Modular positioning navigation time service system and positioning navigation time service method
The PNT system, with its modular design, enables plug-and-play and modular upgrades of sensors, solving the flexibility and compatibility issues of existing PNT systems and improving the system's robustness and sensor update efficiency.
Patent Information
- Application Number
- CN202511698913.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-19
- Publication Date
- 2026-02-03
AI Technical Summary
Existing PNT systems lack flexibility and compatibility at both the hardware architecture and software system levels, resulting in low efficiency in sensor updates and upgrades, high software maintenance costs, and difficulty in adapting to the multi-domain and multi-carrier requirements in complex environments.
It adopts a modular design, including PNT component modules and information fusion modules. Through standardized information extraction and modular architecture, it enables plug-and-play and seamless reconfiguration of sensors, and supports the free combination and dynamic adjustment of multiple sensors.
It enables plug-and-play and modular upgrades of sensors, improves system compatibility and robustness in complex environments, and provides continuous and reliable PNT services.
Smart Images
Figure CN121454883A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of positioning, navigation and timing technology, and in particular to a modular positioning, navigation and timing system and method. Background Technology
[0002] Positioning, Navigating & Timing (PNT) systems are commonly used to provide various users with key spatiotemporal parameters such as location, speed, and time, playing an indispensable role in many fields such as mobile measurement, intelligent transportation, and autonomous driving.
[0003] However, existing PNT systems have the following technical limitations: (1) At the hardware architecture level, the current PNT platform generally adopts a fixed integration scheme of multi-source sensors, which is characterized by a fixed configuration of sensor types, electrical interfaces and integration modes, and lacks the hardware reconfigurability to support dynamic switching of multiple domain environments and multiple carriers. (2) At the software system level, most mainstream PNT systems are designed based on specific environments or behavioral patterns and adopt a bundled integrated architecture. Customized development of fusion strategies and algorithms is required for the selected sensor combination. This rigid architecture means that when the complexity of PNT application scenarios increases, a single sensor configuration cannot meet the multiple performance requirements of continuity, availability and reliability in complex environments. (3) In terms of system configuration, existing PNT systems fail to achieve modular component design. The output formats of each sensor are different. When adjusting or expanding the sensors, it is necessary to rewrite the interface protocol and configure the hardware and software for each sensor. This results in low efficiency of sensor updates and upgrades, high software maintenance costs, and difficulty in adapting to the needs of flexible deployment and upgrades. Therefore, it is necessary to propose a modular PNT system and corresponding method that can achieve plug-and-play functionality, seamless reconfiguration, and open expansion. Summary of the Invention
[0004] This invention provides a modular positioning, navigation, and timing system and method to address the shortcomings of existing bundled integrated PNT technologies, such as poor compatibility, poor flexibility, poor adaptability, and high maintenance costs, thereby achieving continuous, usable, and reliable PNT output in complex cross-domain and cross-carrier environments.
[0005] In a first aspect, the present invention provides a modular positioning, navigation, and timing system, comprising: At least one PNT component module and one information fusion module are provided, wherein the PNT component module and the information fusion module are decoupled from each other and are connected and exchange information through their respective information transmission interfaces. The PNT component module includes a sensing element, an edge computing chip, and an information transmission interface. The sensing element senses and collects artificial or natural raw information for PNT calculation as a measurement output, which is then transmitted to the edge computing chip for data preprocessing. The edge computing chip deploys a standardized information extraction submodule, which includes a parameter setting unit, a parameter characteristic extraction unit, a constraint construction unit, and a statistical characteristic extraction unit. The standardized information extraction submodule converts the observation models of various types of PNT components into standardized information, which is then transmitted to the information fusion module through the information transmission interface. The information fusion module includes an intelligent computing platform and an information transmission interface. The intelligent computing platform includes an information monitoring submodule, a parameter adjustment submodule, a spatiotemporal unification submodule, and an intelligent fusion submodule. The information transmission interface is used to complete the information interaction between the information fusion module and the PNT component module, and to transmit information to the intelligent computing platform. According to a component-based positioning, navigation, and timing system provided by this invention, the standardized information extraction submodule extracts common features from the observation information of each PNT component, abstracts the standardized description of heterogeneous observation information, and outputs standardized information. The standardized information includes standardized parameters, standardized parameter characteristics, standardized constraint equations, and standardized measurement statistical characteristics.
[0006] The parameter setting unit sets the standardized state parameters in the standardized information based on the measurement information of the sensing element. The parameter characteristic extraction unit extracts the standardized characteristics from the standardized parameters according to the parameters set by the parameter setting unit. The constraint construction unit constructs standardized constraint equations based on the state parameters and establishes a mapping between the measurement output of the sensing element and the state parameters. The statistical characteristic extraction unit extracts the statistical characteristics of the measurement information output by the sensing element. Secondly, the present invention also provides a modular positioning, navigation, and timing method, comprising: Establish a state parameter list that includes public parameters and private parameters. The public parameters are shared parameters of all PNT components, and the private parameters are determined by the mapping relationship between the public parameters and the measurement output of the sensing element. The common parameters are constructed from the carrier position, carrier velocity, and carrier attitude. Construct a mapping function that represents the public parameters and the measurement output of the sensing element, determine the simplest form of the mapping function, and use the unknown variables of the non-public parameters in the simplest form of the mapping function as the private parameters.
[0007] A modular positioning, navigation, and timing method according to the present invention, applied to the parameter characteristic extraction unit, includes: Based on the parameters set by the parameter setting unit, the standardized characteristics in the standardized parameters are extracted, and a method for processing state parameters during information fusion is provided.
[0008] According to a modular positioning, navigation, and timing method provided by the present invention, the statistical characteristics of the measurement information of the PNT component are extracted, including noise type, noise processing method, and initial prior weighting table, including: A preset high-precision reference system is constructed and used as the reference truth. The observation error time series is obtained by using the reference truth and the output of the PNT component. Based on the observation error time series, the observation error distribution is fitted, and the fitted noise is divided into white noise and colored noise according to the Gaussian normal distribution law; If it is determined to be white noise, then estimate the measurement error of the white noise; If it is determined to be colored noise, the detected colored error is classified into noise categories, including outliers, systematic errors, and spatiotemporal correlation errors. Based on the noise classification results, a noise processing rule is constructed. If it is an outlier, it is marked as an error that needs to be removed and is not included in the calculation. If it is a systematic error, it is corrected and removed in advance during the calculation of the observation value. If it is a spatiotemporal correlation error, it is modeled as a state to be estimated and estimated during the estimation process. A priori initial weight ratio table is determined based on observation noise modeling.
[0009] A component-based positioning, navigation, and timing method according to the present invention, applied to the constraint construction unit, includes: The measurement information and the state parameter list are obtained, and based on the measurement form of the sensor element's measurement output, they are classified into single-epoch constraints and multi-epoch constraints. For single-epoch constraints, a single-epoch residual model is established based on the mapping relationship between the public parameters, the private parameters and the measurement information of the PNT component, and the statistical characteristics of the measurement information of the PNT component. The single-epoch residual model is then subjected to Taylor expansion, and the linearized absolute constraint mapping matrix is obtained after retaining the constant term and the first-order term. A single-epoch residual model with unified constraints is then constructed. For multi-epoch constraints, a multi-epoch residual model is established based on the mapping relationship between the public parameters, the private parameters, and the measurement information at adjacent time points, and the statistical characteristics of the measurement information of the PNT component. The multi-epoch residual model is then subjected to Taylor expansion, and the linearized relative constraint mapping matrix is obtained after retaining the constant term and the first-order term. A multi-epoch residual model with unified constraints is then constructed. Construct standardized constraint information, including constraint categories, measurement information, and constraint matrices.
[0010] According to the present invention, a component-based positioning, navigation, and timing method is applied to the information monitoring submodule for information interaction and intelligent updating between the PNT component module and the information fusion module, including: The information fusion module maintains the PNT component list and parameter list, monitors all interface information and decodes the information. When the information is successfully decoded, it is classified. If the component ID field is empty, it is determined that a new PNT component has been connected to the system. The information fusion module generates a list of PNT components, records the unique ID number in the list, and sends the component ID number, pose information and initialization information to the PNT component. The PNT component receives and saves the sent component ID number as the current component ID identifier, and attaches the current component ID identifier in subsequent interactions. If the parameter creation flag in the information type is 1, it indicates that there is a request to create a new parameter. The information fusion module generates a parameter list with no duplicate IDs and passes the new parameter ID to the PNT component. After receiving the new parameter ID, the PNT component maps the new parameter to the new parameter ID and sends the parameter creation information to the information fusion module. The information fusion module receives the parameter creation information, adds the corresponding new parameter ID to the parameter list, and passes the parameter addition request to the parameter adjustment submodule. If the observation identifier bit in the information type is 1, it indicates that it is a normal observation. The information fusion module receives the normal observation, extracts standardized information from it, and transmits the extracted information to the spatiotemporal unification submodule. When the intelligent fusion submodule completes the fusion, it sends a feedback identifier to the information monitoring submodule. The information monitoring submodule then sends back the fused post-verification information, including parameter ID, parameter estimate, and post-verification residual, to all PNT components maintained in the PNT component list.
[0011] A component-based positioning, navigation, and timing method according to the present invention, applied to the parameter adjustment submodule, includes: Maintaining a parameter list, specifically including a fusion parameter vector and a fusion covariance matrix, allows for cross-component parameter collaboration and real-time dynamic parameter updates. It also enables the determination of free combinations suitable for at least one PNT component, and the flexible adjustment of PNT components, specifically including: During the system initialization phase, the fusion parameter vector and the fusion covariance matrix are initialized as empty vector and matrix, respectively. When the PNT component needs to create new parameters, the information that needs to be transmitted includes an abstract structure of the new parameter information, an association matrix representing the relationship between the new parameter and existing parameters, and the initial variance information of the new parameter. When the information fusion module receives a new parameter request from the PNT component, it triggers the addition of parameters. The new parameter information vector is obtained by multiplying the correlation matrix with the fusion parameter vector. If the new parameter is not associated with any existing parameter, the correlation matrix is a zero matrix. According to the error propagation law, the parameter list vector is formed by the fusion parameter vector and the new parameter information vector. Determine the variance and covariance information corresponding to the parameter list vector, wherein the variance and covariance information includes parameter-unrelated form and existing parameter reconstruction form.
[0012] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the modular positioning, navigation, and timing method as described above.
[0013] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the componentized positioning, navigation, and timing method as described above.
[0014] The modular positioning, navigation, and timing system and method provided by this invention achieves plug-and-play sensor functionality by employing a standardized information extraction method, eliminating the dependence of traditional systems on specific sensor protocols. The modular architecture supports independent upgrades and maintenance of PNT components and the fusion center. The flexible fusion mechanism can adapt to the free combination of one or more PNT components and supports the dynamic adjustment of PNT components participating in the fusion. This not only ensures the compatibility of multi-sensor systems but also improves the system robustness in complex environments, enabling continuous, stable, and high-precision PNT services for various carriers. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in this 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 this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0016] Figure 1 This is a schematic diagram of the modular positioning, navigation, and timing system provided by the present invention; Figure 2 This is a schematic diagram of the statistical characteristic extraction method for PNT component measurement information provided by the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention.
[0017] Figure label: 100: Component-based positioning, navigation, and timing system; 110: PNT component module; 111: Standardized information extraction submodule; 111.1: Parameter setting unit; 111.2: Parameter characteristic extraction unit; 111.3: Constraint construction unit; 111.4: Statistical Feature Extraction Unit; 120: Information Fusion Module; 121: Information monitoring submodule; 122: Parameter adjustment submodule: 123: Spatiotemporal unification submodule; 124: Intelligent fusion submodule. Detailed Implementation
[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.
[0019] Figure 1 This is a schematic diagram of the component-based positioning, navigation, and timing system provided in an embodiment of the present invention, as shown below. Figure 1 As shown, it includes: The PNT system 100 includes several PNT component modules 110 and an information fusion module 120.
[0020] Understandably, the PNT component module 110 and the information fusion module 120 are connected and exchange information through a hardware interface. It should be noted that different sensors currently have different interface types. For example, radar uses a network port, image acquisition devices such as cameras use USB, and other types of interfaces such as serial ports can also be used to interconnect sensors and corresponding devices. Different types of interfaces correspond to different data protocols and output data in different formats.
[0021] The PNT component module 110 consists of sensing elements, an edge computing chip, and an information transmission interface. When the system is in use, the sensing elements perceive and collect artificial or natural raw information that can be used for PNT calculation as measurement output. Common sensing elements include, but are not limited to, cameras, GNSS antennas, and inertial measurement elements. This raw information is transmitted to the standardized information extraction submodule 111 in the edge computing chip to extract standardized information and interact with the information fusion module 120. The standardized information extraction submodule 111 includes a parameter setting unit 111.1, a parameter characteristic extraction unit 111.2, a constraint construction unit 111.3, and a statistical characteristic extraction unit 111.4.
[0022] The information fusion module 120 includes an information transmission device and an intelligent computing platform. The intelligent computing platform facilitates information interaction between the information fusion module and multiple PNT components, and transmits information to the intelligent computing platform. The intelligent computing platform deploys an information monitoring submodule 121, a parameter adjustment submodule 122, a spatiotemporal unification submodule 123, and an intelligent fusion submodule 124.
[0023] The information monitoring submodule 121 monitors information from the PNT component modules and classifies the information. The processed information is then passed to the parameter adjustment submodule 122. The parameter adjustment submodule 122 intelligently adjusts the state parameters involved in the fusion. Subsequently, the standardized information enters the spatiotemporal unification submodule 123 and undergoes spatiotemporal alignment. The spatiotemporally aligned information then enters the intelligent fusion submodule 124, which fuses the standardized information provided by multiple PNT components and outputs the fusion estimation results of the PNT parameters.
[0024] Each PNT component module in the system is functionally independent and decoupled from the information fusion module. The information fusion module does not presuppose any component as a necessary or core component, and the system can continue to run even when any PNT component is connected or offline.
[0025] In one embodiment, taking the PNT system provided in the embodiment as the execution subject to perform the positioning, navigation, and timing method as an example, the positioning, navigation, and timing method provided in this application embodiment is introduced. The method includes the following steps: S201, the sensing element in the PNT component module collects raw artificial or natural information that can be used for PNT calculation as a measurement output.
[0026] Specifically, the raw information may include natural features (such as point clouds and image information used by lidar and visual sensors) or artificial information (such as information transmitted by satellites or ultra-wideband positioning base stations).
[0027] S202, construct standardized information based on the relationship between the observation information obtained by the sensing element and the state parameters of the corresponding PNT sensor; Specifically, standardized information includes standardized parameters, standardized parameter characteristics, standardized constraint equations, and standardized measurement statistical characteristics.
[0028] Specifically, the steps for constructing standardized information include: S202.1: Parameter setting unit 111.1 determines the measurement output list of the corresponding sensor in the PNT component. and status information parameter list .
[0029] Among them, the measurement output list It includes direct quantities sensed and acquired by sensors, which are set according to the sensor type, such as pseudorange, phase, and Doppler information measured by GNSS components; image information obtained by camera components and feature pixel coordinates extracted from the images; point cloud information obtained by lidar components, etc.
[0030] Status information parameter list Including public parameters and private parameters .
[0031] Specifically, all PNT components have the same public parameter list, which takes the following form: (1) in, For the three-dimensional position of the carrier, For the three-dimensional velocity of the carrier, The three-dimensional posture of the carrier.
[0032] Specifically, different PNT components have different private parameters. The form of the private parameters depends on the sensor type corresponding to the PNT component, and the method for establishing them is as follows: Establish sensor observation information and common parameters Mapping relationship between them: (2) in, For the sensor's measurement output; A vector consisting of common parameters; This represents the mapping relationship between sensor observations and parameters. Simplifying equation (2) to its simplest form, all the unknowns that were not eliminated in equation (2) are taken as the private parameters of the PNT sensor. .
[0033] Table 1 shows the measurement outputs of some PNT components. and private parameters For example, in actual implementation, the PNT components that can be referred to include, but are not limited to, the following example components: Table 1
[0034] S202.2: After obtaining the parameter list, the parameter feature extraction unit 111.2 performs feature extraction on the private parameters to form a standardized feature description of the parameters.
[0035] For example, the standardized characteristics of parameters can be described in the form of the following structure: Struct { long id; double time[2]; int size; int n; double *xini, *xest, *xcor; MAP map; CORRECTION corr; bool flag_int; } In the above structure, "id" represents the parameter's sequence number, used by the information fusion module to identify the parameter; "time" represents the parameter's start and end times; "size" and "n" represent the parameter's size and degrees of freedom, respectively, used by the information fusion module to pre-allocate storage space for parameter estimation; "xini", "xest", and "xcor" represent the parameter's initial value, estimated value, and correction amount, respectively; "map" represents the parameter's time-varying characteristics, with MAP representing the parameter's statistical characteristics; "corr" represents the parameter's correction method. Generally, the correction method for conventional parameters is estimated value = initial value + correction amount, but there are special cases, such as attitude angles parameterized as quaternions, whose correction should follow the quaternion correction rules; "flag_int" indicates whether the parameter has integer characteristics. If it does, after parameter estimation is completed, integer parameters can be fixed, and then other parameters can be updated to improve the accuracy of system parameter estimation.
[0036] S202.3 Constraint Construction Unit 111.3 Constructs standardized constraints of a uniform form based on parameters and their characteristics.
[0037] Specifically, standardized constraint information includes constraint categories, measurement information, and constraint matrices.
[0038] Specifically, the method for constructing standardized constraint information is as follows, to build a unified measurement function model: (3) in, This represents the sensor's measurement output, which is the measurement information in the standardized constraint information; This represents the parameter list corresponding to the sensor, consisting of private parameters and public parameters; This indicates the mapping relationship between sensor measurement output and parameters.
[0039] Based on the number of epochs of the epoch constraints provided by the PNT component, the PNT component is classified into single-epoch components and multi-epoch components, which are the constraint categories in the standardized constraint information. According to the different categories, the measurement function model formula (4) is obtained: (4) in Let be a continuous function with continuous derivatives, satisfying . List of status information parameters , These are parameter information at different times.
[0040] Linearizing equation (5) yields the unified measurement model equation (5): (5) in, and This is the constraint matrix.
[0041] S202.4 The statistical characteristic extraction unit extracts the statistical characteristics of the sensor measurement information. Specifically, the method for extracting statistical characteristics is as follows: Figure 2 As shown.
[0042] S202.41: Construct a high-precision reference system as a reference truth value. S202.42: Obtain the time series of observation errors using the reference true value and the output of the PNT component.
[0043] S202.43 Based on the time series of observation errors, the distribution of observation errors is fitted, and the fitted noise is classified into white noise and colored noise according to the law of Gaussian normal distribution.
[0044] S202.43.1 If it is white noise, then estimate its measurement error; S202.443.2 If the noise is colored, the detected colored error shall be further classified into noise categories, including outliers, systematic errors and spatiotemporal correlation errors; S202.44 Based on further noise classification, noise processing rules are constructed: if it is an outlier, it is marked as an error that needs to be removed and is not included in the calculation; if it is a systematic error, it is corrected and removed in advance during the calculation of the observation value; if it is a spatiotemporally related error, it is modeled as a state to be estimated and estimated during the estimation process.
[0045] S202.45 Determine the prior initial weight ratio table based on observation noise modeling.
[0046] Specifically, select a unit-weighted observation from the observation category list, and set its measurement error as the unit-weighted error of the entire measurement system.
[0047] Based on the measurement errors of all obtained measurement observations and the unit weight error, determine the weight ratios between various types of observations and establish a priori initial weight ratio relationship table.
[0048] Specifically, within the prior observations, the measurement uncertainty is calculated for different observations based on the measurement quality characteristic function or empirical function of each type of observation, and the prior initial weight ratio table is jointly established to determine the prior stochastic model (6) for the current observation. Equation (6) is the extracted standardized measurement statistical characteristic: (6) in, For the prior stochastic model of the observations, The weight ratios between various observations provided by the prior initial weight ratio table. The prior uncertainty is calculated internally for various types of observations.
[0049] S203, the information listening submodule 121 of the information fusion module 120 listens to the PNT component information and classifies the information. The specific processing method is as follows: The S203.1 information fusion module maintains the PNT component list and parameter list, monitors all interface information and performs information decoding. When information is successfully decoded, it is categorized.
[0050] S203.2.1.1 If the component ID field is empty, it indicates that a new PNT component has been connected to the system. The information fusion module generates a unique ID number in the PNT component list, records it in the PNT component list, and sends the component ID number, pose information, and initialization information to it.
[0051] S203.2.1.2 The PNT component receives and saves the component ID number sent by the fusion module as the current component ID identifier, and attaches the component ID identifier in subsequent interactions.
[0052] S203.2.2.1 If the parameter creation flag is 1 in the information type, it indicates that there is a request to create a new parameter. The information fusion module generates a parameter list with no duplicate IDs and passes the new parameter ID to the PNT component.
[0053] S203.2.2.2 The PNT component receives the newly created parameter ID, associates the newly created parameter with the parameter ID, and sends the parameter creation information to the information fusion module. The information fusion module receives the creation request, adds the corresponding parameter ID from the parameter list, and passes the parameter addition request to the parameter adjustment submodule.
[0054] S203.2.3.1 If the parameter deletion flag is 1 in the information type, it indicates that there is a parameter deletion request. The information fusion module reads the parameter ID to be deleted and passes it to the parameter adjustment submodule. The parameter adjustment submodule deletes the parameter with the corresponding ID from the maintained parameter list.
[0055] S203.2.4 If the observation identifier bit is 1 in the information type, it indicates that it is a normal observation. The information fusion module receives the observation, extracts standardized information from it, and transmits the information to the spatiotemporal unification submodule.
[0056] S203.3 When the intelligent fusion submodule completes the fusion, it sends a feedback identifier to the information monitoring submodule. The information monitoring submodule then sends back the fused post-verification information, including parameter ID, parameter estimate, and post-verification residual, to all PNT components maintained in the PNT component list.
[0057] S204, the parameter adjustment submodule of the information fusion module maintains the parameter list and dynamically adds or deletes parameters after receiving standardized information from the PNT component module.
[0058] Specifically, the parameter tuning submodule also maintains the fusion parameter vector. and fusion covariance matrix Both are initialized as empty vectors and matrices.
[0059] Specifically, the parameter adjustment steps include: When the PNT component needs to create new parameters, the information that needs to be passed includes the information about creating the new parameters. Abstracted structures and matrices representing the relationships between new and existing parameters. And the initial variance information of the newly created parameters. When the information fusion module receives a new parameter request from the PNT component, it triggers the parameter addition process, as shown in equation (7): (7) in, and These represent newly created parameter vectors and existing parameter vectors, respectively. For a matrix representing the relationship between the two, if there is no correlation, It is an all-zero matrix. According to the law of error propagation, the newly formed parameter list is... The corresponding variance and covariance matrices should be: (8) in, For the variance and covariance information of the existing parameters, Initial variance information for newly created parameters. This provides variance and covariance information for the newly formed parameter list.
[0060] If the new parameter is not related to the existing parameter, then It is a zero matrix. The form is: (9) If the new parameters are completely reconstructed from the existing parameters, then The form is: (10) S205, the spatiotemporal unification unit of the information fusion module performs spatiotemporal unification on the acquired standardized information.
[0061] It is understandable that the time of the standardized information output by different PNT component modules may be different, but the elastic fusion step of the elastic fusion unit requires a unified and synchronized information time point. Therefore, it is necessary to unify the spatiotemporal output of different PNT component modules.
[0062] Specifically, the standardized function model and the standardized stochastic model are first normalized in time and in space, including: Align the normalized function model and the normalized stochastic model to the navigation coordinate system of the carrier, and align the normalized function model and the normalized stochastic model to the current time based on the local clock.
[0063] Specifically, the steps for spatial alignment are illustrated below: A mathematical model 1 for describing the vehicle's attitude is established in the ground-fixed coordinate system A. If it is necessary to transform the mathematical model in the ground-fixed coordinate system to the vehicle coordinate system B, the transformation matrix G from the ground-fixed coordinate system to the vehicle coordinate system can be determined. By combining the mathematical model 1 with the transformation matrix G, the mathematical model 1 can be easily and quickly transformed from the original coordinate system A to the new coordinate system B.
[0064] Furthermore, we can set the origin of coordinate system A at the centroid of the vehicle (such as a car, drone, etc.), and the three coordinate axes... The coordinate axes point directly forward, to the right, and down from the vehicle's center of mass, respectively. The origin of coordinate system B is located at the center of mass of sensor 1. Pointing directly in front, directly to the right, and directly below the center of mass of sensor 1, respectively, and considering the different installation positions and orientations, the center of mass of sensor 1 differs from the center of mass of the vehicle by a displacement vector. The coordinate axes of sensor 1 differ from the coordinate axes of the vehicle by a rotation matrix. ,Right now .
[0065] Therefore, for the function model based on the coordinate system of sensor 1, projecting it to the vehicle coordinate system requires multiplying by a transformation matrix. .
[0066] Correspondingly, in the original standardized function model The parameters involving the three-dimensional coordinate system need to be adjusted to... The transformations described above constitute the projection operation of the function model.
[0067] Optionally, the navigation coordinate system can be set as the plane where the carrier is located at the initial moment, or by selecting the centroid of the carrier at the initial moment as the origin. This application embodiment does not limit this.
[0068] Specifically, examples of methods for unifying time are as follows: This application can obtain a standardized function model through a standardized information extraction module. The data is then processed using a standardized stochastic model. Subsequently, it is transformed into second observation information by the standardization interaction module and transmitted to the information fusion module. The information fusion module obtains a coordinate system that is the sensor coordinate system. And the time is the Information in seconds.
[0069] Considering issues such as communication and computation, the actual time is the [number]th [time]. Seconds. The information fusion module needs to obtain the first... Vehicle coordinate system in seconds The following information, therefore, needs to be included. The information of the second is converted into the first. Second.
[0070] Optionally, the time conversion method can be processed according to commonly used methods such as constant time model, constant velocity model and B-spline curve interpolation, and the embodiments of this application do not limit this.
[0071] S206, the flexible fusion unit of the information fusion module performs optimal estimation of the standardized information after spatiotemporal unification and outputs navigation, positioning and timing information.
[0072] Specifically, "optimal estimation" involves weighted calculations based on the weights of each sensor, using statistical methods to process data from different sensors to obtain more accurate position, velocity, and time information. Optimal estimation can be used to fuse information from multiple sensors to reduce measurement errors and provide an estimate that is as close as possible to the true value.
[0073] Optionally, the optimal estimation method may include, but is not limited to, Kalman filtering, least squares estimation, factor graph optimization, etc., and the embodiments of this application do not limit this.
[0074] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3As shown, the electronic device may include a processor 310, a communications interface 320, a memory 330, and a communication bus 340. The processor 310, communications interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions from the memory 330 to execute a component-based positioning, navigation, and timing method. Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0075] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; 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 of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A modular positioning, navigation, and timing system, characterized in that, include: At least one PNT component module and one information fusion module are provided, wherein the PNT component module and the information fusion module are decoupled from each other and are connected and exchange information through their respective information transmission interfaces. The PNT component module includes a sensing element, an edge computing chip, and an information transmission interface. The sensing element senses and collects artificial or natural raw information for PNT calculation as a measurement output, which is then transmitted to the edge computing chip for data preprocessing. The edge computing chip deploys a standardized information extraction submodule, which includes a parameter setting unit, a parameter characteristic extraction unit, a constraint construction unit, and a statistical characteristic extraction unit. The standardized information extraction submodule converts the observation models of various types of PNT components into standardized information, which is then transmitted to the information fusion module through the information transmission interface. The information fusion module includes an intelligent computing platform and an information transmission interface. The intelligent computing platform includes an information monitoring submodule, a parameter adjustment submodule, a spatiotemporal unification submodule, and an intelligent fusion submodule. The information transmission interface is used to complete the information interaction between the information fusion module and the PNT component module, and to transmit information to the intelligent computing platform.
2. The modular positioning, navigation, and timing system according to claim 1, characterized in that, The standardized information extraction submodule extracts common features from the observation information of each PNT component, abstracts the standardized description of heterogeneous observation information, and outputs standardized information. The standardized information includes standardized parameters, standardized parameter characteristics, standardized constraint equations, and standardized measurement statistical characteristics. The parameter setting unit sets the standardized state parameters in the standardized information based on the measurement information of the sensing element. The parameter characteristic extraction unit extracts the standardized characteristics from the standardized parameters according to the parameters set by the parameter setting unit. The constraint construction unit constructs standardized constraint equations based on the state parameters and establishes a mapping between the measurement output of the sensing element and the state parameters. The statistical characteristic extraction unit extracts the statistical characteristics of the measurement information output by the sensing element.
3. A component-based positioning, navigation, and timing method, applied to the parameter setting unit of claim 1 or 2, characterized in that, include: Establish a state parameter list that includes public parameters and private parameters. The public parameters are shared parameters of all PNT components, and the private parameters are determined by the mapping relationship between the public parameters and the measurement output of the sensing element. The common parameters are constructed from the carrier position, carrier velocity, and carrier attitude. Construct a mapping function that represents the public parameters and the measurement output of the sensing element, determine the simplest form of the mapping function, and use the unknown variables of the non-public parameters in the simplest form of the mapping function as the private parameters.
4. The component-based positioning, navigation, and timing method according to claim 3, applied to the parameter characteristic extraction unit, characterized in that, include: Based on the parameters set by the parameter setting unit, the standardized characteristics in the standardized parameters are extracted, and a method for processing state parameters during information fusion is provided.
5. The component-based positioning, navigation, and timing method according to claim 4, characterized in that, Extract the statistical characteristics of the measurement information of the PNT component, including noise type, noise processing method, and initial prior weighting table, including: A preset high-precision reference system is constructed and used as the reference truth. The observation error time series is obtained by using the reference truth and the output of the PNT component. Based on the observation error time series, the observation error distribution is fitted, and the fitted noise is divided into white noise and colored noise according to the Gaussian normal distribution law; If it is determined to be white noise, then estimate the measurement error of the white noise; If it is determined to be colored noise, the detected colored error is classified into noise categories, including outliers, systematic errors, and spatiotemporal correlation errors. Based on the noise classification results, a noise processing rule is constructed. If it is an outlier, it is marked as an error that needs to be removed and is not included in the calculation. If it is a systematic error, it is corrected and removed in advance during the calculation of the observation value. If it is a spatiotemporal correlation error, it is modeled as a state to be estimated and estimated during the estimation process. A priori initial weight ratio table is determined based on observation noise modeling.
6. The component-based positioning, navigation, and timing method according to claim 3, applied to the constraint construction unit, characterized in that, include: The measurement information and the state parameter list are obtained, and based on the measurement form of the sensor element's measurement output, they are classified into single-epoch constraints and multi-epoch constraints. For single-epoch constraints, a single-epoch residual model is established based on the mapping relationship between the public parameters, the private parameters and the measurement information of the PNT component, and the statistical characteristics of the measurement information of the PNT component. The single-epoch residual model is then subjected to Taylor expansion, and the linearized absolute constraint mapping matrix is obtained after retaining the constant term and the first-order term. A single-epoch residual model with unified constraints is then constructed. For multi-epoch constraints, a multi-epoch residual model is established based on the mapping relationship between the public parameters, the private parameters, and the measurement information at adjacent time points, and the statistical characteristics of the measurement information of the PNT component. The multi-epoch residual model is then subjected to Taylor expansion, and the linearized relative constraint mapping matrix is obtained after retaining the constant term and the first-order term. A multi-epoch residual model with unified constraints is then constructed. Construct standardized constraint information, including constraint categories, measurement information, and constraint matrices.
7. The component-based positioning, navigation, and timing method according to claim 3, applied to the information monitoring submodule, is used for information interaction and intelligent updating between the PNT component module and the information fusion module, characterized in that... include: The information fusion module maintains the PNT component list and parameter list, monitors all interface information and decodes the information. When the information is successfully decoded, it is classified. If the component ID field is empty, it is determined that a new PNT component has been connected to the system. The information fusion module generates a list of PNT components, records the unique ID number in the list, and sends the component ID number, pose information and initialization information to the PNT component. The PNT component receives and saves the sent component ID number as the current component ID identifier, and attaches the current component ID identifier in subsequent interactions. If the parameter creation flag in the information type is 1, it indicates that there is a request to create a new parameter. The information fusion module generates a parameter list with no duplicate IDs and passes the new parameter ID to the PNT component. After receiving the new parameter ID, the PNT component maps the new parameter to the new parameter ID and sends the parameter creation information to the information fusion module. The information fusion module receives the parameter creation information, adds the corresponding new parameter ID to the parameter list, and passes the parameter addition request to the parameter adjustment submodule. If the observation identifier bit in the information type is 1, it indicates that it is a normal observation. The information fusion module receives the normal observation, extracts standardized information from it, and transmits the extracted information to the spatiotemporal unification submodule. When the intelligent fusion submodule completes the fusion, it sends a feedback identifier to the information monitoring submodule. The information monitoring submodule then sends back the fused post-verification information, including parameter ID, parameter estimate, and post-verification residual, to all PNT components maintained in the PNT component list.
8. The component-based positioning, navigation, and timing method according to claim 3, applied to the parameter adjustment submodule, characterized in that, include: Maintaining a parameter list, specifically including a fusion parameter vector and a fusion covariance matrix, allows for cross-component parameter collaboration and real-time dynamic parameter updates. It also enables the determination of free combinations suitable for at least one PNT component, and the flexible adjustment of PNT components, specifically including: During the system initialization phase, the fusion parameter vector and the fusion covariance matrix are initialized as empty vector and matrix, respectively. When the PNT component needs to create new parameters, the information that needs to be transmitted includes an abstract structure of the new parameter information, an association matrix representing the relationship between the new parameter and existing parameters, and the initial variance information of the new parameter. When the information fusion module receives a new parameter request from the PNT component, it triggers the addition of parameters. The new parameter information vector is obtained by multiplying the correlation matrix with the fusion parameter vector. If the new parameter is not associated with any existing parameter, the correlation matrix is a zero matrix. According to the error propagation law, the parameter list vector is formed by the fusion parameter vector and the new parameter information vector. Determine the variance and covariance information corresponding to the parameter list vector, wherein the variance and covariance information includes parameter-unrelated form and existing parameter reconstruction form.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the componentized positioning, navigation, and timing method as described in any one of claims 3 to 8.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the componentized positioning, navigation, and timing method as described in any one of claims 3 to 8.