate the dispersion and uncertainty of the current observation data in real time. When external signal interference is significant, the noise value will increase accordingly, indicating a possible decrease in the accuracy of the current measurement data. To further ensure that the data output by the satellite navigation system has not experienced abnormal deviations, a chi-square test can be performed on the first positioning result to determine the positioning chi-square result. In this application, the chi-square test is a residual-based consistency detection technique. It compares the actual observations of the satellite navigation system with the theoretical observations predicted by the inertial navigation system and constructs a test quantity that conforms to a specific statistical distribution. If the test quantity exceeds the set threshold, it indicates that the positioning chi-square result is abnormal, which means that the satellite navigation signal may have been affected by non-line-of-sight multipath interference or satellite jump, thus providing an important detection basis for subsequent fusion weight allocation.

[0021] In addition to the parameters calculated in real time as described above, this embodiment can also enhance the comprehensiveness of satellite navigation system quality control by acquiring multi-dimensional parameters. For example, it can include acquiring satellite navigation system feature values, positioning quality, and velocity control quality corresponding to the first positioning result. Satellite navigation system feature values ​​can reflect the original physical attributes of satellite signals; positioning quality can be determined based on the trajectory smoothness of the satellite navigation system, and abnormal jump points where trajectory distortion occurs can be identified by analyzing the geometric coherence of the satellite navigation positioning point in the trajectory during the motion process. When determining velocity control quality, taking a vehicle as an example, it can be determined based on the difference between the speed of the satellite navigation system and the wheel speed of the vehicle. Thus, by introducing sensors such as wheel speedometers equipped on the vehicle as external references, the confidence level of the satellite speed measurement results can be verified by utilizing dynamic consistency.

[0022] Furthermore, this application can determine the fusion weights of the first positioning result in integrated navigation based on the aforementioned acquired and determined parameters and the stable state of the inertial navigation system. The stable state of the inertial navigation system reflects the convergence of its internal state variables. When the inertial navigation system is in a stable state, since it can provide reliable short-term motion references, five key indicators—positioning noise, positioning chi-square result, satellite navigation system eigenvalues, positioning quality, and velocity stability quality—can be comprehensively considered to finely adjust the fusion weights through the logical coupling of multiple features. However, when the inertial navigation system is in an unstable state, such as during system initialization or when the inertial navigation sensor malfunctions, it is necessary to avoid prediction residuals that may have large deviations and instead determine the fusion weights primarily based on indicators with clear external physical meaning, such as satellite navigation system eigenvalues, positioning quality, and velocity stability quality. The final determined fusion weights directly represent the degree of trust the integrated navigation algorithm has in the satellite navigation system's positioning results during data fusion calculation. The larger the weight value, the more the system trusts the satellite navigation system's data during the final solution. This application addresses the problem of overly simplistic dimensions in traditional evaluation methods by constructing a multi-dimensional evaluation logic that includes trajectory smoothness and cross-sensor velocity consistency, significantly improving the positioning and identification capabilities of the integrated navigation system for satellite navigation and positioning systems. By introducing the inertial navigation system's stable state as a criterion, it achieves adaptive switching of weight evaluation strategies under different system operating conditions, ensuring both high-precision dynamic control after inertial navigation convergence and system stability during the instability phase. Finally, through precise reconstruction of the fused weights, it effectively suppresses positioning drift in complex environments, improving the positioning accuracy and reliability of the integrated navigation system.

[0023] In some embodiments of this application, the stable state of the inertial navigation system is determined by at least one of the following methods: determining whether the first duration of continuous positioning of the satellite navigation system exceeds a first time threshold; determining whether the peak value of the gyroscope Z-axis zero offset peak of the inertial navigation system within a first preset time window is less than a first peak value threshold; determining whether the second duration of entering integrated navigation exceeds a second time threshold; and determining whether the status position of the inertial navigation system is normal.

[0024] Determining whether an inertial navigation system (INS) has entered a stable state is a prerequisite for implementing a differentiated weight allocation strategy. The system can flexibly determine this using multiple criteria. For example, it can be measured by monitoring the first duration of continuous positioning achieved by the satellite navigation system. When this duration exceeds a pre-set first time threshold, it usually means that the satellite navigation system has passed the initial fluctuation period, and its output data sequence has become smooth and stable. Simultaneously, the internal physical parameters of the INS are also important criteria for determining stability. The system can monitor in real time the fluctuation of the gyroscope's Z-axis zero-bias peak-to-peak value within a specific first preset time window. Zero bias refers to the sensor's output deviation in a static state, and the stability of the Z-axis zero bias directly affects the accuracy of the vehicle's heading angle calculation. When this peak-to-peak value is less than a pre-set first peak threshold, it indicates that the gyroscope's random noise is at an extremely low level, meaning the INS has the ability to provide high-precision motion reference. Furthermore, the cumulative duration of entering the integrated navigation mode can also be used as a reference indicator. When this second duration exceeds a second time threshold, the internal algorithm or related configuration of the integrated navigation system has usually completed the convergence process. Finally, the internal status bits of the inertial navigation system can be read in real time. If the status bit displays normally, it can be directly confirmed that the inertial navigation system has entered a stable operating phase. The above-mentioned indicators for determining the stable state of the inertial navigation system can be used selectively. For example, to pursue efficiency, only the intuitive and relatively reliable status bit indicators of the inertial navigation system can be used to determine whether a stable state has been entered. Alternatively, two, three, or more indicators can be selected for comprehensive judgment to improve the accuracy of the judgment. Through the above-mentioned multi-type joint judgment mechanism, it can be ensured that the integrated navigation system can flexibly switch evaluation logic based on the real-time reliability of the inertial navigation reference in the subsequent weight calculation process, thereby improving the robustness of the positioning scheme.

[0025] In some embodiments of this application, determining the constant speed quality includes: when the wheel speed of the carrier carrying the integrated navigation system is detected to be valid, within a second preset time window, calculating the speed difference between the speed of the satellite navigation system and the wheel speed at the first continuous positioning time, and forming a speed difference sequence; determining the speed mean and speed standard deviation based on the speed difference sequence; and determining the constant speed quality based on the speed mean and speed standard deviation.

[0026] In the embodiments of this application, the assessment of velocity stability is a key means of determining the confidence level of the satellite navigation system's velocity measurement. This velocity stability essentially reflects the degree of consistency between the dynamic information output by the satellite navigation system and the actual physical motion of the carrier. In this application, the wheel speed data fed back by the carrier carrying the integrated navigation system can be monitored first. When the wheel speed data is confirmed to be valid, a specific second preset time window can be selected, such as a time window containing multiple sampling epochs, and within this window, the difference between the velocity measurement value of the satellite navigation system and the synchronously acquired wheel speed value at the first consecutive positioning moment can be continuously calculated. Through this difference calculation, multiple instantaneous velocity points can be transformed into a velocity difference sequence that reflects the trend of deviation evolution over time.

[0027] After acquiring the velocity difference sequence, the mean and standard deviation of the velocity can be calculated. The mean velocity measures whether there is a persistent systematic error in the satellite velocity measurement results. For example, non-line-of-sight signal interference in urban canyon environments often leads to an overall shift in velocity values. The standard deviation of the velocity characterizes the dispersion of velocity fluctuations and can effectively identify instantaneous data jumps caused by multipath effects. Finally, by comprehensively judging the magnitude of the mean and standard deviation of the velocity, the final velocity determination quality is determined. This quantification method of velocity determination quality not only considers the magnitude of the error but also its stability. By deeply coupling the dynamic observations of the satellite navigation system with the physical constraints of the carrier chassis, the problem of a single sensor being unable to self-prove velocity measurement accuracy is effectively solved. Abnormal data under complex conditions such as wheel slippage or satellite signal multipath interference are identified, providing a high-confidence logical criterion for the subsequent adaptive adjustment of fusion weights.

[0028] Figure 2 A flowchart illustrating a method for determining positioning quality according to some embodiments of this application is shown. The method includes: S210: Calculate the trajectory of the satellite navigation system based on the first positioning results at consecutive time points; S220: Calculate the change in course within the third preset time window and determine the course sequence; S230: Based on the heading sequence, calculate the dispersion of the heading and the maximum heading deviation; S240: Determine trajectory smoothness based on the dispersion of the course and the maximum course deviation.

[0029] The core of positioning quality assessment lies in the in-depth analysis of the geometric and physical characteristics of the satellite navigation system's output trajectory. In this application, the first positioning results at consecutive time points can be used to calculate the satellite navigation system's trajectory orientation, thereby reflecting the instantaneous motion vector direction of the carrier between adjacent sampling epochs. To capture abnormal disturbances during motion, the change in trajectory orientation within a specific third preset time window can be calculated, thus constructing a complete trajectory orientation sequence to record the evolution of the carrier's motion direction over time. After obtaining the trajectory orientation sequence, the dispersion of the trajectory orientation and the maximum trajectory orientation deviation can be calculated. The dispersion of the trajectory orientation can be determined using parameters such as the trajectory orientation standard deviation, the trajectory orientation mean absolute deviation, and the median absolute deviation. For example, taking the trajectory orientation standard deviation as an example, this parameter can be used to measure the overall dispersion of the carrier's motion direction over a period of time, effectively reflecting subtle trajectory jitters; while the maximum trajectory orientation deviation can pinpoint the most drastic direction jump within that time window, thereby capturing the sudden changes in positioning points caused by multipath interference or satellite switching. Finally, based on the calculated standard deviation and maximum deviation of the trajectory, the smoothness of the trajectory can be comprehensively determined. In this way, the originally abstract geometric features of the trajectory are transformed into quality evaluation indicators with quantitative levels.

[0030] Furthermore, when determining the trajectory smoothness based on the dispersion of the trajectory and the maximum trajectory deviation, it may include: based on the fixed solution type of the first positioning result, matching the dispersion of the trajectory and the maximum trajectory deviation with multiple preset dispersion thresholds of the trajectory and multiple maximum trajectory deviation thresholds to determine the corresponding trajectory quality level as the trajectory smoothness.

[0031] The process of determining trajectory smoothness can employ refined classification and judgment logic to adapt to the inherent noise levels of satellite navigation systems under different positioning states. For example, the fixed solution type of the first positioning result can be identified first. In the field of satellite positioning technology, a fixed solution represents a situation where the satellite navigation system receiver has successfully solved the integer ambiguity of the carrier phase, and the positioning accuracy is at the centimeter level (or higher). Therefore, the trajectory corresponding to this fixed solution can possess high geometric smoothness. Based on this prior state, the previously calculated dispersion of the course direction and the maximum course direction deviation can be hierarchically matched with multiple preset sets of course direction dispersion thresholds and multiple sets of maximum course direction deviation thresholds. When the positioning solution type is a fixed solution, a more stringent first set of thresholds can be used for matching. For example, taking the course direction standard deviation as an example, the course direction standard deviation can be required to be less than x degrees and the maximum course direction deviation less than y degrees. If these conditions are met, the trajectory quality level is determined to be optimal. When the localization solution is a non-fixed solution, such as a floating-point solution or a single-point localization solution, considering the instability in its signal processing, the judgment boundary can be dynamically widened. For example, the corresponding track direction standard deviation can be widened to less than x+1 degrees and the maximum track direction deviation to less than y+1 degrees. By setting differentiated thresholds for different localization solution states in this way, the calculated statistical feature values ​​can be accurately mapped to multiple preset trajectory quality levels, and the final matched level result can be used as a quantitative indicator to characterize the trajectory smoothness. In some examples of this application, the trajectory quality level can be defined as a discrete level from a value of 1 to a value of 9, where a value of 1 represents excellent trajectory smoothness, while a value of 9 represents severe distortion or abrupt changes in the trajectory. The above values ​​are only examples; the actual value settings can be set according to engineering design requirements, such as setting them to binary numbers in machine language, etc., and are not specifically limited here.

[0032] In some embodiments of this application, before calculating the trajectory of the satellite navigation system based on the first positioning results at consecutive time points, the method further includes: initiating the calculation of the trajectory when the first positioning result and the second positioning result of the inertial navigation system meet preset validity conditions; wherein, the preset validity conditions include: when the wheel speed of the carrier carrying the integrated navigation is valid, the wheel speed is greater than a first speed threshold, the latitude and longitude in the positioning result at the previous positioning time are both greater than zero, and the time difference between adjacent detection epochs meets the preset satellite navigation system sampling interval; when the wheel speed is invalid, the first speed result in the first positioning result or the second speed result in the second positioning result is greater than the first speed threshold, the latitude and longitude in the positioning result at the previous positioning time are both greater than zero, and the time difference between adjacent detection epochs meets the preset satellite navigation system sampling interval.

[0033] To ensure the physical accuracy of the trajectory calculation and eliminate noise interference, a data validity determination logic can be pre-executed before calculating the trajectory based on the first positioning results from consecutive time points. The system will only formally initiate the trajectory calculation process when both the first positioning result and the second positioning result output by the inertial navigation system meet the preset validity conditions. This preprocessing step aims to filter out non-ideal positioning points under conditions such as the vehicle being stationary, moving at low speeds, or experiencing abnormal sensor data, thereby preventing invalid data from participating in the smoothness evaluation.

[0034] In the preset validity conditions, when the wheel speed data fed back by the vehicle equipped with the integrated navigation system is detected to be valid, wheel speed can be prioritized as the judgment criterion. At this time, the wheel speed must be greater than a preset first speed threshold to ensure that the vehicle is in a driving state with a clear direction of motion. This first speed threshold can be determined based on the conventional speed setting under actual testing or application scenarios where the wheel speed can be judged as valid. Simultaneously, the spatial attributes of the positioning data from the satellite navigation system can be verified, requiring that the latitude and longitude values ​​in the positioning result of the previous positioning time are all greater than 0, thereby confirming that the navigation and positioning system has entered a valid output state of the global coordinate system. Furthermore, the continuity of the time dimension can also be used as a key criterion, requiring that the time difference between adjacent detection epochs must meet the preset sampling interval requirements of the satellite navigation system. For example, this time difference should be within a preset multiple of the GNSS sampling interval range to prevent mathematical logical jumps in trajectory calculation due to frame loss or severe delay.

[0035] In another operating condition, when wheel speed data is invalid due to hardware failure, communication interruption, or speed not meeting requirements, the system can switch to a judgment logic based on the speed results within the integrated navigation system. If at least one of the first speed results from the first positioning result or the second speed result from the second positioning result is greater than the aforementioned first speed threshold, and simultaneously satisfies the constraints that the latitude and longitude at the previous positioning time are both greater than zero and the time difference between adjacent detection epochs is within a reasonable sampling range, the system will also determine that the current positioning status is valid and initiate trajectory calculation. Through this mutually redundant judgment criterion, the validity of positioning data can be accurately controlled under various sensor operating conditions.

[0036] Figure 3 A flowchart illustrating another method for determining positioning quality provided in some embodiments of this application is shown.

[0037] S301: Reset trajectory smoothness level; S302: Determine if the wheel speed is valid; if the wheel speed is valid, proceed to step S303; if the wheel speed is invalid, proceed to step S304. S303: Determine whether the wheel speed is greater than the first speed threshold, whether the latitude and longitude in the positioning result of the second epoch are greater than zero, and whether the time difference between adjacent first and second epochs meets the preset satellite navigation system sampling interval; if the aforementioned conditions are met, execute step S305; if the aforementioned conditions are not met, execute step S315. S304: Determine whether the first velocity result of the satellite navigation system or the second velocity result of the inertial navigation system is greater than the first velocity threshold, whether the latitude and longitude in the positioning result of the second epoch are greater than zero, and whether the time difference between the first epoch and the second epoch meets the preset sampling interval of the satellite navigation system; if the aforementioned conditions are met, proceed to step S305; if the aforementioned conditions are not met, proceed to step S315. S305: Calculate the course direction based on the forward and backward position changes of the satellite navigation system; S306: Determine if the course direction of the second epoch is valid; if valid, proceed to step S307; if invalid, proceed to step S314. S307: Determine the change in track direction based on the difference between the track direction in the first epoch and the track direction in the second epoch, and perform angle reduction.

[0038] S308: Based on the changes in course within the third preset time window, determine the course change sequence, and calculate the course standard deviation and maximum course deviation of the course change sequence; S309: When the first positioning result is a fixed solution, and the standard deviation of the trajectory is less than x degrees and the maximum trajectory deviation is less than y degrees; or when the first positioning result is a non-fixed solution, and the standard deviation of the trajectory is less than x+1 degrees and the maximum trajectory deviation is less than y+1 degrees, the trajectory smoothness level is determined to be level 1; if the requirements of step S309 are not met, the judgment in S310 is further executed. S310: If the standard deviation of the trajectory is less than x+1 degrees and the maximum trajectory deviation is less than y+1 degrees, the trajectory smoothness level is determined to be level 2; if the requirements of step S310 are not met, the judgment of S311 is further executed. S311: If the standard deviation of the trajectory is less than x+2 degrees and the maximum trajectory deviation is less than y+2 degrees, the trajectory smoothness level is determined to be level 3; if the requirements of step S311 are not met, the judgment in S312 is further executed. S312: When the first positioning result is a fixed solution, and the standard deviation of the trajectory is less than x+10 degrees and the maximum trajectory deviation is less than y+20 degrees, or when the first positioning result is a non-fixed solution, and the standard deviation of the trajectory is less than x+20 degrees and the maximum trajectory deviation is less than y+40 degrees, the trajectory smoothness level is determined to be level 4; if the requirements of step S312 are not met, the trajectory smoothness level is initially determined to be level 5, and the judgment in step S313 is executed. S313: When the standard deviation of the trajectory is greater than x+40 degrees and the maximum trajectory deviation is greater than y+100 degrees, the trajectory smoothness level is determined to be level 9. S314: Update the trajectory at the previous positioning time to the trajectory at the current positioning time; S315: Clear the trajectory change buffer and determine the trajectory of the second epoch as invalid.

[0039] In some embodiments of this application, the satellite navigation system characteristic values ​​may include at least one of the following parameter types: positioning solution type, standard deviation, signal-to-noise ratio, number of satellites, horizontal accuracy factor, carrier wave and pseudorange residual.

[0040] In some embodiments of this application, the first positioning result includes velocity result and position result; the positioning noise includes velocity noise and position noise; and the positioning chi-square result includes velocity chi-square value and position chi-square value.

[0041] Satellite navigation system eigenvalues, as fundamental parameters for evaluating the positioning environment, can encompass various parameter types reflecting both the physical and algorithmic levels of the signal. For example, they can include positioning solution types reflecting the fixed state of the GNSS receiver's ambiguity regarding carrier phase integer cycles. Standard deviation reflects the statistical dispersion of the positioning output value. Signal-to-noise ratio (SNR) characterizes the ratio of the useful signal strength to the ambient background noise intensity. Satellite number refers to the number of effective satellites participating in the positioning solution for the current epoch. Horizontal accuracy factor describes the impact of the spatial distribution geometry of satellites within the carrier's horizontal plane on positioning accuracy; a smaller value indicates a more ideal geometric distribution. Carrier and pseudorange residuals reflect the residual deviation between the original observations and the final calculated positioning point, and can be used to measure the internal consistency of the observation data and identify gross errors. This multi-dimensional set of eigenvalues ​​provides a detailed physical basis for determining the quality of the original data of the current satellite navigation system.

[0042] In this application, the first positioning result, as the main input of the integrated navigation system, can include velocity and position results, thus providing a complete description of the vehicle's motion vector and absolute coordinate information in three-dimensional space. To achieve accurate calculation of the confidence level of these observation data, the integrated navigation positioning system can further determine the velocity noise corresponding to the velocity result and the positioning noise corresponding to the positioning result, thereby enabling the quantitative evaluation of the uncertainties of the satellite navigation system in both the motion process and spatial position determination dimensions. Correspondingly, during further chi-square testing, the velocity and positioning results can be detected separately to determine the corresponding velocity and position chi-square values. This allows for independent fault detection and evaluation for velocity and distance measurement anomalies. This fine-grained data processing approach enables the system to more scientifically reconstruct the fusion weights based on the differentiated performance of velocity and position deviations, thereby significantly improving the positioning accuracy of the integrated navigation system in dynamic environments.

[0043] Figure 4 A schematic diagram of a satellite navigation fusion weight determination device 400 provided in some embodiments of this application is shown. This fusion weight determination device 400 is used for combined navigation of a satellite navigation system and an inertial navigation system, and includes: a first acquisition unit 410, used to acquire a first positioning result of the satellite navigation system and determine the positioning noise of the satellite navigation system based on the first positioning result; a chi-square unit 420, used to perform a chi-square test on the first positioning result to determine the positioning chi-square result; a second acquisition unit 430, used to acquire the satellite navigation system feature value, positioning quality, and velocity stability quality corresponding to the first positioning result, wherein the positioning quality is determined based on the trajectory smoothness of the satellite navigation system, and the velocity stability quality is determined based on the speed and wheel speed difference of the satellite navigation system; and a weight determination unit 440, used to determine whether the inertial navigation system is in a stable state; when the inertial navigation system is in a stable state, a fusion weight is determined based on the positioning noise, the positioning chi-square result, the satellite navigation system feature value, the positioning quality, and the velocity stability quality; when the inertial navigation system is in an unstable state, a fusion weight is determined based on the satellite navigation system feature value, the positioning quality, and the velocity stability quality; wherein the fusion weight is used to determine the confidence level of the first positioning result in the combined navigation.

[0044] The specific implementation description and determination of the beneficial effects of the above embodiments can be referred to the embodiments of the satellite navigation fusion weight determination method described above, and will not be repeated here.

[0045] Based on the same technical concept, this application also provides a computer-readable storage medium storing instructions thereon, which, when read by a processor, implement the satellite navigation fusion weight determination method provided in the above embodiments.

[0046] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not described in detail or in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Furthermore, the above embodiments can be freely combined as needed.

Claims

1. A method for determining fusion weights in satellite navigation, characterized in that, Combined navigation for satellite navigation systems and inertial navigation systems, including: Obtain the first positioning result of the satellite navigation system, and determine the positioning noise of the satellite navigation system based on the first positioning result; Perform a chi-square test on the first positioning result to determine the positioning chi-square result; Obtain the satellite navigation system feature value, positioning quality, and velocity control quality corresponding to the first positioning result, wherein the positioning quality is determined based on the trajectory smoothness of the satellite navigation system, and the velocity control quality is determined based on the speed and wheel speed difference of the satellite navigation system; Determine whether the inertial navigation system is in a stable state; When the inertial navigation system is in a stable state, the fusion weights are determined based on the positioning noise, the positioning chi-square result, the satellite navigation system feature value, the positioning quality, and the velocity stability quality. When the inertial navigation system is in an unstable state, the fusion weights are determined based on the feature values ​​of the satellite navigation system, the positioning quality, and the velocity stability quality. The fusion weight is used to determine the degree of trust in the first positioning result in the integrated navigation. The determination of the constant speed quality includes: when the wheel speed of the carrier carrying the integrated navigation system is detected to be valid, within a second preset time window, calculating the speed difference between the speed of the satellite navigation system and the wheel speed, and forming a speed difference sequence; based on the speed difference sequence, determining the speed mean and speed standard deviation; and based on the speed mean and speed standard deviation, determining the constant speed quality. The determination of positioning quality includes: calculating the trajectory of the satellite navigation system based on the first positioning results at consecutive time intervals; calculating the change of the trajectory within a third preset time window to determine the trajectory sequence; calculating the dispersion of the trajectory and the maximum trajectory deviation based on the trajectory sequence; and determining the trajectory smoothness based on the dispersion of the trajectory and the maximum trajectory deviation.

2. The method for determining fusion weights according to claim 1, characterized in that, The stable state of the inertial navigation system is determined by at least one of the following methods: Determine whether the first duration of continuous positioning by the satellite navigation system exceeds a first time threshold; Determine whether the peak value of the gyroscope Z-axis zero offset peak of the inertial navigation system within the first preset time window is less than the first peak value threshold. Determine whether the second duration of entering the integrated navigation exceeds the second time threshold; Determine whether the status bits of the inertial navigation system are normal.

3. The method for determining fusion weights according to claim 1, characterized in that, Before calculating the trajectory of the satellite navigation system based on the first positioning results at consecutive time points, the method further includes: When the first positioning result and the second positioning result of the inertial navigation system meet the preset validity conditions, the calculation of the course direction is initiated; The preset validity conditions include: In the first epoch, when the wheel speed of the carrier carrying the integrated navigation is valid, the wheel speed is greater than the first speed threshold, the latitude and longitude in the positioning result of the second epoch are both greater than zero, and the time difference between adjacent first epochs and second epochs meets the preset satellite navigation system sampling interval. Alternatively, at the first epoch, when the wheel speed is invalid, the first speed result in the first positioning result or the second speed result in the second positioning result is greater than the first speed threshold, and the latitude and longitude in the positioning result of the second epoch are both greater than zero, and the time difference between adjacent first epochs and second epochs satisfies the preset satellite navigation system sampling interval. The second epoch is earlier than the first epoch.

4. The method for determining fusion weights according to claim 1, characterized in that, Determining the trajectory smoothness based on the dispersion of the trajectory and the maximum trajectory deviation includes: Based on the fixed solution type of the first positioning result, the dispersion of the trajectory and the maximum trajectory deviation are matched with multiple preset trajectory dispersion thresholds and multiple maximum trajectory deviation thresholds to determine the corresponding trajectory quality level as the trajectory smoothness.

5. The method for determining fusion weights according to any one of claims 1 to 4, characterized in that, The satellite navigation system characteristic values ​​include at least one of the following parameter types: positioning solution type, standard deviation, signal-to-noise ratio, number of satellites, horizontal accuracy factor, carrier wave and pseudorange residual.

6. The method for determining fusion weights according to any one of claims 1 to 4, characterized in that, The first positioning result includes velocity result and position result; The positioning noise includes velocity noise and position noise; The positioning chi-square result includes velocity chi-square value and position chi-square value.

7. A satellite navigation fusion weight determination device, characterized in that, Combined navigation for satellite navigation systems and inertial navigation systems, including: The first acquisition unit is used to acquire the first positioning result of the satellite navigation system and determine the positioning noise of the satellite navigation system based on the first positioning result; Chi-square unit, used to perform chi-square test on the first positioning result to determine the positioning chi-square result; The second acquisition unit is used to acquire the satellite navigation system feature value, positioning quality, and velocity control quality corresponding to the first positioning result, wherein the positioning quality is determined based on the trajectory smoothness of the satellite navigation system, and the velocity control quality is determined based on the speed and wheel speed difference of the satellite navigation system. A weight determination unit is used to determine whether the inertial navigation system is in a stable state; when the inertial navigation system is in a stable state, the fusion weight is determined based on the positioning noise, the positioning chi-square result, the satellite navigation system feature value, the positioning quality, and the velocity stability quality; when the inertial navigation system is in an unstable state, the fusion weight is determined based on the satellite navigation system feature value, the positioning quality, and the velocity stability quality; wherein, the fusion weight is used to determine the confidence level of the first positioning result in the integrated navigation; The determination of the constant speed quality includes: when the wheel speed of the carrier carrying the integrated navigation system is detected to be valid, within a second preset time window, calculating the speed difference between the speed of the satellite navigation system and the wheel speed, and forming a speed difference sequence; based on the speed difference sequence, determining the speed mean and speed standard deviation; and based on the speed mean and speed standard deviation, determining the constant speed quality. The determination of positioning quality includes: calculating the trajectory of the satellite navigation system based on the first positioning results at consecutive time intervals; calculating the change of the trajectory within a third preset time window to determine the trajectory sequence; calculating the dispersion of the trajectory and the maximum trajectory deviation based on the trajectory sequence; and determining the trajectory smoothness based on the dispersion of the trajectory and the maximum trajectory deviation.

8. A computer-readable storage medium, characterized in that, It stores instructions that, when read by a processor, implement the satellite navigation fusion weight determination method as described in any one of claims 1 to 6.

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