Beidou anti-jamming navigation method based on multi-source data fusion and related equipment

Abstract

The embodiment of the application provides a Beidou anti-interference navigation method based on multi-source data fusion and related equipment, relates to the technical field of satellite navigation, and the method comprises the following steps: acquiring target multi-source data; extracting target interference characteristics according to the target multi-source data; based on a plurality of target cooperative positioning nodes in a web3.0 decentralized cooperative network, target anomaly determination is respectively performed according to the target interference characteristics to generate target cooperative results; target credibility operators are dynamically determined according to the target cooperative results; and target multi-source data fusion operations are performed according to the target credibility operators to output target anti-interference positioning results. The above method can adaptively adjust the credibility of multi-source data according to changes in the interference environment, can suppress the influence of abnormal data on the positioning result in a complex interference environment, can avoid the amplification effect of a single positioning node or a single data source anomaly on the positioning result, and thus the stability and reliability of the anti-interference positioning result are improved.

Description

Technical Field

[0001] The embodiments of this application relate to the field of satellite navigation technology, and in particular to a BeiDou anti-interference navigation method and related equipment based on multi-source data fusion. Background Technology

[0002] Satellite navigation mainly refers to the technology of determining the position and time information of objects on the ground or near the ground by receiving signals sent by navigation satellites.

[0003] In complex electromagnetic environments, positioning observation data from the BeiDou Navigation Satellite System is susceptible to interference, obstruction, and other factors, leading to a decrease in positioning accuracy. To improve the reliability of positioning results, existing technologies typically employ multi-source data fusion, combining BeiDou satellite observation data with inertial measurement data or other auxiliary positioning data to reduce the impact of anomalies from a single data source on the positioning results.

[0004] However, most existing multi-source data fusion methods are based on fixed models or preset weights. When the reliability of different data sources fluctuates with environmental changes, it is difficult to reflect the quality differences between the data sources in a timely manner, and abnormal data may still have an adverse impact on the fusion results. In addition, existing anti-interference localization methods mostly rely on a single localization node or a centralized processing architecture. Anomaly detection is mainly based on local signal characteristics and lacks a cross-node consistency verification mechanism, which makes anomaly detection susceptible to single-point failure. Summary of the Invention

[0005] According to embodiments of this application, a BeiDou anti-interference navigation method and related equipment based on multi-source data fusion are provided. By acquiring multi-source target data and extracting target interference features, multiple cooperative positioning nodes perform anomaly detection in a decentralized cooperative network to form a target cooperative result. Based on the target cooperative result, a target credibility operator is dynamically determined, which enables the credibility of multi-source data to be adaptively adjusted according to changes in the interference environment. By performing multi-source data fusion operation based on the target credibility operator, the influence of abnormal data on the positioning result can be suppressed in complex interference environments, avoiding the amplification effect of anomalies from a single positioning node or a single data source on the positioning result, thereby improving the stability and reliability of the anti-interference positioning result.

[0006] In the first aspect of this application, a BeiDou anti-interference navigation method based on multi-source data fusion is proposed, comprising:

[0007] Acquire target multi-source data;

[0008] Extract target interference features based on multi-source target data;

[0009] Based on multiple target collaborative localization nodes in a web3.0 decentralized collaborative network, target anomaly determination is performed according to target interference characteristics to generate target collaborative results;

[0010] Based on the results of the target collaboration, the target credibility operator is dynamically determined;

[0011] Based on the target credibility operator, perform target multi-source data fusion operation to output the target anti-interference positioning result.

[0012] In some feasible implementations, the multiple target collaborative positioning nodes in the aforementioned web3.0 decentralized collaborative network perform target anomaly determination based on target interference characteristics to generate target collaborative results, including:

[0013] Based on multiple target collaborative localization nodes, the corresponding target anomaly judgment results are determined respectively;

[0014] Perform a consistency assessment operation on multiple target anomaly determination results to determine the target consistency assessment result;

[0015] Based on the objective consistency assessment results, if the objective convergence is greater than or equal to a preset convergence threshold, objective collaboration results are generated.

[0016] In some feasible implementations, the aforementioned synergistic results include:

[0017] Target trusted status information, target anomaly level information, and / or target fusion permission information.

[0018] In some feasible implementations, the above-mentioned dynamic determination of the target credibility operator based on the target collaboration result includes:

[0019] Based on the target collaboration results, update the first target credibility parameter set and / or the second target credibility parameter set;

[0020] The target credibility operator is dynamically determined based on the updated first target credibility parameter set and / or the second target credibility parameter set;

[0021] The first target confidence parameter set includes: the first confidence parameters corresponding to multiple target collaborative positioning nodes;

[0022] The second set of credibility parameters includes: second credibility parameters corresponding to multiple sources of data for various objectives.

[0023] In some feasible implementations, updating the first target credibility parameter set and / or the second target credibility parameter set based on the target collaboration results includes:

[0024] If the first abnormal frequency corresponding to the target cooperative positioning node is greater than or equal to the first preset frequency threshold within a number of consecutive time windows greater than or equal to the first preset number, then the first confidence parameter corresponding to the target cooperative positioning node is reduced.

[0025] And / or,

[0026] If, within a number of consecutive time windows greater than or equal to the second preset number, the second anomaly frequency corresponding to the target multi-source data is greater than or equal to the second preset frequency threshold, then the second confidence parameter corresponding to the target multi-source data is reduced.

[0027] In some feasible implementations, the above-mentioned target multi-source data fusion operation based on the target confidence operator to output the target anti-interference localization result includes:

[0028] Construct the target cost function based on the target credibility operator;

[0029] Based on the target cost function, perform the target multi-source data fusion operation.

[0030] In some feasible implementations, the above-mentioned construction of the target cost function based on the target credibility operator includes:

[0031] Construct the target cost function based on the following formula:

[0032] (2)

[0033] in, Used to represent the objective cost function; Used to represent prior residuals; Used to represent the prior residual information matrix; Used to represent inertial navigation residuals; Used to represent the inertial navigation information matrix; Used to represent robust kernel functions; Used to represent the residual of BeiDou observations; Used to represent the BeiDou information matrix; Used to represent Web3 collaborative residuals; Used to represent the information matrix of a collaborative network; Used to represent the initial state estimation vector; Used to represent The state estimation vector at time 1; Used to represent The state estimation vector corresponding to time step 1.

[0034] A second aspect of this application proposes a BeiDou anti-interference navigation device based on multi-source data fusion, comprising:

[0035] The acquisition unit is used to acquire target multi-source data;

[0036] The extraction unit is used to extract target interference features based on multi-source target data;

[0037] The generation unit is used to perform target anomaly determination based on the target interference characteristics of multiple target collaborative positioning nodes in the Web3.0 decentralized collaborative network, so as to generate target collaborative results.

[0038] The determination unit is used to dynamically determine the target credibility operator based on the target collaboration results;

[0039] The output unit is used to perform target multi-source data fusion operation based on the target confidence operator to output the target anti-interference positioning result.

[0040] In a third aspect of this application, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0041] In a fourth aspect of this application, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method according to the first aspect of this application.

[0042] This application provides a BeiDou anti-interference navigation method and related equipment based on multi-source data fusion. The method includes: acquiring target multi-source data; extracting target interference features based on the target multi-source data; performing target anomaly determination based on the target interference features using multiple target collaborative positioning nodes in a Web3.0 decentralized collaborative network to generate target collaborative results; dynamically determining a target credibility operator based on the target collaborative results; and performing target multi-source data fusion operations based on the target credibility operator to output target anti-interference positioning results. In this way, by acquiring target multi-source data and extracting target interference features, utilizing multiple collaborative positioning nodes to perform anomaly determination in a decentralized collaborative network to form target collaborative results, and dynamically determining the target credibility operator based on the target collaborative results, the credibility of multi-source data can be adaptively adjusted according to changes in the interference environment. Performing multi-source data fusion operations based on the target credibility operator can suppress the impact of abnormal data on positioning results in complex interference environments, avoiding the amplification effect of anomalies from a single positioning node or a single data source on the positioning results, thereby improving the stability and reliability of anti-interference positioning results.

[0043] It should be understood that the description in the Summary Section is not intended to limit the key or essential features of the embodiments of this application, nor is it intended to restrict the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0044] The above and other features, advantages, and aspects of the embodiments of this application will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0045] Figure 1 A flowchart illustrating a BeiDou anti-interference navigation method based on multi-source data fusion, provided for an embodiment of this application;

[0046] Figure 2 A structural schematic diagram of a BeiDou anti-interference navigation device based on multi-source data fusion provided in this application embodiment;

[0047] Figure 3 This is a structural schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0049] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0050] In a first aspect of this application, a BeiDou anti-interference navigation method based on multi-source data fusion is proposed. Figure 1 A flowchart illustrating a BeiDou anti-interference navigation method 100 based on multi-source data fusion, as provided in this application embodiment, is shown below. Figure 1 As shown, method 100 includes:

[0051] Step S1: Obtain target multi-source data.

[0052] For example, the aforementioned multi-source target data may include: target BeiDou satellite observation data, target inertial measurement data corresponding to the aforementioned target BeiDou satellite observation data, and / or, target cooperative positioning node data, etc.

[0053] The aforementioned target inertial measurement data may include: time-dimensional inertial measurement data corresponding to the target's BeiDou satellite observation data, and / or, space-dimensional inertial measurement data.

[0054] Step S2: Extract target interference features based on multi-source target data.

[0055] For example, before performing step S2 above, corresponding preprocessing operations can be performed on the target multi-source data to improve the quality of the target interference features.

[0056] The aforementioned preprocessing operations may include: performing multipath error suppression on the target BeiDou satellite observation data, and performing zero-bias stability compensation on the target inertial measurement data.

[0057] For example, the aforementioned target interference features may include: signal domain interference features, consistency domain interference features, and / or, multi-source fusion residual interference features, etc.

[0058] The aforementioned signal domain interference characteristics may include: signal-to-noise ratio anomaly characteristics, and / or, energy distribution anomaly characteristics, etc., for use in noise anomaly determination.

[0059] The aforementioned consistency domain interference features may include: spatiotemporal residual features, and / or ephemeris verification features, etc., for use in deceptive anomaly detection.

[0060] Among them, the aforementioned multi-source fusion residual interference features may include: Mahalanobis distance residual features based on factor graph optimization output, etc., for use in integrity state anomaly determination.

[0061] Step S3: Based on multiple target collaborative positioning nodes in the web3.0 decentralized collaborative network, target anomaly determination is performed according to the target interference characteristics to generate target collaborative results.

[0062] For example, the aforementioned multiple target cooperative positioning nodes can perform target anomaly determination based on the target interference characteristics combined with the electromagnetic environment data obtained by sensing, and / or historical evidence data, to generate target cooperative results.

[0063] Among them, the aforementioned target cooperative positioning nodes can correspond to: autonomous vehicle nodes, edge base station nodes, and / or, intelligent roadside unit nodes, etc.

[0064] For example, multiple target cooperative positioning nodes can perform spatial consistency anomaly determination, logical authenticity anomaly determination, etc., based on the aforementioned target interference characteristics combined with electromagnetic environment data obtained through sensing, and / or historical evidence data.

[0065] The aforementioned spatial consistency anomaly determination is used to judge the consistency of the distortion of the target interference feature signal.

[0066] The aforementioned logic authenticity anomaly determination is used to judge the legality of the ephemeris hash corresponding to the aforementioned target interference features verified by the target collaborative positioning node through the Web3.0 protocol.

[0067] In some feasible implementations, step S3 above; based on multiple target collaborative positioning nodes in the web3.0 decentralized collaborative network, target anomaly determination is performed according to target interference characteristics to generate target collaborative results, including:

[0068] Step S31: Based on multiple target collaborative positioning nodes, determine the corresponding target anomaly judgment results respectively.

[0069] For example, based on multiple target collaborative positioning nodes, the corresponding spatial consistency anomaly determination results and / or logical authenticity anomaly determination results can be determined respectively.

[0070] Step S32: Perform a consistency assessment operation on the anomaly determination results of multiple targets to determine the target consistency assessment results.

[0071] For example, a consistency assessment operation can be performed on the spatial consistency anomaly determination results and / or logical authenticity anomaly determination results corresponding to multiple target collaborative positioning nodes to determine the above target consistency assessment results.

[0072] Specifically, the spatial consistency anomaly determination results and / or logical authenticity anomaly determination results can be quantified into a corresponding anomaly confidence score vector.

[0073] Specifically, the Euclidean distance between each pair of anomaly confidence score vectors can be calculated to determine the target convergence of the target consistency assessment results.

[0074] Specifically, the aforementioned objective convergence can be determined based on the following formula:

[0075] (1)

[0076] in, Used to represent the convergence of the objective. Used to represent the actual Euclidean distance between any two pairs of anomaly confidence score vectors. Used to indicate the maximum preset Euclidean distance.

[0077] It is understandable that the aforementioned objective convergence is negatively correlated with the aforementioned Euclidean distance; that is, the smaller the Euclidean distance, the higher the objective convergence.

[0078] Step S33: If the target convergence is determined to be greater than or equal to the preset convergence threshold based on the target consistency evaluation results, generate the target collaboration result.

[0079] For example, if the convergence of the objective is determined to be greater than or equal to 70% based on the above objective consistency assessment results, then objective coordination results can be generated.

[0080] It should be noted that the aforementioned preset convergence threshold and the determination accuracy requirement of the target confidence operator, and / or the output accuracy requirement of the target anti-interference positioning result are positively correlated. That is, the higher the determination accuracy requirement of the target confidence operator, and / or the output accuracy requirement of the target anti-interference positioning result, the higher the aforementioned preset convergence threshold.

[0081] It should be noted that the above steps S32 to S33 can be automatically triggered and executed through smart contracts preset in the Web3.0 network.

[0082] By performing distributed consensus verification on the target collaboration results through steps S32 to S33, accurate data support can be provided for the dynamic determination of the target credibility operator in step S4, thereby enhancing the environmental adaptability and robust survivability of the BeiDou navigation system in the face of malicious deception and complex interference, and thus improving the output accuracy of the target anti-interference positioning results.

[0083] It should be noted that the above-mentioned target collaboration results may include: target trust status information, target anomaly level information, and / or target fusion permission information.

[0084] For example, the aforementioned target trust status information may include: trust status level information, such as: fully trustworthy, partially trustworthy, or untrustworthy.

[0085] For example, the target anomaly level information described above is used to quantify the severity of target interference. For instance, the target anomaly level information could correspond to a Level 1 minor multipath anomaly, or a Level 5 fatal deception interference anomaly, etc.

[0086] For example, the aforementioned target fusion permission information can be used to determine whether target BeiDou satellite observation data, target inertial measurement data corresponding to the aforementioned target BeiDou satellite observation data, and / or target cooperative positioning node data, etc., participate in fusion. For example, the aforementioned target fusion permission information can correspond to: allowing target BeiDou satellite observation data to participate in fusion, restricting the weight of target BeiDou satellite observation data participating in fusion, or forcibly removing target BeiDou satellite observation data from the fusion process, etc.

[0087] By outputting the target collaboration results from multiple dimensions, we can further provide accurate data support for the dynamic determination of the target credibility operator in step S4, thereby enhancing the BeiDou navigation system's environmental adaptability and robust survivability in the face of malicious deception and complex interference, and thus improving the output accuracy of the target anti-interference positioning results.

[0088] Step S4: Dynamically determine the target credibility operator based on the target collaboration results.

[0089] For example, the target credibility operator can be dynamically determined based on the above target collaboration results to achieve differentiated weighting of data sources of different quality, thereby improving the execution accuracy of the target multi-source data fusion operation.

[0090] In some feasible implementations, step S4 above; dynamically determining the target credibility operator based on the target collaboration result, includes:

[0091] Step S41: Update the first target credibility parameter set and / or the second target credibility parameter set based on the target collaboration results.

[0092] It should be noted that the aforementioned first target confidence parameter set may include: first confidence parameters corresponding to multiple target collaborative localization nodes. The aforementioned second target confidence parameter set may include: second confidence parameters corresponding to multi-source data from multiple targets.

[0093] For example, if, based on the aforementioned target collaboration results, it is determined that the target trust status information corresponding to the target collaborative positioning node corresponds to complete trust, the first trust parameter corresponding to the target collaborative positioning node can be increased to update the aforementioned first target trust parameter set. If, based on the aforementioned target collaboration results, it is determined that the target trust status information corresponding to the target collaborative positioning node corresponds to untrust, the first trust parameter corresponding to the target collaborative positioning node can be decreased to update the aforementioned first target trust parameter set.

[0094] For example, if, based on the aforementioned target collaboration results, it is determined that the target trust status information corresponding to the target multi-source data corresponds to complete trustworthiness, the second trustworthiness parameter corresponding to the target multi-source data can be increased to update the aforementioned second target trustworthiness parameter set. If, based on the aforementioned target collaboration results, it is determined that the target trust status information corresponding to the target multi-source data corresponds to untrustworthiness, the second trustworthiness parameter corresponding to the target multi-source data can be decreased to update the aforementioned second target trustworthiness parameter set.

[0095] For example, if, based on the aforementioned target collaboration results, it is determined that the target anomaly level information corresponding to the target collaborative positioning node corresponds to a Level 1 minor multipath anomaly, the first confidence parameter corresponding to the target collaborative positioning node can be increased to update the aforementioned first target confidence parameter set. If, based on the aforementioned target collaboration results, it is determined that the target anomaly level information corresponding to the target collaborative positioning node corresponds to a Level 5 fatal deception interference anomaly, the first confidence parameter corresponding to the target collaborative positioning node can be decreased to update the aforementioned first target confidence parameter set.

[0096] For example, if, based on the aforementioned target collaboration results, the target anomaly level information corresponding to the target multi-source data is determined to correspond to a Level 1 minor multipath anomaly, the second confidence parameter corresponding to the target multi-source data can be increased to update the aforementioned second target confidence parameter set. If, based on the aforementioned target collaboration results, the target anomaly level information corresponding to the target multi-source data is determined to be a Level 5 fatal deception interference anomaly, the second confidence parameter corresponding to the target multi-source data can be decreased to update the aforementioned second target confidence parameter set.

[0097] For example, if, based on the aforementioned target collaboration results, it is determined that the target fusion permission information corresponding to the target multi-source data corresponds to permission to participate in fusion, the second confidence parameter corresponding to the target multi-source data can be increased to update the aforementioned second target confidence parameter set. If, based on the aforementioned target collaboration results, it is determined that the target multi-source data corresponds to forced cut-off fusion, the second confidence parameter corresponding to the target multi-source data can be decreased to update the aforementioned second target confidence parameter set.

[0098] In some feasible implementations, step S41 above; updating the first target credibility parameter set and / or the second target credibility parameter set according to the target collaboration result, includes:

[0099] Step S411: If the first abnormal frequency corresponding to the target cooperative positioning node is greater than or equal to the first preset frequency threshold within a number of consecutive time windows greater than or equal to the first preset number, then reduce the first confidence parameter corresponding to the target cooperative positioning node.

[0100] Step S412: For a target cooperative positioning node whose first confidence parameter has been reduced, if the first abnormal frequency corresponding to the target cooperative positioning node is less than the first preset frequency threshold within a third preset number of consecutive time windows, then the first confidence parameter corresponding to the target cooperative positioning node is increased.

[0101] And / or,

[0102] Step S413: If the second anomaly frequency corresponding to the target multi-source data is greater than or equal to the second preset frequency threshold within a number of consecutive time windows greater than or equal to the second preset number, then reduce the second confidence parameter corresponding to the target multi-source data.

[0103] Step S414: For the target multi-source data whose second confidence parameter has been reduced, if the second anomaly frequency corresponding to the target multi-source data is less than the second preset frequency threshold within a number of consecutive time windows greater than or equal to the fourth preset number, then the second confidence parameter corresponding to the target multi-source data is increased.

[0104] It should be noted that the third preset quantity is not less than the first preset quantity, and the fourth preset quantity is not less than the second preset quantity, so that the recovery trigger condition of the credibility parameter is more stringent than the degradation trigger condition, and the credibility parameter is prevented from being restored prematurely before the abnormal state has been fully eliminated.

[0105] The first preset frequency threshold and the second preset frequency threshold can be set differently according to the actual needs of the scenario.

[0106] By using a frequency determination mechanism based on continuous time window statistics, refined dynamic updates of the reliability parameters of collaborative positioning nodes and multi-source data can be achieved, thereby enhancing the robustness of abnormal signal identification, isolating malicious collaborative positioning nodes or unstable multi-source data, and improving the reliability of target reliability operators in complex interference scenarios. This further improves the execution accuracy of target multi-source data fusion operations and achieves high-precision output of anti-interference positioning results for targets.

[0107] Step S42: Dynamically determine the target credibility operator based on the updated first target credibility parameter set and / or the second target credibility parameter set.

[0108] For example, the updated first target credibility parameter set and / or the second target credibility parameter set can be mapped to dynamically determine the target credibility operator.

[0109] It should be noted that the aforementioned target confidence operator can be used to adjust the contribution weight matrix corresponding to each observation in the aforementioned multi-source target data. The contribution weight matrix characterizes the degree of influence of each observation on state estimation during the multi-source target data fusion operation.

[0110] By dynamically updating the first and second target confidence parameter sets and dynamically determining the target confidence operator accordingly, the robustness of identifying abnormal signals is significantly enhanced, and malicious collaborative positioning nodes or unstable multi-source data are effectively isolated. This improves the reliability of the target confidence operator determination in complex interference scenarios, thereby increasing the execution accuracy of target multi-source data fusion operations and ultimately achieving high-precision output of target anti-interference positioning results.

[0111] Step S5: Based on the target confidence operator, perform target multi-source data fusion operation to output the target anti-interference positioning result.

[0112] For example, the target confidence operator described above can be used to perform a confidence weighting operation on the target multi-source data participating in the fusion to construct a target cost function; the target cost function can be solved to achieve joint optimization of the target multi-source data; and the target anti-interference positioning result can be output based on the joint optimization result of the target multi-source data.

[0113] In some feasible implementations, step S5 above; performing a multi-source data fusion operation on the target based on the target confidence operator to output the target anti-interference localization result includes:

[0114] Step S51: Construct the target cost function based on the target credibility operator.

[0115] For example, a mapping calculation operation can be performed based on the above target credibility operator to determine the inertial navigation information matrix, the BeiDou information matrix, and / or the cooperative network information matrix, so as to construct the above target cost function based on the prior residuals and the corresponding prior residual information matrix, the inertial navigation residuals and the corresponding inertial navigation information matrix, the BeiDou observation residuals and the corresponding BeiDou information matrix, and / or the Web3 cooperative residuals and the corresponding cooperative network information matrix.

[0116] It should be noted that the aforementioned target credibility operator can dynamically adjust the contribution weight of different data sources in the aforementioned target multi-source data fusion operation by weighting and scaling the information matrix corresponding to each residual term, so as to suppress the influence of abnormal data or interfered data with low credibility on the fusion result, thereby improving the stability and reliability of the aforementioned target anti-interference positioning result.

[0117] In some feasible implementations, step S51 above; constructing the target cost function based on the target credibility operator, includes:

[0118] Step S511; Construct the objective cost function according to the following formula:

[0119] (2)

[0120] in, Used to represent the objective cost function; Used to represent prior residuals; Used to represent the prior residual information matrix; Used to represent inertial navigation residuals; Used to represent the inertial navigation information matrix; Used to represent robust kernel functions; Used to represent the residual of BeiDou observations; Used to represent the BeiDou information matrix; Used to represent Web3 collaborative residuals; Used to represent the information matrix of a collaborative network; Used to represent the initial state estimation vector; Used to represent The state estimation vector at time 1; Used to represent The state estimation vector corresponding to time step 1.

[0121] It should be noted that by minimizing the objective cost function It can calculate the target's optimal state variables at the current moment, such as position, velocity, and attitude.

[0122] Among them, the aforementioned a priori residuals It can be used to determine known estimates of initial or historical states. Prior residual information matrix. Used to represent the confidence level of prior data. Prior constraint terms. This is used to ensure that the current positioning calculation is continuous and does not deviate from a reasonable range of historical trajectories.

[0123] Among them, the aforementioned inertial navigation residual This is used to represent adjacent times calculated based on the inertial measurement unit, i.e. Time and Motion increment constraints between time points. The aforementioned inertial navigation information matrix. The confidence level of inertial navigation data is dynamically determined by the target confidence operator. If the interference is severe, the inertial navigation information matrix is ​​increased. The above inertial navigation constraint terms It is used to maintain the continuity of positioning results by relying on short-term high-precision calculations of inertial navigation when BeiDou signals are interfered with.

[0124] Among them, BeiDou observation residual Used to represent the deviation between actual satellite observations and predictions based on the satellite's condition. Robust kernel function. Used to suppress anomalous observations, such as multipath interference signals, and their negative impact on overall optimization. BeiDou Information Matrix The confidence level of BeiDou observation data is dynamically determined by the target confidence operator. If the BeiDou signal is severely interfered with, the BeiDou information matrix is ​​reduced. This is to reduce the impact of this observation term on the final solution. The above-mentioned BeiDou observation constraints... It is used to provide an absolute position reference for the system and to correct the cumulative drift error of the inertial navigation system.

[0125] Among them, Web3 collaborative residuals Used to represent the deviation between the local state and the consensus results of other cooperating nodes in the decentralized network. Cooperative Network Information Matrix This can be determined based on the consistency evaluation results of multiple target collaborative positioning nodes. The aforementioned Web3.0 collaborative constraints... By introducing data from external nodes to correct local errors, positioning accuracy can still be maintained even if all local sensors are interfered with, as long as the cooperative network is reliable.

[0126] By constructing a target cost function that includes prior, motion, observation, and collaborative multidimensional factors, weighted fusion and constraint modeling of multi-source heterogeneous data can be achieved. By utilizing the synergistic effect of robust kernel functions and collaborative residual terms, nonlinear suppression of abnormal observation residuals can be achieved under complex interference environments. Furthermore, under the premise of satisfying physical and dynamic constraints, the collective consensus of decentralized networks can be used to correct single-machine positioning biases, thereby improving the stability and reliability of anti-interference positioning results in adversarial environments.

[0127] Step S52: Perform the target multi-source data fusion operation according to the target cost function.

[0128] For example, the target cost function in the above formula (2) can be solved to perform the target multi-source data fusion operation in order to achieve joint optimization of the target multi-source data.

[0129] Specifically, the target cost function in the above formula (2) can be solved based on a preset iterative optimization algorithm, such as the Gauss-Newton algorithm or the LM algorithm, so as to achieve joint optimization calculation of the above target multi-source data by minimizing the total residual, and obtain the target's optimal state quantity at the current time, so as to output the target anti-interference positioning result.

[0130] By constructing a target cost function based on the target credibility operator and performing target multi-source data fusion operation based on the target cost function, it is possible to perform differentiated weighting and constraint modeling on target multi-source data of different sources and qualities. This can suppress the negative impact of abnormal data or interfered data with low credibility on the fusion results, improve the stability and robustness of the target multi-source data fusion process, and thus improve the accuracy and reliability of target anti-interference positioning results in complex interference environments.

[0131] Based on this, the BeiDou anti-interference navigation method based on multi-source data fusion provided in this application includes: acquiring target multi-source data; extracting target interference features based on the target multi-source data; performing target anomaly determination based on the target interference features using multiple target cooperative positioning nodes in a Web3.0 decentralized cooperative network to generate target cooperative results; dynamically determining the target credibility operator based on the target cooperative results; and performing target multi-source data fusion operation based on the target credibility operator to output target anti-interference positioning results. The above method, by acquiring target multi-source data and extracting target interference features, utilizing multiple cooperative positioning nodes to perform anomaly determination in a decentralized cooperative network to form target cooperative results, and dynamically determining the target credibility operator based on the target cooperative results, allows the credibility of multi-source data to adaptively adjust with changes in the interference environment. Performing multi-source data fusion operation based on the target credibility operator can suppress the impact of abnormal data on positioning results in complex interference environments, avoiding the amplification effect of anomalies from a single positioning node or a single data source on the positioning results, thereby improving the stability and reliability of anti-interference positioning results.

[0132] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0133] The above is an introduction to the method embodiments. The following describes the solution described in this application through device embodiments.

[0134] A second aspect of this application proposes a BeiDou anti-interference navigation device based on multi-source data fusion. Figure 2 This is a structural schematic diagram of a BeiDou anti-interference navigation device 200 based on multi-source data fusion, provided as an embodiment of this application. (See attached diagram.) Figure 2 The BeiDou anti-interference navigation device 200 based on multi-source data fusion shown includes: an acquisition unit 210, an extraction unit 220, a generation unit 230, a determination unit 240, and an output unit 250.

[0135] Acquisition unit 210 is used to acquire target multi-source data;

[0136] Extraction unit 220 is used to extract target interference features based on target multi-source data;

[0137] The generation unit 230 is used to perform target anomaly determination according to the target interference characteristics of multiple target cooperative positioning nodes in the web3.0 decentralized cooperative network, so as to generate target cooperative results;

[0138] The determination unit 240 is used to dynamically determine the target credibility operator based on the target collaboration results;

[0139] Output unit 250 is used to perform target multi-source data fusion operation according to the target confidence operator to output the target anti-interference positioning result.

[0140] Figure 3 This is a schematic diagram of the structure of an electronic device 300 provided in an embodiment of this application. Figure 3 As shown, the electronic device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage section 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the terminal device or server. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0141] The following components are connected to I / O interface 305: an input section 306 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to I / O interface 305 as needed. A removable medium 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 310 as needed so that computer programs read from it can be installed into storage section 308 as needed.

[0142] Specifically, according to embodiments of this application, the above method flow steps can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a machine-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the functions defined in the system of this application.

[0143] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0144] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0145] The units or modules described in the embodiments of this application can be implemented in software or hardware. The described units or modules can also be located in a processor. The names of these units or modules do not, in certain circumstances, constitute a limitation on the unit or module itself.

[0146] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A BeiDou anti-interference navigation method based on multi-source data fusion, characterized in that, include: Acquire target multi-source data; Based on the target multi-source data, extract the target interference features; Based on multiple target collaborative positioning nodes in a web3.0 decentralized collaborative network, target anomaly determination is performed according to the target interference characteristics to generate target collaborative results; Based on the target collaboration results, the target credibility operator is dynamically determined; Based on the target credibility operator, perform a multi-source data fusion operation to output the target anti-interference positioning result; The multiple target collaborative positioning nodes in the Web3.0 decentralized collaborative network perform target anomaly determination according to the target interference characteristics to generate target collaborative results, including: Based on the multiple target collaborative positioning nodes, the corresponding target anomaly judgment results are determined respectively; Perform a consistency evaluation operation on multiple target anomaly determination results to determine the target consistency evaluation result; If, based on the target consistency evaluation results, the target convergence is determined to be greater than or equal to a preset convergence threshold, the target collaboration result is generated. The target collaborative results include: Target trusted status information, target anomaly level information, and target fusion permission information; The step of dynamically determining the target credibility operator based on the target collaboration result includes: Based on the target collaboration results, update the first target credibility parameter set and the second target credibility parameter set; The target credibility operator is dynamically determined based on the updated first target credibility parameter set and the second target credibility parameter set; The first target credibility parameter set includes: first credibility parameters corresponding to multiple target cooperative positioning nodes; The second set of target credibility parameters includes: second credibility parameters corresponding to multiple target multi-source data.

2. The method according to claim 1, characterized in that, The step of updating the first target credibility parameter set and the second target credibility parameter set based on the target collaboration result includes: If the first abnormal frequency corresponding to the target cooperative positioning node is greater than or equal to the first preset frequency threshold within a number of consecutive time windows greater than or equal to the first preset number, then the first confidence parameter corresponding to the target cooperative positioning node is reduced. and, If, within a number of consecutive time windows greater than or equal to a second preset number, the second anomaly frequency corresponding to the target multi-source data is greater than or equal to a second preset frequency threshold, then the second confidence parameter corresponding to the target multi-source data is reduced.

3. The method according to claim 1, characterized in that, The step of performing target multi-source data fusion operation based on the target confidence operator to output target anti-interference localization result includes: Based on the target credibility operator, construct the target cost function; The target multi-source data fusion operation is performed according to the target cost function.

4. The method according to claim 3, characterized in that, The step of constructing the target cost function based on the target credibility operator includes: The target cost function is constructed according to the following formula: in, Used to represent the objective cost function; Used to represent prior residuals; Used to represent the prior residual information matrix; Used to represent inertial navigation residuals; Used to represent the inertial navigation information matrix; Used to represent robust kernel functions; Used to represent the residual of BeiDou observations; Used to represent the BeiDou information matrix; Used to represent Web3 collaborative residuals; Used to represent the information matrix of a collaborative network; Used to represent the initial state estimation vector; Used to represent The state estimation vector at time 1; Used to represent The state estimation vector corresponding to time step 1.

5. A BeiDou anti-interference navigation device based on multi-source data fusion, characterized in that, include: The acquisition unit is used to acquire target multi-source data; The extraction unit is used to extract target interference features based on the target multi-source data; The generation unit is used to perform target anomaly determination based on the target interference characteristics of multiple target collaborative positioning nodes in the web3.0 decentralized collaborative network, so as to generate target collaborative results. The determining unit is used to dynamically determine the target credibility operator based on the target collaboration result; The output unit is used to perform a multi-source data fusion operation on the target based on the target confidence operator to output the anti-interference positioning result of the target; The multiple target collaborative positioning nodes in the Web3.0 decentralized collaborative network perform target anomaly determination according to the target interference characteristics to generate target collaborative results, including: Based on the multiple target collaborative positioning nodes, the corresponding target anomaly judgment results are determined respectively; Perform a consistency evaluation operation on multiple target anomaly determination results to determine the target consistency evaluation result; If, based on the target consistency evaluation results, the target convergence is determined to be greater than or equal to a preset convergence threshold, the target collaboration result is generated. The target collaborative results include: Target trusted status information, target anomaly level information, and target fusion permission information; The step of dynamically determining the target credibility operator based on the target collaboration result includes: Based on the target collaboration results, update the first target credibility parameter set and the second target credibility parameter set; The target credibility operator is dynamically determined based on the updated first target credibility parameter set and the second target credibility parameter set; The first target credibility parameter set includes: first credibility parameters corresponding to multiple target cooperative positioning nodes; The second set of target credibility parameters includes: second credibility parameters corresponding to multiple target multi-source data.

6. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 4.

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