A navigation track fusion method based on multi-threshold decision and comprehensive similarity
Patent Information
- Application Number
- CN202611477550.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-09-22
- Publication Date
- 2026-10-30
AI Technical Summary
[0006]针对现有技术的不足，本发明提供了一种基于多门限判决和综合相似度的航行轨迹融合方法，用于解决现有技术存在难以适用多源数据、无法适配动态变化的观测环境与数值差异以及融合的时效性和准确性不足的技术问题
本发明基于航行轨迹信息构建航行轨迹数据库，实现对多源航行轨迹数据处理，将待融合轨迹与目标轨迹集中的目标轨迹，分别通过距离相似度、速度相似度与对应的预设阈值进行匹配判断，能够适配观测环境动态变化所引起的轨迹数值差异，对多条匹配目标轨迹计算综合相似度以筛选最优轨迹进行加权融合，能够提高轨迹匹配的精准性，将参与当前加权融合的轨迹从航行轨迹数据库中移除并对剩余航行轨迹循环执行融合步骤，能够减少后续匹配环节的数据计算量，提升航行轨迹融合处理的时效性。
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Abstract
Description
Technical Field
[0001] This invention relates to the field of trajectory fusion technology, and in particular to a navigation trajectory fusion method based on multi-threshold decision and comprehensive similarity. Background Technology
[0002] In applications such as marine maritime monitoring and maritime traffic control, ship-aircraft joint observation platforms can collaboratively collect navigation trajectories using multiple types of sensing devices, effectively compensating for the limitations of single observation platforms in terms of detection range, perspective flexibility, and operational timeliness. However, data from multiple observation platforms and various types of sensing devices exhibit significant heterogeneity, spatiotemporal asynchrony, and redundancy / conflict characteristics. Furthermore, these data are susceptible to factors such as complex sea conditions, external interference, and equipment errors, leading to issues like anomalies, duplicate identification of the same target source, and spatiotemporal matching deviations in navigation trajectory data. Therefore, data fusion processing of ship-aircraft navigation trajectories is of significant research importance.
[0003] Existing navigation trajectory data fusion methods are usually based on algorithms such as least squares and Kalman filtering, which have certain effects on single sensor data processing.
[0004] Existing flight trajectory fusion methods suffer from the following main drawbacks: First, methods based on single sensor data or simple weighted fusion strategies are ill-suited to complex scenarios involving multi-source heterogeneous data. For example, the least squares method is sensitive to noise and cannot effectively handle unstructured data. Second, existing trajectory data screening and discrimination methods often employ a single, fixed threshold mechanism, which cannot adapt to dynamically changing observation environments and numerical differences. For instance, it is prone to missing detections of weakly connected data and misjudging noisy data. Third, existing methods lack the ability to deeply mine trajectory similarity, and manual analysis is insufficient to meet the needs of dynamically changing trajectory associations, resulting in inadequate timeliness and accuracy of fusion.
[0005] Therefore, existing technologies have technical problems such as difficulty in applying to multi-source data, inability to adapt to dynamically changing observation environments and numerical differences, and insufficient timeliness and accuracy of data fusion. Summary of the Invention
[0006] To address the shortcomings of existing technologies, this invention provides a navigation trajectory fusion method based on multi-threshold decision and comprehensive similarity, which solves the technical problems of existing technologies, such as difficulty in applying multi-source data, inability to adapt to dynamically changing observation environments and numerical differences, and insufficient timeliness and accuracy of fusion.
[0007] The technical means employed in this invention are as follows: This invention provides a flight trajectory fusion method based on multi-threshold decision and comprehensive similarity, comprising: S1. Collect navigation trajectory information through multiple data acquisition units, and construct a navigation trajectory database based on the navigation trajectory information; S2. In the navigation trajectory database, any navigation trajectory collected by one of the data acquisition units is taken as the trajectory to be fused, and all navigation trajectories collected by the other data acquisition unit are used to construct the target trajectory set. S3. Calculate the distance similarity and velocity similarity between the trajectory to be fused and each target trajectory in the target trajectory set. Based on the relationship between the distance similarity and velocity similarity and the corresponding preset threshold, determine whether the trajectory to be fused matches each target trajectory. S4. If there is a target trajectory in the target trajectory set that matches the trajectory to be fused, perform weighted fusion on the trajectory to be fused and the matching target trajectory to generate the fused trajectory. If multiple target trajectories in the target trajectory set match the trajectory to be fused, calculate the comprehensive similarity between the trajectory to be fused and each matching target trajectory, and then perform a weighted fusion of the target trajectory with the highest comprehensive similarity and the trajectory to be fused to generate the fused trajectory. S5. Remove the trajectories to be fused and the target trajectories that are currently involved in the weighted fusion from the navigation trajectory database, and repeat steps S2 to S4 based on the remaining navigation trajectories in the navigation trajectory database until all fusionable trajectories have completed weighted fusion to obtain the navigation trajectory fusion result.
[0008] Furthermore, it also includes performing the following fusion steps for scattered trajectory points that were not included in the navigation trajectory: Calculate the association cost between each scattered trajectory point and each target trajectory in the target trajectory set; The scattered trajectory points are merged into the target trajectory with the lowest association cost.
[0009] Furthermore, the formula for calculating the associated cost is as follows:
[0010] in, Indicates the current trajectory to be merged. With the target trajectory The value of the relationship between them; Indicates the current trajectory to be merged. With the target trajectory The difference in distance between them; Indicates the current trajectory to be merged. With the target trajectory The angular difference between them; and These represent distance weight and angle weight, respectively.
[0011] Furthermore, the data acquisition unit is an infrared sensor, a photoelectric sensor, an inertial sensor, or a GPS positioning sensor.
[0012] Furthermore, when both the distance similarity and the velocity similarity are less than the corresponding preset thresholds, it is determined that the trajectory to be fused matches the target trajectory.
[0013] Furthermore, the formula for calculating the distance similarity is:
[0014] in, Indicates distance similarity; , and These represent the x, y, and z coordinates of the i-th navigation trajectory collected by data acquisition unit a at time t; , and These represent the x, y, and z coordinates of the j-th navigation trajectory collected by data acquisition unit b at time t.
[0015] Furthermore, the formula for calculating the velocity similarity is:
[0016] in, Indicates speed similarity; , and These represent the velocity information in the x, y, and z directions of the i-th navigation trajectory collected by data acquisition unit a at time t; , and These represent the velocity information in the x, y, and z directions of the j-th navigation trajectory collected by data acquisition unit b at time t.
[0017] Furthermore, the expression for the comprehensive similarity is:
[0018] in, Indicates overall similarity; This represents the weight of the k-th fuzzy factor; This represents the dimensional similarity mapping function; Let represent the Euclidean distance of the k-th fuzzy factor at time t.
[0019] Furthermore, it also includes: Determine whether the navigation trajectory information collected by a single data acquisition unit exists; If the data type representation of the navigation trajectory information is complete and the sensor status feedback parameters are intact, then the navigation trajectory information is determined to exist. If the sensor system status identification information, data validity identification information, and coordinate system type identification information are all correct, then it is determined that there is no missing data type representation. If the total amount of missing data for the returned detection distance parameter, returned field of view parameter, covariance estimation error parameter, and accuracy factor parameter is not greater than a preset percentage, then the sensor status returned parameters are considered complete.
[0020] Compared with the prior art, the present invention has the following advantages: This invention constructs a navigation trajectory database based on navigation trajectory information, enabling the processing of multi-source navigation trajectory data. It matches the trajectory to be fused with target trajectories in the target trajectory set using distance similarity, velocity similarity, and corresponding preset thresholds. This adapts to the numerical differences in trajectory caused by dynamic changes in the observation environment. It calculates the comprehensive similarity of multiple matching target trajectories to select the optimal trajectory for weighted fusion, improving the accuracy of trajectory matching. Removing the trajectory currently participating in the weighted fusion from the navigation trajectory database and repeatedly performing the fusion step on the remaining navigation trajectories reduces the amount of data computation in subsequent matching stages and improves the timeliness of navigation trajectory fusion processing. Attached Figure Description
[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0022] Figure 1 This is a flowchart illustrating a navigation trajectory fusion method based on multi-threshold decision and comprehensive similarity in an embodiment of the present invention. Figure 2 This is a flowchart illustrating the fusion process of the trajectory to be fused and the target trajectory in an embodiment of the present invention. Detailed Implementation
[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0024] It should be noted that the terms "comprising" and "having" and any variations thereof in this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.
[0025] The embodiments of the present invention will now be described with reference to the accompanying drawings.
[0026] Please see Figure 1 and Figure 2 This invention provides a navigation trajectory fusion method based on multi-threshold decision and comprehensive similarity, comprising the following steps: S1. Collect navigation trajectory information through multiple data acquisition units, and construct a navigation trajectory database based on the navigation trajectory information.
[0027] In some embodiments, the data acquisition unit is an infrared sensor, a photoelectric sensor, an inertial sensor, or a GPS positioning sensor.
[0028] After the data acquisition unit collects the navigation trajectory information, it formats and performs spatiotemporal unification processing on the navigation trajectory information data. Formatting realizes the unification of data format, and spatiotemporal unification processing realizes the generation of trajectory. All navigation trajectory information is unified according to the same time starting point and time interval, so that different navigation trajectory information is comparable.
[0029] S2. In the navigation trajectory database, any navigation trajectory collected by one of the data acquisition units is taken as the trajectory to be fused, and all navigation trajectories collected by the other data acquisition unit are used to construct the target trajectory set.
[0030] S3. Calculate the distance similarity and velocity similarity between the trajectory to be fused and each target trajectory in the target trajectory set. Based on the relationship between the distance similarity and velocity similarity and the corresponding preset threshold, determine whether the trajectory to be fused matches each target trajectory.
[0031] To ensure the accuracy of the matching, a three-level threshold was set to filter out one or more target trajectories that are similar to the trajectories to be fused.
[0032] The three-level threshold is as follows: The first threshold setting is to calculate the similarity between the starting point and the ending point of the trajectory to be fused and the target trajectory.
[0033] The feature vectors of the trajectory's starting and ending points need to satisfy the following constraints:
[0034] in, express The x-coordinate of the flight trajectory i collected by time data acquisition unit a; express The y-coordinate of the flight trajectory i collected by time data acquisition unit a; express The z-coordinate of the flight trajectory i collected by time data acquisition unit a; express The x-coordinate of the navigation trajectory j collected by the time data acquisition unit b; express The y-coordinate of the flight trajectory j collected by the time data acquisition unit b; express The z-coordinate of the flight trajectory j collected by time data acquisition unit b; , and These represent the first x-coordinate threshold, the first y-coordinate threshold, and the first z-coordinate threshold, respectively. Indicates the first threshold. When all When the two trajectories satisfy the first threshold rule, it is determined that they meet the first threshold rule.
[0035] Second threshold setting: Limitation of the average position error of the entire trajectory.
[0036] The error of the feature vector of the entire trajectory is calculated:
[0037] in, express The x-coordinate of the flight trajectory i collected by time data acquisition unit a; express The y-coordinate of the flight trajectory i collected by time data acquisition unit a; express The z-coordinate of the flight trajectory i collected by time data acquisition unit a; express The x-coordinate of the navigation trajectory j collected by the time data acquisition unit b; express The y-coordinate of the flight trajectory j collected by the time data acquisition unit b; express The z-coordinate of the flight trajectory j collected by time data acquisition unit b; , and These represent the second x-coordinate threshold, the second y-coordinate threshold, and the second z-coordinate threshold, respectively. Indicates the second threshold; when all When the two trajectories satisfy the second threshold rule, it is determined that they meet the second threshold rule.
[0038] The third threshold setting: Based on the motion information of the trajectory, the speed similarity of the trajectory is judged, and the speed error judgment condition is as follows:
[0039] in, express The velocity in the x-direction of the flight trajectory i collected by the time data acquisition unit a; express The y-direction velocity of the navigation trajectory i collected by the time data acquisition unit a; express The velocity in the z-direction of the flight trajectory i collected by the time data acquisition unit a; express The velocity in the x-direction of the flight trajectory j collected by the time data acquisition unit a; express The y-direction velocity of the navigation trajectory j collected by the time data acquisition unit a; express The velocity in the z-direction of the flight trajectory j collected by the time data acquisition unit a; , , These represent the velocity threshold values in the x, y, and z directions, respectively. Indicates the third threshold; when all At that time, it is determined that the two trajectories satisfy the third threshold rule.
[0040] When both navigation trajectory i and navigation trajectory j satisfy the three threshold decision conditions, it is determined that these two trajectories meet the matching conditions.
[0041] To improve decision-making efficiency, a simplified form of the multi-threshold formula is given, as follows: The distance similarity between the trajectory to be fused and the target trajectory is calculated as the first threshold criterion. The formula for calculating the distance similarity is:
[0042] in, Indicates distance similarity; , and These represent the x, y, and z coordinates of the i-th navigation trajectory collected by data acquisition unit a at time t; , and These represent the x, y, and z coordinates of the j-th navigation trajectory collected by data acquisition unit b at time t.
[0043] Considering the potential biases between different data types, selection is based on experience. Threshold ,when If yes, proceed to the next step of judgment; otherwise, consider them as two different targets.
[0044] The velocity similarity between the trajectory to be fused and the target trajectory is calculated as the second threshold criterion. The formula for calculating the velocity similarity is:
[0045] in, Indicates speed similarity; , and These represent the velocity information in the x, y, and z directions of the i-th navigation trajectory collected by data acquisition unit a at time t; , and These represent the velocity information in the x, y, and z directions of the j-th navigation trajectory collected by data acquisition unit b at time t. (Setting...) threshold ,when If the trajectory is correctly identified, subsequent trajectory correlation judgments will be performed; otherwise, the two targets will be considered different and will not be merged.
[0046] When both distance similarity and velocity similarity criteria are met, i.e., when both distance similarity and velocity similarity are less than the corresponding preset thresholds, the trajectory to be fused is determined to match the target trajectory.
[0047] S4. If there is a target trajectory in the target trajectory set that matches the trajectory to be fused, perform weighted fusion on the trajectory to be fused and the matching target trajectory to generate the fused trajectory.
[0048] If multiple target trajectories in the target trajectory set match the trajectory to be fused, calculate the comprehensive similarity between the trajectory to be fused and each matching target trajectory, and then perform weighted fusion with the target trajectory with the highest comprehensive similarity to generate the fused trajectory.
[0049] The calculation process for comprehensive similarity is as follows: determine the Cauchy membership function of the fuzzy factors. The formula is expressed as:
[0050] in, Represents the Euclidean distance of the k-th fuzzy factor; This indicates the extent of the fuzzy factors in the fuzzy factor set; the fuzzy factor set includes the target's position and velocity.
[0051] The formula for calculating the extent of fuzzy factors is:
[0052] in, This represents the expansion degree of the k-th fuzzy factor in the fuzzy factor set; express The Euclidean distance of the k-th fuzzy factor at time n; n represents the number of trajectory points on the track.
[0053] The weighted sum of the Cauchy membership function values for each fuzzy factor is used as the comprehensive similarity. The expression for the comprehensive similarity is:
[0054] in, Indicates overall similarity; This represents the weight of the k-th fuzzy factor; This represents the dimensional similarity mapping function; Let represent the Euclidean distance of the k-th fuzzy factor at time t.
[0055] When calculating the weights for weighted fusion, the weight of the navigation trajectory collected by data acquisition unit a is: The weight of the navigation trajectory collected by data acquisition unit b is , The confidence level of data acquisition unit a. Let b be the confidence level of the data acquisition unit; the expression is:
[0056] in, , These represent the total amount of data acquired by data acquisition unit a and data acquisition unit b, respectively. , These represent the effective data volume of data acquisition unit a and data acquisition unit b, respectively. , These represent the data refresh rates of data acquisition unit a and data acquisition unit b, respectively.
[0057] S5. Remove the trajectories to be fused and the target trajectories that are currently involved in the weighted fusion from the navigation trajectory database, and repeat steps S2 to S4 based on the remaining navigation trajectories in the navigation trajectory database until all fusionable trajectories have completed weighted fusion to obtain the navigation trajectory fusion result.
[0058] In some embodiments, the method further includes: for scattered trajectory points not included in the navigation trajectory, performing the following fusion steps: calculating the association cost between each scattered trajectory point and each target trajectory in the target trajectory set; and fusion the scattered trajectory points into the target trajectory with the minimum association cost.
[0059] In some embodiments, the formula for calculating the association cost is:
[0060] in, Indicates the current trajectory to be merged. With the target trajectory The value of the relationship between them; Indicates the current trajectory to be merged. With the target trajectory The difference in distance between them; Indicates the current trajectory to be merged. With the target trajectory The angular difference between them; and These represent distance weight and angle weight, respectively.
[0061] In some embodiments, the method further includes: determining whether navigation trajectory information acquired by a single data acquisition unit exists; if the data type representation of the navigation trajectory information is complete and the sensor status feedback parameters are complete, then the navigation trajectory information is determined to exist; wherein, if the sensor system status identification information, data validity identification information, and coordinate system type identification information are all correct, then the data type representation is determined to be complete; if the total amount of missing data for the feedback detection distance parameter, feedback field of view parameter, covariance estimation error parameter, and accuracy factor parameter is not greater than a preset percentage, then the sensor status feedback parameters are determined to be complete. The preset percentage can be 25%.
[0062] This invention constructs a navigation trajectory database based on navigation trajectory information, enabling the processing of multi-source navigation trajectory data. It matches the trajectory to be fused with target trajectories in the target trajectory set using distance similarity, velocity similarity, and corresponding preset thresholds. This adapts to the numerical differences in trajectory caused by dynamic changes in the observation environment. It calculates the comprehensive similarity of multiple matching target trajectories to select the optimal trajectory for weighted fusion, improving the accuracy of trajectory matching. Removing the trajectory currently participating in the weighted fusion from the navigation trajectory database and repeatedly performing the fusion step on the remaining navigation trajectories reduces the amount of data computation in subsequent matching stages and improves the timeliness of navigation trajectory fusion processing.
[0063] The process of converting single-source data format into a standard format for fused data is as follows: In order to make the description of multi-source data information consistent, it is converted to the same reference coordinate system. For example, radar describes position information in polar coordinates, and AIS position information is provided by the GPS system, which uses a coordinate system based on the WGS-84 coordinate system.
[0064] To balance the computational complexity of coordinate transformation with the data requirements of subsequent modules, the multi-coordinate system is uniformly converted to a Cartesian coordinate system. The origin of the Cartesian coordinate system coincides with the origin of the ship's engine information data, and the x-axis is parallel to the parallel of latitude where the origin is located. Furthermore, since the conversion formula between the WGS-84 coordinate system and the Cartesian coordinate system is complex and cannot meet real-time requirements when dealing with large amounts of data, a coordinate system conversion formula suitable for computer-aided calculation is adopted as follows: The transformation formulas between polar coordinates and rectangular coordinates are as follows:
[0065]
[0066] The Gauss-Kruger projection method is used to transform the WGS-84 coordinate system to a rectangular coordinate system. The formula is as follows:
[0067] in:
[0068] This represents constant coefficients that are independent of the point location but depend only on the ellipsoidal parameters. Indicates the longitude of the central meridian. It represents the arc length of a meridian measured from the equator. This represents the radius of curvature of the zonal loop.
[0069]
[0070]
[0071] The methods for achieving time unification include: different sampling time intervals for the target from multiple data sources, different start times for target sampling from different data sources, and significant differences between the sampling time and measurement data when acquiring target information data, requiring the target information to be converted to the same time.
[0072] Considering the data refresh rate requirements and the data processing speed of subsequent subsystems, data calibration points are selected every second starting from zero, and the Lagrange interpolation algorithm is used to achieve time calibration.
[0073] Lagrange interpolation is relatively easy to implement in engineering; its core content lies in using basis functions. Make Able to fit This allows for the fitting of the interpolation value at any given time. The Lagrange interpolation method is shown in the following equation:
[0074] in, The expression is as follows:
[0075] To ensure computational speed, n=4 is chosen, meaning that the approximate value of the measurement within the interval is estimated using the measurement values two moments before and after the current moment, as shown in the following expression:
[0076] in: .
[0077] Obtaining more accurate drone trajectories by fusing trajectory information from multiple sources is beneficial for airport management; the dual-threshold design ensures the accuracy of trajectory fusion while improving the fusion speed; and the fusion of independent trajectory points is considered to ensure the stability of fusion and reduce trajectory omissions.
[0078] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0079] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.
Claims
1. A navigation trajectory fusion method based on multi-threshold decision and comprehensive similarity, characterized in that, include: S1. Collect navigation trajectory information through multiple data acquisition units, and construct a navigation trajectory database based on the navigation trajectory information; S2. In the navigation trajectory database, any navigation trajectory collected by one of the data acquisition units is taken as the trajectory to be fused, and all navigation trajectories collected by the other data acquisition unit are used to construct the target trajectory set. S3. Calculate the distance similarity and velocity similarity between the trajectory to be fused and each target trajectory in the target trajectory set. Based on the relationship between the distance similarity and velocity similarity and the corresponding preset threshold, determine whether the trajectory to be fused matches each target trajectory. S4. If there is a target trajectory in the target trajectory set that matches the trajectory to be fused, perform weighted fusion on the trajectory to be fused and the matching target trajectory to generate the fused trajectory. If multiple target trajectories in the target trajectory set match the trajectory to be fused, calculate the comprehensive similarity between the trajectory to be fused and each matching target trajectory, and then perform a weighted fusion of the target trajectory with the highest comprehensive similarity and the trajectory to be fused to generate the fused trajectory. S5. Remove the trajectories to be fused and the target trajectories that are currently involved in the weighted fusion from the navigation trajectory database, and repeat steps S2 to S4 based on the remaining navigation trajectories in the navigation trajectory database until all fusionable trajectories have completed weighted fusion to obtain the navigation trajectory fusion result.
2. The navigation trajectory fusion method based on multi-threshold decision and comprehensive similarity according to claim 1, characterized in that, This also includes performing the following fusion steps for scattered trajectory points that were not included in the navigation trajectory: Calculate the association cost between each scattered trajectory point and each target trajectory in the target trajectory set; The scattered trajectory points are merged into the target trajectory with the lowest association cost.
3. The navigation trajectory fusion method based on multi-threshold decision and comprehensive similarity according to claim 2, characterized in that, The formula for calculating the associated cost is as follows: in, Indicates the current trajectory to be merged. With the target trajectory The value of the relationship between them; Indicates the current trajectory to be merged. With the target trajectory The distance difference between them; Indicates the current trajectory to be merged. With the target trajectory The angular difference between them; and These represent distance weight and angle weight, respectively.
4. The navigation trajectory fusion method based on multi-threshold decision and comprehensive similarity according to claim 1, characterized in that, The data acquisition unit is an infrared sensor, photoelectric sensor, inertial sensor, or GPS positioning sensor.
5. The navigation trajectory fusion method based on multi-threshold decision and comprehensive similarity according to claim 1, characterized in that, When both the distance similarity and the velocity similarity are less than the corresponding preset thresholds, it is determined that the trajectory to be fused matches the target trajectory.
6. The navigation trajectory fusion method based on multi-threshold decision and comprehensive similarity according to claim 5, characterized in that, The formula for calculating the distance similarity is: in, Indicates distance similarity; , and These represent the x, y, and z coordinates of the i-th navigation trajectory collected by data acquisition unit a at time t; , and These represent the x, y, and z coordinates of the j-th navigation trajectory collected by data acquisition unit b at time t.
7. The navigation trajectory fusion method based on multi-threshold decision and comprehensive similarity according to claim 5, characterized in that, The formula for calculating the velocity similarity is: in, Indicates speed similarity; , and These represent the velocity information in the x, y, and z directions of the i-th navigation trajectory collected by data acquisition unit a at time t; , and These represent the velocity information in the x, y, and z directions of the j-th navigation trajectory collected by data acquisition unit b at time t.
8. The navigation trajectory fusion method based on multi-threshold decision and comprehensive similarity according to claim 1, characterized in that, The expression for the comprehensive similarity is: in, Indicates overall similarity; This represents the weight of the k-th fuzzy factor; This represents the dimensional similarity mapping function; Let represent the Euclidean distance of the k-th fuzzy factor at time t.
9. The navigation trajectory fusion method based on multi-threshold decision and comprehensive similarity according to claim 1, characterized in that, Also includes: Determine whether the navigation trajectory information collected by a single data acquisition unit exists; If the data type representation of the navigation trajectory information is complete and the sensor status feedback parameters are intact, then the navigation trajectory information is determined to exist. If the sensor system status identification information, data validity identification information, and coordinate system type identification information are all correct, then it is determined that there is no missing data type representation. If the total amount of missing data for the returned detection distance parameter, returned field of view parameter, covariance estimation error parameter, and accuracy factor parameter is not greater than a preset percentage, then the sensor status returned parameters are considered complete.