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

Abstract

The embodiment of the invention provides a Beidou anti-interference navigation method based on multi-source data fusion and related equipment, and relates to the technical field of satellite navigation, the method comprises the following steps: obtaining target multi-source data; extracting target interference features according to the target multi-source data; based on a plurality of target cooperative positioning nodes in the web3.0 decentralized cooperative network, respectively executing target anomaly judgment according to the target interference characteristics so as to generate a target cooperative result; dynamically determining a target credibility operator according to a target collaboration result; and according to the target credibility operator, executing a target multi-source data fusion operation to output a target anti-interference positioning result. According to the method, the credibility of the multi-source data can be adaptively adjusted along with the change of the interference environment, the influence of abnormal data on the positioning result can be inhibited in the complex interference environment, and the amplification effect of the positioning result caused by the abnormity of a single positioning node or a single data source is avoided; therefore, the stability and the reliability of an anti-interference positioning result are improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of satellite navigation technology, and in particular to a Beidou anti-interference navigation method based on multi-source data fusion and related equipment. BACKGROUND

[0002] Satellite navigation mainly refers to a technology for determining position information and time information of a ground or near-space object by receiving signals sent by navigation satellites.

[0003] In a complex electromagnetic environment, Beidou satellite navigation system positioning observation data is easily affected by factors such as interference and shielding, resulting in a decrease in positioning result accuracy. To improve the reliability of the positioning result, the existing technology usually adopts a multi-source data fusion method to jointly process Beidou satellite observation data and inertial measurement data or other auxiliary positioning data, so as to reduce the influence of abnormal data from a single data source on the positioning result.

[0004] However, most of the existing multi-source data fusion methods are based on fixed models or preset weights, and when the credibility of different data sources fluctuates with changes in the environment, it is difficult to reflect the quality differences of each data source in a timely manner, and abnormal data may still have an adverse effect on the fusion result. In addition, the existing anti-interference positioning methods mostly rely on a single positioning node or a centralized processing architecture, and the abnormality determination is mainly based on local signal characteristics, lacking a consistent verification mechanism across nodes, which makes the abnormality determination subject to single-point failure risk. SUMMARY

[0005] According to embodiments of the present application, a Beidou anti-interference navigation method based on multi-source data fusion and related equipment are provided, by acquiring target multi-source data and extracting target interference features, using multiple collaborative positioning nodes to respectively perform abnormality determination in a decentralized collaborative network, forming a target collaborative result, and based on the target collaborative result, dynamically determining a target credibility operator, which can adaptively adjust the credibility of multi-source data with changes in the interference environment, and performing multi-source data fusion operation based on the target credibility operator, which can suppress the influence of abnormal data on the positioning result in a complex interference environment, avoiding the amplification effect of a single positioning node or a single data source abnormality on the positioning result, thereby improving the stability and reliability of the anti-interference positioning result.

[0006] In a first aspect of the present application, a Beidou anti-interference navigation method based on multi-source data fusion is provided, comprising: acquiring target multi-source data; extracting target interference features according to the target multi-source data; based on multiple target collaborative positioning nodes in a web3.0 decentralized collaborative network, respectively performing target abnormality determination according to the target interference features to generate a target collaborative result; determine the target credibility operator dynamically according to the target coordination result; perform target multi-source data fusion operation according to the target credibility operator to output target anti-interference positioning result.

[0007] In some possible implementation manners, the target coordination result in the web3.0-based decentralized coordination network is generated by performing target anomaly determination on the target interference feature by each target coordination positioning node, and includes: determining a corresponding target anomaly determination result by each target coordination positioning node; performing consistency evaluation operation on the target anomaly determination results to determine a target consistency evaluation result; generating the target coordination result when the target convergence degree is greater than or equal to the preset convergence threshold according to the target consistency evaluation result.

[0008] In some possible implementation manners, the target coordination result includes: target credibility state information, target anomaly level information, and / or target fusion permission information.

[0009] In some possible implementation manners, the target credibility operator is determined dynamically according to the target coordination result, and includes: updating the first target credibility parameter set and / or the second target credibility parameter set according to the target coordination result; determining the target credibility operator dynamically according to the updated first target credibility parameter set and / or the second target credibility parameter set; The first target credibility parameter set includes first credibility parameters corresponding to the target coordination positioning nodes. The second target credibility parameter set includes second credibility parameters corresponding to the target multi-source data.

[0010] In some possible implementation manners, the first target credibility parameter set and / or the second target credibility parameter set is updated according to the target coordination result, and includes: decreasing the first credibility parameter corresponding to the target coordination positioning node if the first anomaly frequency corresponding to the target coordination positioning node is greater than or equal to the first preset frequency threshold in greater than or equal to the first preset number of continuous time windows; and / or, decreasing the second credibility parameter corresponding to the target multi-source data if the second anomaly frequency corresponding to the target multi-source data is greater than or equal to the second preset frequency threshold in greater than or equal to the second preset number of continuous time windows.

[0011] In some possible implementations, the target multi-source data fusion operation is performed according to the target credibility operator to output a target anti-interference positioning result, including: constructing a target cost function according to the target credibility operator; performing the target multi-source data fusion operation according to the target cost function.

[0012] In some possible implementations, the target cost function is constructed according to the target credibility operator, including: constructing the target cost function according to the following formula: (2) wherein, is used to represent the target cost function; is used to represent the prior residual; is used to represent a prior residual information matrix; is used to represent an inertial navigation residual; is used to represent an inertial navigation information matrix; is used to represent a robust kernel function; is used to represent a Beidou observation residual; is used to represent a Beidou information matrix; is used to represent a Web3 collaborative residual; is used to represent a collaborative network information matrix; is used to represent an initial state estimation vector; is used to represent a state estimation vector corresponding to the time t; is used to represent a state estimation vector corresponding to the time t.

[0013] In a second aspect, the application provides a Beidou anti-interference navigation device based on multi-source data fusion, including: an acquisition unit configured to acquire target multi-source data; an extraction unit configured to extract target interference features according to the target multi-source data; a generation unit configured to perform target anomaly determination according to the target interference features based on a plurality of target collaborative positioning nodes in a Web3.0 decentralized collaborative network, to generate target collaborative results; a determination unit configured to dynamically determine a target credibility operator according to the target collaborative results; an output unit configured to perform a target multi-source data fusion operation according to the target credibility operator to output a target anti-interference positioning result.

[0014] In a third aspect of the present application, an electronic device is provided. The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the method as described above when executing the program.

[0015] In a fourth aspect of the present application, a computer readable storage medium is provided, which stores a computer program, and the program is executed by a processor to implement the method according to the first aspect of the present application.

[0016] The Beidou anti-interference navigation method based on multi-source data fusion provided by the embodiments of the present application comprises: acquiring target multi-source data; extracting target interference features according to the target multi-source data; respectively performing target anomaly determination according to the target interference features based on a plurality of target collaborative positioning nodes in a web3.0 decentralized collaborative network to generate target collaborative results; dynamically determining a target credibility operator according to the target collaborative results; and performing target multi-source data fusion operation according to the target credibility operator to output a target anti-interference positioning result. In this way, by acquiring target multi-source data and extracting target interference features, anomaly determination is performed by a plurality of collaborative positioning nodes in a decentralized collaborative network to form target collaborative results, and a target credibility operator is dynamically determined based on the target collaborative results, so that the credibility of multi-source data can be adaptively adjusted according to changes in the interference environment, multi-source data fusion operation is performed based on the target credibility operator, the influence of abnormal data on the positioning result can be suppressed in a complex interference environment, and the amplification effect of a single positioning node or a single data source anomaly on the positioning result is avoided, thereby improving the stability and reliability of the anti-interference positioning result.

[0017] It should be understood that the content described in the summary section is not intended to limit the key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0018] The above and other features, advantages, and aspects of the embodiments of the present application will become more apparent by describing in detail the following embodiments with reference to the attached drawings. In the drawings, the same or similar reference numerals refer to the same or similar elements, in which: Figure 1 A flowchart of a Beidou anti-interference navigation method based on multi-source data fusion provided by the embodiments of the present application; Figure 2 A structural schematic diagram of a Beidou anti-interference navigation device based on multi-source data fusion provided by the embodiments of the present application; Figure 3 A structural schematic diagram of an electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0019] In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure clearer, the following will be combined with the drawings in the embodiments of the present disclosure to make a clear and complete description of the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are a part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present disclosure.

[0020] In addition, the term "and / or" in this paper is only to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are in an "or" relationship.

[0021] The first aspect of the embodiments of the present application proposes a Beidou anti-jamming navigation method based on multi-source data fusion. Figure 1 The flowchart of the Beidou anti-jamming navigation method 100 based on multi-source data fusion provided by the embodiments of the present application is shown in Figure 1 As shown in the figure, the method 100 includes: Step S1: obtaining target multi-source data.

[0022] Exemplarily, the above-mentioned target multi-source data can include: target Beidou satellite observation data, target inertial measurement data corresponding to the target Beidou satellite observation data, and / or target cooperative positioning node data, etc.

[0023] Among them, the above-mentioned target inertial measurement data can include: time dimension inertial measurement data corresponding to the target Beidou satellite observation data, and / or space dimension inertial measurement data Step S2: extracting target interference features according to the target multi-source data.

[0024] Exemplarily, the above-mentioned target multi-source data can be subjected to corresponding preprocessing operations before the above-mentioned step S2 is executed, so as to improve the quality of the target interference features.

[0025] Among them, the above-mentioned preprocessing operations can include: performing multipath error suppression operation on the target Beidou satellite observation data, performing zero offset stability compensation operation on the target inertial measurement data, etc.

[0026] Exemplarily, the above-mentioned target interference features can include: signal domain interference features, consistency domain interference features, and / or multi-source fusion residual interference features, etc.

[0027] Among them, the above-mentioned signal domain interference features can include: signal-to-noise ratio anomaly features, and / or energy distribution anomaly features, etc., for noise anomaly judgment.

[0028] The consistency domain interference feature can include a spatiotemporal residual feature, an ephemeris verification feature, and / or the like for spoofing anomaly determination.

[0029] The multi-source fusion residual interference feature can include a Mahalanobis distance residual feature based on factor graph optimization output, and / or the like for integrity state anomaly determination.

[0030] Step S3: 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 feature to generate a target cooperative result.

[0031] For example, the plurality of target cooperative positioning nodes can respectively perform target anomaly determination according to the target interference feature in combination with perceived electromagnetic environment data and / or historical evidence data to generate a target cooperative result.

[0032] The target cooperative positioning nodes can correspond to autonomous vehicle nodes, edge base station nodes, intelligent roadside unit nodes, and / or the like.

[0033] For example, the plurality of target cooperative positioning nodes can respectively perform spatial consistency anomaly determination and logical authenticity anomaly determination according to the target interference feature in combination with perceived electromagnetic environment data and / or historical evidence data.

[0034] The spatial consistency anomaly determination is used to determine the consistency of target interference feature signal distortion.

[0035] The logical authenticity anomaly determination is used to determine the legality of target cooperative positioning nodes verifying an ephemeris hash corresponding to the target interference feature through a Web3.0 protocol.

[0036] In some possible implementations, the step S3 of performing target anomaly determination according to the target interference feature based on a plurality of target cooperative positioning nodes in a web3.0 decentralized cooperative network to generate a target cooperative result includes: Step S31: Based on the plurality of target cooperative positioning nodes, a corresponding target anomaly determination result is respectively determined.

[0037] For example, a spatial consistency anomaly determination result and / or a logical authenticity anomaly determination result can be respectively determined based on the plurality of target cooperative positioning nodes.

[0038] Step S32: A consistency evaluation operation is performed on the plurality of target anomaly determination results to determine a target consistency evaluation result.

[0039] Exemplarily, the spatial consistency anomaly determination results corresponding to the plurality of target collaborative positioning nodes, and / or the logical authenticity anomaly determination results, can be subjected to consistency evaluation operations to determine the target consistency evaluation result.

[0040] Specifically, the spatial consistency anomaly determination results, and / or the logical authenticity anomaly determination results, can be quantified into a corresponding pair of anomaly confidence score vectors.

[0041] Specifically, the Euclidean distances between each pair of anomaly confidence score vectors can be calculated to determine a target convergence degree of the target consistency evaluation result.

[0042] Specifically, the target convergence degree can be determined based on the following formula: (1) wherein, is used to represent the target convergence degree, is used to represent the actual Euclidean distance between each pair of anomaly confidence score vectors, is used to represent the maximum preset Euclidean distance.

[0043] It can be understood that the target convergence degree and the Euclidean distance are negatively correlated, that is, the smaller the Euclidean distance, the higher the target convergence degree.

[0044] Step S33: generating a target collaboration result in a case where the target convergence degree is greater than or equal to a preset convergence threshold according to the target consistency evaluation result.

[0045] Exemplarily, in a case where the target convergence degree is greater than or equal to 70% according to the target consistency evaluation result, the target collaboration result can be generated.

[0046] It should be noted that the preset convergence threshold is positively correlated with the determination accuracy requirement of the target credibility operator, and / or the output accuracy requirement of the target anti-interference positioning result, that is, the higher the determination accuracy requirement of the target credibility operator, and / or the output accuracy requirement of the target anti-interference positioning result, the higher the preset convergence threshold.

[0047] It should be noted that the steps S32-S33 can be automatically triggered and executed by a smart contract preset in the Web3.0 network.

[0048] Through the distributed consensus verification of the target collaboration result by the steps S32-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 survival ability of the Beidou navigation system in the face of malicious fraud and complex interference, and further improving the output accuracy of the target anti-interference positioning result.

[0049] It should be noted that the target coordination result can include target trust state information, target anomaly level information, and / or target fusion permission information.

[0050] Exemplarily, the target trust state information can include trust state level information, such as fully trusted, partially trusted, or untrusted, etc.

[0051] Exemplarily, the target anomaly level information is used to quantify the severity of the target interference. For example, the target anomaly level information can correspond to a 1st level of slight multipath anomaly, or a 5th level of fatal spoofing interference anomaly, etc.

[0052] Exemplarily, the target fusion permission information can be used to determine whether the target Beidou satellite observation data, target inertial measurement data corresponding to the target Beidou satellite observation data, and / or target cooperative positioning node data, etc. participate in fusion. For example, the target fusion permission information can correspond to allowing the target Beidou satellite observation data to participate in fusion, limiting the weight of the target Beidou satellite observation data participating in fusion, or forcibly cutting off the target Beidou satellite observation data participating in fusion, etc.

[0053] By outputting the target coordination result in multiple dimensions, further precise data support can be provided for the dynamic determination of the target trust degree operator in step S4, thereby enhancing the environmental adaptability and robust survival ability of the Beidou navigation system in the face of malicious spoofing and complex interference, and further improving the output precision of the target anti-interference positioning result.

[0054] Step S4: dynamically determining a target trust degree operator according to the target coordination result.

[0055] Exemplarily, the target trust degree operator can be dynamically determined according to the target coordination result to realize differentiated weighting of different quality data sources, thereby improving the execution precision of the target multi-source data fusion operation.

[0056] In some feasible embodiments, the step S4 of dynamically determining a target trust degree operator according to the target coordination result includes: Step S41: updating a first target trust degree parameter set and / or a second target trust degree parameter set according to the target coordination result.

[0057] It should be noted that the first target trust degree parameter set can include first trust degree parameters corresponding to a plurality of target cooperative positioning nodes. The second target trust degree parameter set can include second trust degree parameters corresponding to a plurality of target multi-source data.

[0058] Exemplarily, in a case that the target cooperative positioning node corresponding target trust state information corresponding to the target cooperative result is determined to be fully trusted, the first trust degree parameter corresponding to the target cooperative positioning node can be raised to update the first target trust degree parameter set. In a case that the target cooperative positioning node corresponding target trust state information corresponding to the target cooperative result is determined to be untrusted, the first trust degree parameter corresponding to the target cooperative positioning node can be lowered to update the first target trust degree parameter set.

[0059] Exemplarily, in a case that the target multi-source data corresponding target trust state information corresponding to the target cooperative result is determined to be fully trusted, the second trust degree parameter corresponding to the target multi-source data can be raised to update the second target trust degree parameter set. In a case that the target multi-source data corresponding target trust state information corresponding to the target cooperative result is determined to be untrusted, the second trust degree parameter corresponding to the target multi-source data can be lowered to update the second target trust degree parameter set.

[0060] Exemplarily, in a case that the target cooperative positioning node corresponding target anomaly level information corresponding to the target cooperative result is determined to be 1st level slight multipath anomaly, the first trust degree parameter corresponding to the target cooperative positioning node can be raised to update the first target trust degree parameter set. In a case that the target cooperative positioning node corresponding target anomaly level information corresponding to the target cooperative result is determined to be 5th level fatal spoofing interference anomaly, the first trust degree parameter corresponding to the target cooperative positioning node can be lowered to update the first target trust degree parameter set.

[0061] Exemplarily, in a case that the target multi-source data corresponding target anomaly level information corresponding to the target cooperative result is determined to be 1st level slight multipath anomaly, the second trust degree parameter corresponding to the target multi-source data can be raised to update the second target trust degree parameter set. In a case that the target multi-source data corresponding target anomaly level information corresponding to the target cooperative result is determined to be 5th level fatal spoofing interference anomaly, the second trust degree parameter corresponding to the target multi-source data can be lowered to update the second target trust degree parameter set.

[0062] Exemplarily, in a case that the target multi-source data corresponding target fusion permission information corresponding to the target cooperative result is determined to be allowed to participate in fusion, the second trust degree parameter corresponding to the target multi-source data can be raised to update the second target trust degree parameter set. In a case that the target multi-source data corresponding to the target cooperative result is determined to be forced to be cut off fusion, the second trust degree parameter corresponding to the target multi-source data can be lowered to update the second target trust degree parameter set.

[0063] In some possible implementation manners, the step S41; updating the first target credibility parameter set and / or the second target credibility parameter set according to the target cooperative positioning result comprises: The 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 in greater than or equal to the first preset number of continuous time windows, the first credibility parameter corresponding to the target cooperative positioning node is reduced.

[0064] The step S412; for the target cooperative positioning node whose first credibility parameter has been reduced, if the first abnormal frequency corresponding to the target cooperative positioning node is less than the first preset frequency threshold in greater than or equal to the third preset number of continuous time windows, the first credibility parameter corresponding to the target cooperative positioning node is increased.

[0065] And / or, The step S413; if the second abnormal frequency corresponding to the target multi-source data is greater than or equal to the second preset frequency threshold in greater than or equal to the second preset number of continuous time windows, the second credibility parameter corresponding to the target multi-source data is reduced.

[0066] The step S414; for the target multi-source data whose second credibility parameter has been reduced, if the second abnormal frequency corresponding to the target multi-source data is less than the second preset frequency threshold in greater than or equal to the fourth preset number of continuous time windows, the second credibility parameter corresponding to the target multi-source data is increased.

[0067] It should be noted that the third preset number is not less than the first preset number, and the fourth preset number is not less than the second preset number, 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 recovered too early in the case that the abnormal state has not been eliminated sufficiently.

[0068] The first preset frequency threshold and the second preset frequency threshold can be set differently according to actual scene requirements.

[0069] Through the frequency determination mechanism based on continuous time window statistics, fine dynamic updating of the credibility parameters of the cooperative positioning nodes and the multi-source data can be realized, so as to enhance the recognition robustness of abnormal signals, isolate malicious cooperative positioning nodes or unstable multi-source data, and further improve the determination reliability of the target credibility operator in a complex interference scene, so as to further improve the execution accuracy of the target multi-source data fusion operation, and realize high-precision output of the target anti-interference positioning result.

[0070] The step S42; dynamically determining the target credibility operator according to the updated first target credibility parameter set and / or the second target credibility parameter set.

[0071] Exemplarily, the updated first target credibility parameter set and / or the second target credibility parameter set can be mapped to calculate the target credibility operator.

[0072] It should be noted that the target credibility operator can be used to adjust the contribution weight matrix corresponding to each observation item in the target multi-source data. The contribution weight matrix is used to represent the influence degree of each observation item on the state estimation during the execution of the target multi-source data fusion operation.

[0073] By dynamically updating the first target credibility parameter set and the second target credibility parameter set, and dynamically determining the target credibility operator, the robustness of identifying abnormal signals is significantly enhanced, the maliciously coordinated positioning nodes or unstable multi-source data are effectively isolated, and the determination reliability of the target credibility operator in a complex interference scene is improved. Therefore, the execution accuracy of the target multi-source data fusion operation is improved, and finally the high-precision output of the target anti-interference positioning result is realized.

[0074] Step S5: performing a target multi-source data fusion operation according to the target credibility operator to output a target anti-interference positioning result.

[0075] Exemplarily, according to the target credibility operator, a credibility weighting operation can be performed on the target multi-source data participating in the fusion to construct a target cost function. The target cost function is solved to realize joint optimization of the target multi-source data. According to the joint optimization result of the target multi-source data, the target anti-interference positioning result is output.

[0076] In some possible implementations, the step S5 of performing a target multi-source data fusion operation according to the target credibility operator to output a target anti-interference positioning result comprises: Step S51: constructing a target cost function according to the target credibility operator.

[0077] Exemplarily, a mapping calculation operation can be performed according to the target credibility operator to determine an inertial navigation information matrix, a Beidou information matrix, and / or a cooperative network information matrix, so as to construct the target cost function according to the prior residual and the corresponding prior residual information matrix, the inertial navigation residual and the corresponding inertial navigation information matrix, the Beidou observation residual and the corresponding Beidou information matrix, and / or the Web3 cooperative residual and the corresponding cooperative network information matrix.

[0078] It should be noted that the target credibility operator can dynamically adjust the contribution weight of different data sources in the target multi-source data fusion operation by weighting and scaling the information matrix corresponding to each residual item, so as to suppress the influence of abnormal data or disturbed data with low credibility on the fusion result, thereby improving the stability and reliability of the target anti-interference positioning result.

[0079] In some possible implementations, the step S51; constructing a target cost function according to a target credibility operator, comprises: The step S511; constructing a target cost function according to the following formula: (2) Wherein, is used to represent the target cost function; is used to represent the prior residual; is used to represent the prior residual information matrix; is used to represent the inertial navigation residual; is used to represent the inertial navigation information matrix; is used to represent the robust kernel function; is used to represent the Beidou observation residual; is used to represent the Beidou information matrix; is used to represent the Web3 collaborative residual; is used to represent the collaborative network information matrix; is used to represent the initial state estimation vector; is used to represent is used to represent the state estimation vector corresponding to the time t; is used to represent is used to represent the state estimation vector corresponding to the time t.

[0080] It should be noted that by minimizing the target cost function , the optimal state quantity of the target at the current time can be solved, such as position, velocity, attitude, etc.

[0081] Wherein, the above-mentioned prior residual can be used to determine the known estimation of the initial state or the historical state. The prior residual information matrix is used to represent the confidence of the prior data. The prior item constraint term is used to ensure that the current positioning calculation is continuous and will not deviate from the reasonable range of the historical trajectory.

[0082] Wherein, the above-mentioned inertial navigation residual is used to represent the motion increment constraint between the adjacent time, i.e. and . The above-mentioned inertial navigation information matrix is used to represent the confidence of the inertial navigation data, which is dynamically determined by the target credibility operator, such as increasing the inertial navigation information matrix when the interference is serious. The above-mentioned inertial navigation item constraint term is used to maintain the continuity of the positioning result by relying on the short-term high-precision calculation of the inertial navigation when the Beidou signal is interfered.

[0083] Wherein, the Beidou observation residual error For representing the deviation between the actual observation value of the satellite and the state prediction value. Robust kernel function For suppressing the negative impact of abnormal observation values, such as multipath interference signals, on overall optimization. Beidou information matrix The confidence of the Beidou observation data is dynamically determined by the target credibility operator. If the Beidou signal is severely disturbed, the Beidou information matrix is reduced To reduce the impact of the observation term on the final solution. The above Beidou observation constraint term For providing an absolute position reference for the system and correcting the cumulative drift error of the inertial navigation system.

[0084] Wherein, Web3 collaborative residual error For representing the deviation between the local state and the consensus result of other collaborative nodes in the decentralized network. Collaborative network information matrix The above Web3.0 collaborative constraint term can be determined according to the consistency evaluation result of multiple target collaborative positioning nodes. The above Web3.0 collaborative constraint term can be determined according to the consistency evaluation result of multiple target collaborative positioning nodes. By introducing the data of external nodes to correct local errors, even if all local sensors are disturbed, as long as the collaborative network is reliable, the positioning accuracy can still be maintained.

[0085] By constructing a target cost function including priori, motion, observation and collaborative multi-dimensional factors, weighted fusion and constraint modeling of multi-source heterogeneous data can be realized. By the synergistic effect of robust kernel function and collaborative residual error term, abnormal observation residual error can be nonlinearly suppressed in a complex interference environment, and on the premise of satisfying physical dynamics constraints, the positioning deviation of single machine can be corrected by the group consensus of decentralized network, thereby improving the stability and reliability of anti-interference positioning results in an adversarial environment.

[0086] Step S52; according to the target cost function, performing target multi-source data fusion operation.

[0087] Exemplarily, the target cost function in the above formula (2) can be solved to perform the target multi-source data fusion operation to realize joint optimization of the target multi-source data.

[0088] Specifically, the target cost function in the above formula (2) can be solved based on a preset iterative optimization algorithm, such as Gauss-Newton algorithm or LM algorithm, to realize joint optimization solution of the above target multi-source data by minimizing the total residual error, and obtain the optimal state quantity of the target at the current time, to output the target anti-interference positioning result.

[0089] By constructing a target cost function based on a target credibility operator and performing a target multi-source data fusion operation based on the target cost function, differentiating weighting and constraint modeling of target multi-source data of different sources and different qualities can be implemented, thereby suppressing the negative influence of abnormal data or disturbed data with low credibility on the fusion result, improving the stability and robustness of the target multi-source data fusion process, and further improving the accuracy and reliability of the target anti-interference positioning result in a complex interference environment.

[0090] Based on this, the Beidou anti-interference navigation method based on multi-source data fusion provided by the embodiments of the present application includes: acquiring target multi-source data; extracting target interference features according to the target multi-source data; based on a plurality of target collaborative positioning nodes in a web3.0 decentralized collaborative network, performing target anomaly determination according to the target interference features to generate target collaborative results; dynamically determining a target credibility operator according to the target collaborative results; and performing a target multi-source data fusion operation according to the target credibility operator to output a target anti-interference positioning result. The above method can make the credibility of multi-source data adaptively adjust with the change of the interference environment by acquiring target multi-source data and extracting target interference features, performing anomaly determination by a plurality of collaborative positioning nodes in a decentralized collaborative network to form target collaborative results, and dynamically determining a target credibility operator based on the target collaborative results. The multi-source data fusion operation based on the target credibility operator can suppress the influence of abnormal data on the positioning result in a complex interference environment, avoid the amplification effect of a single positioning node or a single data source anomaly on the positioning result, thereby improving the stability and reliability of the anti-interference positioning result.

[0091] It should be noted that for the above-mentioned method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the order of the described actions, because according to the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0092] The above is the introduction of the method embodiment, and the following will further illustrate the scheme described in the present application through the device embodiment.

[0093] The second aspect of the embodiments of the present application proposes a Beidou anti-interference navigation device based on multi-source data fusion. Figure 2 A structural schematic diagram of a Beidou anti-interference navigation device 200 based on multi-source data fusion provided by the embodiments of the present application. As shown in Figure 2The illustrated Beidou anti-interference navigation device 200 based on multi-source data fusion includes an acquisition unit 210, an extraction unit 220, a generation unit 230, a determination unit 240, and an output unit 250.

[0094] The acquisition unit 210 is configured to acquire target multi-source data. The extraction unit 220 is configured to extract target interference features according to the target multi-source data. The generation unit 230 is configured to perform target anomaly determination according to the target interference features based on a plurality of target collaborative positioning nodes in a web3.0 decentralized collaborative network, to generate a target collaborative result. The determination unit 240 is configured to dynamically determine a target credibility operator according to the target collaborative result. The output unit 250 is configured to perform target multi-source data fusion operation to output a target anti-interference positioning result according to the target credibility operator.

[0095] Figure 3 A structural schematic diagram of an electronic device 300 is provided for the embodiments of the present application. As shown in the figure, Figure 3 The electronic device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 302 or loaded from a storage portion 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for terminal device or server operation are also stored. The CPU 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0096] The following components are connected to the I / O interface 305: an input portion 306 including a keyboard, a mouse, and the like; an output portion 307 including a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a speaker, and the like; a storage portion 308 including a hard disk, and the like; and a communication portion 309 including a network interface card such as a LAN card, a modem, and the like. The communication portion 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like is mounted on the drive 310 as needed, so that a computer program read therefrom is installed in the storage portion 308 as needed.

[0097] In particular, the above method flow steps can be implemented as a computer software program in accordance with embodiments of the present application. For example, embodiments of the present application include a computer program product which includes a computer program tangibly embodied on a machine readable medium, the computer program containing program code for executing the methods illustrated in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 309, and / or installed from the removable media 311. When the computer program is executed by the central processing unit (CPU) 301, the above-described functions defined in the system of the present application are executed.

[0098] Note that the computer readable medium described in the present application can be a computer readable signal medium or a computer readable storage medium or any combination thereof. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination thereof. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus or device. In the present application, the computer readable signal medium can include a data signal propagated in a baseband or as a carrier wave in a propagated data signal, in which the computer readable program code is embodied. Such propagated data signal can take many forms, including but not limited to, an electromagnetic signal, an optical signal or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that can send, propagate or transfer 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 line, optical fiber cable, RF, etc., or any suitable combination thereof.

[0099] 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.

[0100] 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.

[0101] 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-described 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.

2. The method according to claim 1, characterized in that, 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 the target convergence is determined to be greater than or equal to a preset convergence threshold based on the target consistency evaluation results, the target collaboration result is generated.

3. The method according to claim 2, characterized in that, The target collaborative results include: Target trusted status information, target anomaly level information, and / or target fusion permission information.

4. The method according to claim 1, characterized in that, 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 / or the second target credibility parameter set; The target credibility operator is dynamically determined based on the updated first target credibility parameter set and / or 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.

5. The method according to claim 4, characterized in that, The step of updating the first target credibility parameter set and / or 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 / or, 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.

6. 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.

7. The method according to claim 6, 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.

8. 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.

9. 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 7.

10. A 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 method as described in any one of claims 1 to 7.

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