JTIDS relative navigation distributed information fusion method and device

Abstract

The invention discloses a JTIDS relative navigation distributed information fusion method and device, and belongs to the field of navigation information fusion. The method comprises the following steps: relative measurement linearization is carried out through a data chain; merging unknown errors of the sources into measurement errors; a source error is received from the P message, an equivalent measurement noise covariance matrix is calculated, and estimation of the source error is completed; the problem of excessive confidence caused by source errors can be relieved, and the precision and stability of relative navigation are guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of navigation information fusion, and more specifically, to a JTIDS relative navigation distributed information fusion method and device. Background Art

[0002] The complex and diverse navigation satellite jamming methods in modern battlefields are developing rapidly. Anti-jamming methods are difficult to cope with the new types of jamming methods, which brings more threats and challenges to the high-precision battlefield. How to maintain high-precision and high-stability local space and time references within the cluster in a satellite-guided denial environment has become a research focus at home and abroad. In the existing technology, Link-16 tactical data link is used to realize the intercommunication of data links between various services. The mutual position relationship between members is the most important tactical information in coordinated operations. The Joint Tactical Information Distribution System (JTIDS), as the communication carrier of Link-16 data link, has its own relative navigation potential that has been rapidly explored and developed. Since the 1970s and 1980s, the ability of JTIDS relative navigation function has been verified through a large number of theoretical studies and experiments. The realization of JTIDS relative navigation is based on precise network time synchronization. This time-based communication and navigation network enables each member in the network to measure the distance between each other, thereby obtaining position data using the intersection positioning principle.

[0003] In addition to mutual distance measurement, JTIDS relative navigation can also utilize the data exchange capability of the data link to broadcast its own data information to other members in the network, so that members can estimate the relative position and speed information between members even in the absence of a geographic positioning source. According to the different shared data information, it can be divided into two technical approaches: state estimation value sharing state estimation and measurement value sharing state estimation. For the state sharing architecture, according to the different logics of member classification and information fusion source selection, it can be divided into three types of relative navigation architectures: democratic, fixed-order, and covariance-based hierarchical. Among them, one of the solutions adopted by JTIDS relative navigation is a state sharing architecture based on covariance hierarchical levels. Based on this architecture, a practical solution for relative navigation is designed, and performance tests are carried out in the JTIDS prototype terminal.

[0004] In the state sharing JTIDS relative navigation based on covariance hierarchical level, the data link relative measurement obtained by the node includes geographic pseudorange, grid pseudorange and grid bias, which is not only a function of the node's own absolute position, relative position and clock error, but also a function of the source error. There is a complex correlation between each node and the source. In the distributed information fusion relative navigation architecture, each node only models its own state with the NC. When the source is not the NC, the node does not model the source error. If the correlation between the node and the source is not considered, it will lead to multiple fusions of the same source information and mistakenly over-trust the current relative measurement results, making the estimated covariance smaller than the true covariance, causing overconfidence problems, which is very harmful to relative navigation. Therefore, it is necessary to study the relative navigation information fusion method, reasonably handle the source error in relative measurement, and alleviate the overconfidence problem as much as possible.

[0005] In response to the overconfidence problem caused by information reuse, some foreign literature has disclosed some content. The existing solutions can be mainly divided into three categories: correlation maintenance method, correlation approximation method and covariance upper bound method: (1) The correlation maintenance method accurately maintains the correlation between cluster members through a large amount of communication to ensure the consistency of state estimation. However, maintaining the correlation requires multiple two-way communications within the cluster and a large communication capacity, which is difficult to achieve under the broadcast communication mode of data link time division multiple access and limited communication capacity; (2) The correlation approximation method approximates the correlation of cluster nodes to suppress the overconfidence problem of estimation. Communication can be limited to the two nodes where relative measurements occur. However, two-way communication is required to additionally transmit the cross covariance root maintained by itself with other nodes. It is not applicable to broadcast-based data link-based relative navigation systems where the communication content is pre-determined; (3) The covariance upper bound method refers to obtaining the covariance upper bound when the correlation is unknown to ensure the consistency of the fusion result. The typical solution is covariance interaction (Covariance The CI method uses the covariance matrix corresponding to information from different sources and takes minimizing the posterior state estimation covariance matrix as the cost function. It solves the weight of each information through optimization means, and then uses the optimized weight to fuse different information sources to obtain a consistent estimate. It does not require correlation between different information sources and has low requirements on communication links. However, the fusion result is usually too conservative, and the state estimation accuracy is low. In theory, the higher the state dimension, the lower the estimation accuracy, which is difficult to achieve in high-dimensional relative navigation systems.

[0006] JTIDS relative navigation can reliably establish a local space-time benchmark for aircraft clusters in real time under satellite-guided denial conditions, which is worthy of in-depth research. However, the overconfidence problem caused by the state sharing framework based on covariance hierarchical levels has a serious impact on the stability of the system and the accuracy of relative navigation. Therefore, it is necessary to conduct more in-depth research on the overconfidence problem of relative navigation to alleviate the impact of overconfidence as much as possible. Summary of the invention

[0007] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a JTIDS relative navigation distributed information fusion method and device, thereby improving the stability of JTIDS relative navigation information fusion and ensuring the relative positioning accuracy of the cluster.

[0008] The object of the present invention is achieved through the following solutions:

[0009] A JTIDS relative navigation distributed information fusion method comprises the following steps:

[0010] S1, linearize the nonlinear data chain relative measurement equation using the source selection module;

[0011] S2, merge the unknown error of the source into the measurement noise to construct an equivalent measurement error including the source error;

[0012] S3, reading the source quality level information required by the selected data link relative measurement method through the message, and reconstructing the source error variance;

[0013] S4, calculates the equivalent measurement noise covariance matrix according to the constructed equivalent measurement noise, takes the obtained equivalent measurement noise covariance matrix as output, inputs it into the relative navigation information fusion module as the system measurement error covariance, and completes the measurement update of the aircraft state.

[0014] Furthermore, in step S1, the linearization of the nonlinear data link relative measurement equation specifically includes the sub-steps of: Taylor expanding the nonlinear measurement equation near the local time update estimated state quantity to obtain a linearized measurement equation, wherein the linearized measurement equation is a linear combination of the local state error, source error and measurement noise.

[0015] Furthermore, the local state error includes a node error.

[0016] Furthermore, in step S1, the nonlinear data link relative measurement equation is linearized, including geographic pseudorange, grid pseudorange and grid bias, into a linear combination of node error, source error and measurement error.

[0017] Furthermore, in step S1, before the nonlinear data link relative measurement equation is linearized, the step is included: the node receives a PPLI message sent by the navigation source.

[0018] Furthermore, in step S2, the unknown error of the source is merged into the measurement noise to construct an equivalent measurement error including the source error, which specifically includes the sub-steps of: adding the unmodeled unknown error of the source as additional additive noise to the measurement noise to construct a new equivalent measurement equation including the source error.

[0019] Further, in step S3, the source quality level information required by the selected data link relative measurement mode is read through the message to reconstruct the source error variance, which specifically includes the following sub-steps:

[0020] According to the data link measurement method selected by the source selection module, the required source quality level information is read from the received PPLI message, and the corresponding source state error covariance matrix is ​​reconstructed according to the quality level conversion formula.

[0021] Further, in step S4, the data link measurement method selected by the source selection module reads the required source quality level information from the received PPLI message, and reconstructs the corresponding source state error covariance matrix according to the quality level conversion formula, which specifically includes the following sub-steps:

[0022] The source error is substituted into the equivalent measurement error to obtain the equivalent measurement error covariance matrix, which is used as the new measurement error to measure and update the node state; when the relative navigation information fusion performs geographic pseudorange measurement update, the geographic location quality and relative clock quality are selected from the PPLI message to reconstruct the source error; when the relative navigation information fusion performs grid pseudorange measurement update, the relative position quality, height quality and relative clock quality are selected from the PPLI message to reconstruct the source error; when the relative navigation information fusion performs grid bias measurement update, the geographic location quality, relative position quality and height quality are selected from the PPLI message to reconstruct the source error.

[0023] Further, in step S4, the outputting the obtained equivalent measurement noise covariance matrix specifically includes outputting the obtained equivalent measurement noise covariance matrix as a SPA method.

[0024] A JTIDS relative navigation distributed information fusion device comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is loaded by the processor, any of the above methods is executed.

[0025] The beneficial effects of the present invention include:

[0026] (1) The present invention is based on the actual needs of cluster relative navigation under the satellite-guided navigation denial conditions of aviation airborne platforms. By supplementing the partitioning method to alleviate the overconfidence problem caused by the distributed information fusion architecture, the stability of JTIDS relative navigation information fusion can be improved, and the relative positioning accuracy of the cluster can be guaranteed. Specifically, a distributed information fusion method based on a state sharing framework of covariance hierarchical level in JTIDS relative navigation is provided. The unknown error of the source is added to the measurement noise as additive noise to construct a new equivalent measurement noise containing the source error; secondly, the quality level obtained by converting the source error is obtained through the PPLI message, and the source error variance is reconstructed to obtain the equivalent measurement noise covariance matrix, so as to reasonably consider the influence of the source error in the process of updating the node state measurement, alleviate the influence of overconfidence on the relative navigation system, and ensure the accuracy and stability of relative navigation.

[0027] (2) The solution of the present invention has low implementation cost and simple and reliable design principle. The relative navigation source selection logic designed by the present invention is implemented by loading a software algorithm module in the communication, navigation and identification subsystems, receiving PPLI messages, data link relative navigation measurement values ​​and local inertial navigation data through a reserved function module interface, and executing module functions. There is no need to modify the hardware of the existing communication, navigation and identification subsystems. The design principle is simple and reliable, which greatly reduces the implementation cost.

[0028] (3) The scheme of the present invention has good versatility and strong portability. The scheme design idea is applicable to all current airborne platforms. It only needs to control the sensor output to input according to the method defined by the function module interface to execute the function of relative navigation SPA. The scheme has good versatility and strong portability, which is convenient for the debugging and maintenance of the later software algorithm.

[0029] (4) The results of the scheme of the present invention are reliable and the relative navigation information fusion stability is high. The unknown errors of the source can be processed through the designed supplementary partitioning method, and the influence of the overconfidence problem brought by the distributed information fusion framework on the relative navigation information fusion stability of the system can be alleviated as much as possible, which has a high theoretical value.

[0030] (5) The scheme of the present invention is applicable to constructing a local space-time reference through data link relative navigation technology under satellite navigation denial conditions. The designed supplementary partitioning method is used in relative navigation information fusion to alleviate the overconfidence problem and ensure the stability and navigation accuracy of relative navigation information fusion. The designed supplementary partitioning method can be implemented through software algorithms. The design principle is simple and reliable. The algorithm has strong versatility and portability, reduces the influence of the state error of the unmodeled source on the system stability, and has strong engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0032] Figure 1 Schematic diagram of input and output of the relative navigation SPA method software in an embodiment of the present invention;

[0033] Figure 2 This is the overall execution flow of the software algorithm of the relative navigation SPA method in the embodiment of the present invention. DETAILED DESCRIPTION

[0034] All features disclosed in all embodiments in this specification, or steps in all methods or processes implicitly disclosed, except for mutually exclusive features and / or steps, can be combined and / or expanded or replaced in any manner.

[0035] The specific implementation process of the present invention is as follows:

[0036] Figure 1 The input and output schematic diagram of the relative navigation SPA method software of the present invention is given. Figure 1 In the process, the PPLI message from aircraft j is sent to aircraft i by the JTIDS terminal inside the aircraft through the antenna in the form of electromagnetic waves. After the antenna of aircraft i receives the electromagnetic wave signal sent by aircraft j, the RF / baseband signal processing is completed in the JTIDS terminal, and the signal arrival time TOA is detected. The TOA distance information can be obtained by subtracting the start time of the current time slot from this time. This distance represents the distance from aircraft j to aircraft i measured by JTIDS, and is used as the measurement information input of geographic pseudorange and grid pseudorange. Secondly, the digital signal after signal processing is demodulated to obtain the PPLI message of aircraft j. The PPLI message is used as one of the inputs of the relative navigation SPA method. The data items in the PPLI message include: the optimal estimate of the longitude, latitude and altitude of aircraft j λ i ,L i ,h i , optimal estimate of grid coordinates u i ,v i , relative position quality Time Quality Location Quality And high quality The second input of the navigation source selection module is the source selection result of aircraft i outputted in real time by the navigation information fusion module, the position estimate updated at the current step time, and the relative position of aircraft i. The above 16 data are used as the input of the relative navigation SPA method. After completing the reconstruction of the equivalent measurement error, the module outputs the equivalent measurement error covariance matrix calculated in real time to the relative navigation information fusion module.

[0037] Figure 2 The overall execution flow of the software algorithm of the relative navigation SPA method of the present invention is given, which specifically includes the following contents:

[0038] (1) After the node receives the PPLI message sent by the navigation source, the source selection module linearizes the nonlinear data link relative measurement equation, including geographic pseudorange, grid pseudorange and grid bias, into a linear combination of node error, source error and measurement error;

[0039] (2) Incorporate the unknown error of the source into the measurement noise to construct an equivalent measurement error that includes the source error;

[0040] (3) Read the source quality level information required by the selected data link relative measurement method through the PPLI message and reconstruct the source error variance;

[0041] Substitute the source error into the equivalent measurement error to obtain the equivalent measurement error covariance matrix, and use it as the new measurement error to measure and update the node state. When relative navigation information fusion performs geographic pseudorange measurement update, it is necessary to select the geographic location quality and relative clock quality from the PPLI message to reconstruct the source error; when relative navigation information fusion performs grid pseudorange measurement update, it is necessary to select the relative position quality, height quality and relative clock quality from the PPLI message to reconstruct the source error; when relative navigation information fusion performs grid bias measurement update, it is necessary to select the geographic location quality, relative position quality and height quality from the PPLI message to reconstruct the source error.

[0042] (4) Calculate the equivalent measurement noise covariance matrix based on the constructed equivalent measurement noise.

[0043] The above technical solution of the present invention is further described in detail as follows:

[0044] (1) First, the nonlinear measurement equation is Taylor expanded around the local time update estimated state quantity to obtain the linearized measurement equation. Taking geographic pseudorange as an example, the measurement equation of geographic pseudorange is as follows:

[0045]

[0046] In the formula, subscript i represents the local device, and subscript j represents the source. δt i The modeled state quantities are: the three-axis coordinates and clock error of the Earth-centered Earth-fixed system of the local machine, δt jis the unmodeled state quantity: the three-axis coordinates and clock difference of the Earth-centered Earth-fixed system of the source. Taylor expansion of the above formula around the local time update estimated state quantity yields:

[0047]

[0048] In the formula, represents the estimated value of variable a, and H0 is the Jacobian matrix of Taylor expansion.

[0049] (2) The third and fifth terms in the above formula are the unmodeled source state errors, which cannot be estimated directly. Therefore, the unknown state errors of the source are incorporated into the measurement noise to construct a new equivalent measurement noise:

[0050]

[0051] The geographic state quality of the source expresses the magnitude of the three-axis position error of the source in the navigation system, so H2 in the second step is H0 multiplied by the coordinate transformation matrix that transforms the source state error from the Earth-centered Earth-fixed system to the navigation system.

[0052] (3) According to the data link measurement method selected by the source, the required source quality level information is read from the received PPLI message: when the relative navigation information fusion performs geographic pseudorange measurement update, the geographic location quality and relative clock quality need to be selected from the PPLI message; when the relative navigation information fusion performs grid pseudorange measurement update, the relative position quality, height quality and relative clock quality need to be selected from the PPLI message; when the relative navigation information fusion performs grid bias measurement update, the geographic location quality, relative position quality and height quality need to be selected from the PPLI message. Reconstruct the corresponding source state error covariance matrix according to the quality level conversion formula.

[0053] (4) Calculate the equivalent measurement error covariance matrix. Taking geographic pseudorange as an example, based on the constructed equivalent measurement noise, the equivalent measurement noise covariance expression is obtained:

[0054]

[0055] In the formula, represents the source absolute horizontal position error covariance matrix, p h j represents the source absolute height error covariance, represents the source relative clock phase error covariance, obtained by step (3); represents the geographic pseudorange measurement noise w geog The corresponding covariance.

[0056] Finally, the obtained equivalent measurement noise covariance matrix is ​​used as the output of the SPA method and input into the relative navigation information fusion module as the system measurement error covariance to complete the measurement update of the aircraft i state.

[0057] See Table 1. Table 1 shows the specific implementation method of calculating the source state error covariance matrix by the relative navigation SPA method, that is, the method of reconstructing the corresponding source state error covariance matrix according to the quality level conversion formula.

[0058] Table 1

[0059]

[0060] When reconstructing the covariance matrix according to the quality level, it is only necessary to take the upper limit of the state estimation uncertainty level corresponding to the quality level according to Table 1, square it and assign the value. Taking the position quality as an example, assuming that its quality level is known to be q geog , (0≤q geog ≤15), referring to Table 1, we can see that the upper limit of the corresponding estimated uncertainty level is l geog , then the absolute horizontal position estimation covariance matrix P 2×2 (δx n ,δy n ) is calculated as follows:

[0061]

[0062] The principles of relative position quality, height quality, and relative clock vector reconstruction covariance are the same as above.

[0063] It should be noted that within the scope of protection defined in the claims of the present invention, the following embodiments can be combined and / or expanded or replaced in any logical way from the above specific implementation methods, such as disclosed technical principles, disclosed technical features or implicitly disclosed technical features.

[0064] In other embodiments, including but not limited to the following embodiments:

[0065] Example 1

[0066] A JTIDS relative navigation distributed information fusion method comprises the following steps:

[0067] S1, linearize the nonlinear data chain relative measurement equation using the source selection module;

[0068] S2, merge the unknown error of the source into the measurement noise to construct an equivalent measurement error including the source error;

[0069] S3, reading the source quality level information required by the selected data link relative measurement method through the message, and reconstructing the source error variance;

[0070] S4, calculates the equivalent measurement noise covariance matrix according to the constructed equivalent measurement noise, takes the obtained equivalent measurement noise covariance matrix as output, inputs it into the relative navigation information fusion module as the system measurement error covariance, and completes the measurement update of the aircraft state.

[0071] Example 2

[0072] On the basis of Example 1, in step S1, the nonlinear data link relative measurement equation is linearized, which specifically includes the sub-steps of: Taylor expanding the nonlinear measurement equation near the local time update estimated state quantity to obtain a linearized measurement equation, and the linearized measurement equation is a linear combination of the local state error, source error and measurement noise.

[0073] Example 3

[0074] Based on Embodiment 2, the local state error includes a node error.

[0075] Example 4

[0076] On the basis of Example 3, in step S1, the nonlinear data link relative measurement equation is linearized, including geographic pseudorange, grid pseudorange and grid bias, and linearized into a linear combination of node error, source error and measurement error.

[0077] Example 5

[0078] On the basis of Embodiment 3, in step S1, before linearizing the nonlinear data link relative measurement equation, the step is included: the node receives a PPLI message sent by the navigation source.

[0079] Example 6

[0080] On the basis of Example 1, in step S2, the unknown error of the source is merged into the measurement noise to construct an equivalent measurement error including the source error, which specifically includes the sub-steps of: adding the unmodeled unknown error of the source as additional additive noise to the measurement noise, and constructing a new equivalent measurement equation including the source error.

[0081] Example 7

[0082] On the basis of Example 5, in step S3, the source quality level information required by the selected data link relative measurement mode is read through the message to reconstruct the source error variance, which specifically includes the following sub-steps:

[0083] According to the data link measurement method selected by the source selection module, the required source quality level information is read from the received PPLI message, and the corresponding source state error covariance matrix is ​​reconstructed according to the quality level conversion formula.

[0084] Example 8

[0085] On the basis of Example 7, in step S4, the data link measurement method selected by the source selection module reads the required source quality level information from the received PPLI message, and reconstructs the corresponding source state error covariance matrix according to the quality level conversion formula, specifically including the sub-steps:

[0086] The source error is substituted into the equivalent measurement error to obtain the equivalent measurement error covariance matrix, which is used as the new measurement error to measure and update the node state; when the relative navigation information fusion performs geographic pseudorange measurement update, the geographic location quality and relative clock quality are selected from the PPLI message to reconstruct the source error; when the relative navigation information fusion performs grid pseudorange measurement update, the relative position quality, height quality and relative clock quality are selected from the PPLI message to reconstruct the source error; when the relative navigation information fusion performs grid bias measurement update, the geographic location quality, relative position quality and height quality are selected from the PPLI message to reconstruct the source error.

[0087] Example 9

[0088] On the basis of Embodiment 1, in step S4, the outputting the obtained equivalent measurement noise covariance matrix specifically includes outputting the obtained equivalent measurement noise covariance matrix as a SPA method output.

[0089] Example 10

[0090] A JTIDS relative navigation distributed information fusion device comprises a processor and a memory, wherein the memory stores a computer program, and when the computer program is loaded by the processor, the method described in any one of Examples 1 to 9 is executed.

[0091] The units involved in the embodiments of the present invention may be implemented by software or hardware, and the units described may also be arranged in a processor. The names of these units do not, in some cases, limit the units themselves.

[0092] According to one aspect of an embodiment of the present invention, a computer program product or a computer program is provided, the computer program product or the computer program includes a computer instruction, and the computer instruction is stored in a computer-readable storage medium. A processor of a computer device reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the method provided in the above various optional implementations.

[0093] As another aspect, an embodiment of the present invention further provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiment.

Claims

1. A JTIDS relative navigation distributed information fusion method, characterized in that: The following steps are involved: S1, linearize the nonlinear data chain relative measurement equation using the source selection module; S2, merge the unknown error of the source into the measurement noise to construct an equivalent measurement error including the source error; S3, reading the source quality level information required by the selected data link relative measurement method through the message, and reconstructing the source error variance; S4, calculates the equivalent measurement noise covariance matrix according to the constructed equivalent measurement noise, takes the obtained equivalent measurement noise covariance matrix as output, inputs it into the relative navigation information fusion module as the system measurement error covariance, and completes the measurement update of the aircraft state.

2. The JTIDS relative navigation distributed information fusion method according to claim 1, characterized in that: In step S1, the nonlinear data link relative measurement equation is linearized, specifically including the sub-steps of: Taylor expansion of the nonlinear measurement equation near the local time update estimated state quantity to obtain a linearized measurement equation, wherein the linearized measurement equation is a linear combination of the local state error, source error and measurement noise.

3. The JTIDS relative navigation distributed information fusion method according to claim 2, characterized in that: The local state error includes a node error.

4. The JTIDS relative navigation distributed information fusion method according to claim 3 is characterized in that: In step S1, the nonlinear data link relative measurement equation is linearized, including geographic pseudorange, grid pseudorange and grid bias, into a linear combination of node error, source error and measurement error.

5. The JTIDS relative navigation distributed information fusion method according to claim 3 is characterized in that: In step S1, before the nonlinear data link relative measurement equation is linearized, the step is included: the node receives a PPLI message sent by a navigation source.

6. The JTIDS relative navigation distributed information fusion method according to claim 1, characterized in that: In step S2, the unknown error of the source is merged into the measurement noise to construct an equivalent measurement error including the source error, which specifically includes the sub-steps of: adding the unmodeled unknown error of the source as additional additive noise to the measurement noise to construct a new equivalent measurement equation including the source error.

7. The JTIDS relative navigation distributed information fusion method according to claim 5, characterized in that: In step S3, the source quality level information required by the selected data link relative measurement method is read through the message to reconstruct the source error variance, which specifically includes the following sub-steps: According to the data link measurement method selected by the source selection module, the required source quality level information is read from the received PPLI message, and the corresponding source state error covariance matrix is ​​reconstructed according to the quality level conversion formula.

8. The JTIDS relative navigation distributed information fusion method according to claim 7, characterized in that: In step S4, the data link measurement method selected by the source selection module reads the required source quality level information from the received PPLI message, and reconstructs the corresponding source state error covariance matrix according to the quality level conversion formula, which specifically includes the following sub-steps: The source error is substituted into the equivalent measurement error to obtain the equivalent measurement error covariance matrix, which is used as the new measurement error to measure and update the node state; when the relative navigation information fusion performs geographic pseudorange measurement update, the geographic location quality and relative clock quality are selected from the PPLI message to reconstruct the source error; when the relative navigation information fusion performs grid pseudorange measurement update, the relative position quality, height quality and relative clock quality are selected from the PPLI message to reconstruct the source error; when the relative navigation information fusion performs grid bias measurement update, the geographic location quality, relative position quality and height quality are selected from the PPLI message to reconstruct the source error.

9. The JTIDS relative navigation distributed information fusion method according to claim 1, characterized in that: In step S4, the outputting the obtained equivalent measurement noise covariance matrix specifically includes outputting the obtained equivalent measurement noise covariance matrix as a SPA method output.

10. A JTIDS relative navigation distributed information fusion device, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is loaded by the processor, the method according to any one of claims 1 to 9 is executed.

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