Cooperative navigation method and device based on MATLAB system object

Abstract

The invention discloses a collaborative navigation method and device based on MATLAB (matrix laboratory) system objects, and relates to the technical field of navigation, the method comprises the following steps: S1, creating SdInertNavInt objects; s2, a KalmFilt object is created; s3, a CoopNavNode object is created; s4, creating a CoopNavCluster object, and formulating an information interaction strategy between the nodes; and S5, externally calling a CoopNavCluster.step through () function to operate an intra-cluster collaborative navigation algorithm. According to the invention, the coupling degree between the modules is reduced, and multi-node parallel collaborative navigation calculation is facilitated. The architecture can be compatible with master-slave collaborative navigation and parallel collaborative navigation, can be compatible with centralized filtering and distributed filtering, and can be compatible with absolute measurement and relative measurement.

Description

Technical Field

[0001] The present invention relates to the technical field of navigation, and particularly to a cooperative navigation method and device based on MATLAB system objects. Background Art

[0002] Cooperative navigation is a technology in which nodes in a cooperative formation solve and correct their own navigation information in real time through the interaction of navigation information, so as to ensure the maintenance of the cooperative formation configuration and the successful completion of cooperative tasks. According to the information interaction architecture within the formation, cooperative formations can be divided into parallel and master-slave types.

[0003] In a master-slave cooperative navigation formation, the master node needs to carry high-precision navigation sensors, and the slave nodes carry low-cost and low-precision navigation sensors. During cooperative navigation, the master node provides high-precision navigation information for the slave nodes in the formation. The slave nodes correct their own navigation results by receiving the high-precision navigation information of the master node or obtaining relative pose information by observing the master node. The master-slave structure is simple, the system cost is low, and the network topology is clear. However, it has a high dependence on the navigation accuracy of the master node, and the master node is required to have high robustness.

[0004] In a parallel cooperative navigation formation, each node is equipped with navigation sensor devices of the same accuracy. During navigation, mutual correction is achieved by fusing the positioning data of each node. The parallel structure can still ensure the accurate positioning of the remaining nodes when a certain node fails or communication fails, and has higher robustness. However, the decentralized parallel network structure has a complex topology, and the mutual correction mechanism between nodes easily leads to problems with state correlation, and the fusion algorithm is also more complex.

[0005] Cooperative navigation technology mainly involves relative navigation methods, formation configurations, formation information interaction, cooperative positioning algorithms and other technologies. For the core cooperative navigation positioning algorithm, the mainstream is still the navigation algorithm based on Bayesian estimation. The filtering methods based on Bayesian estimation include extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), particle filter (PF), information filter (IF), etc.

[0006] The traditional process-oriented cooperative navigation algorithm architecture has the following disadvantages: 1) It is difficult to reuse, lacks flexibility, and the complexity and maintenance difficulty of the code are very high; 2) It is not easy to expand. When adding or reducing nodes in cooperative navigation, the code modification is very troublesome; 3) The structure is not intuitive and difficult to maintain. When modifying the communication strategy within the cooperative navigation formation, the structure of the code is difficult to understand and maintain.

[0007] Therefore, to meet the actual needs, a cooperative navigation technology based on MATLAB system objects is provided. Summary of the Invention

[0008] Aiming at the defects existing in the prior art, the purpose of the present invention is to provide a cooperative navigation method and device based on MATLAB system objects, which reduces the coupling degree between modules and facilitates multi-node parallel cooperative navigation calculation. This architecture can be compatible with master-slave cooperative navigation and parallel cooperative navigation, can be compatible with centralized filtering and distributed filtering, and can be compatible with absolute measurement and relative measurement.

[0009] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0010] In a first aspect, the present application provides a cooperative navigation method based on MATLAB system objects, and the method includes the following steps:

[0011] S1: Create an SdInertNavInt object for performing inertial navigation numerical integration calculation of a single node;

[0012] S2: Create a KalmFilt object for performing kalman filtering calculation of a single node;

[0013] S3: Create a CoopNavNode object for implementing the cooperative navigation of its own node;

[0014] S4: Create a CoopNavCluster object for implementing the initialization of nodes within a cluster, and formulate an information interaction strategy between nodes;

[0015] S5: Externally call the CoopNavCluster.stepthrough() function to run the cooperative navigation algorithm within the cluster.

[0016] Based on the above technical solution, the CoopNavNode object is compatible with absolute measurement and relative measurement.

[0017] Based on the above technical solution, the absolute measurement includes position, velocity, pseudorange, and pseudorange rate.

[0018] Based on the above technical solution, the relative measurement includes relative ranging, relative velocity measurement, and relative angle measurement.

[0019] Based on the above technical solution, in the step of creating a CoopNavCluster object for implementing the initialization of nodes within a cluster and formulating an information interaction strategy between nodes, a centralized filtering process is included, and the centralized filtering process includes the following steps:

[0020] Each node within the cluster performs inertial navigation numerical integration;

[0021] Each node within the cluster transmits data to the master node;

[0022] Perform centralized filtering calculation at the cluster's main node;

[0023] Transmit the centralized filtering result back to each node and update it.

[0024] Based on the above technical solution, in the creation of the CoopNavCluster object for initializing the nodes within the cluster and formulating the information interaction strategy among the nodes, a distributed filtering process is included. The distributed filtering process includes the following steps:

[0025] Formulate a communication strategy according to the algorithm design;

[0026] Each node within the cluster performs inertial navigation numerical integration;

[0027] According to the communication strategy, information interaction occurs within the cluster;

[0028] Each node performs cooperative navigation filtering processing;

[0029] According to the algorithm design, transmit the result after cooperative navigation filtering processing to relevant nodes for update.

[0030] In a second aspect, the present application also provides a cooperative navigation device based on MATLAB system objects. The device includes:

[0031] An object creation module, which is used to create an SdInertNavInt object for performing inertial navigation numerical integration calculation of a single node;

[0032] The object creation module is also used to create a KalmFilt object for performing kalman filtering calculation of a single node;

[0033] The object creation module is also used to create a CoopNavNode object for implementing the cooperative navigation of its own node;

[0034] The object creation module is also used to create a CoopNavCluster object for initializing the nodes within the cluster and formulate the information interaction strategy among the nodes;

[0035] A function call module, which is used to externally call the CoopNavCluster.stepthrough() function to run the cooperative navigation algorithm within the cluster.

[0036] Based on the above technical solution, the CoopNavNode object is compatible with absolute measurements and relative measurements.

[0037] Based on the above technical solution, the absolute measurements include position, velocity, pseudorange, and pseudorange rate.

[0038] Based on the above technical solution, the relative measurement includes relative ranging, relative speed measurement, and relative angle measurement.

[0039] Based on the above technical solution, the object creation module is further configured to execute a centralized filtering process, and the centralized filtering process includes the following steps:

[0040] Each node within the cluster performs inertial navigation numerical integration;

[0041] Each node within the cluster transmits the data to the master node;

[0042] Centralized filtering calculation is performed at the master node within the cluster;

[0043] The centralized filtering result is transmitted back to each node and updated.

[0044] Based on the above technical solution, the object creation module is further configured to execute a distributed filtering process, and the distributed filtering process includes the following steps:

[0045] Formulate a communication strategy according to the algorithm design;

[0046] Each node within the cluster performs inertial navigation numerical integration;

[0047] According to the communication strategy, information interaction is carried out within the cluster;

[0048] Each node performs cooperative navigation filtering processing;

[0049] According to the algorithm design, the result after cooperative navigation filtering processing is transmitted to the relevant nodes for updating.

[0050] Compared with the prior art, the advantages of the present invention are as follows:

[0051] The present invention reduces the coupling degree between modules, facilitating multi-node parallel cooperative navigation calculation. This architecture can be compatible with master-slave cooperative navigation and parallel cooperative navigation, can be compatible with centralized filtering and distributed filtering, and can be compatible with absolute measurement and relative measurement. Description of the Drawings

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0053] Figure 1 It is the step flow chart of the cooperative navigation method based on the MATLAB system object in the embodiment of the present invention;

[0054] Figure 2 The block diagram of the cooperative navigation device based on MATLAB system objects according to the embodiment of the present invention. Specific embodiments

[0055] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0056] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0057] The embodiments of the present application provide a cooperative navigation method and device based on MATLAB system objects, which reduce the coupling degree between modules and facilitate multi-node parallel cooperative navigation calculation. This architecture can be compatible with master-slave cooperative navigation and parallel cooperative navigation, can be compatible with centralized filtering and distributed filtering, and can be compatible with absolute measurement and relative measurement.

[0058] To achieve the above technical effects, the general idea of the present application is as follows:

[0059] A cooperative navigation method based on MATLAB system objects, the cooperative navigation method based on MATLAB system objects includes the steps of:

[0060] S1: Create an SdInertNavInt object for performing inertial navigation numerical integration calculation of a single node;

[0061] S2: Create a KalmFilt object for performing Kalman filtering calculation of a single node;

[0062] S3: Create a CoopNavNode object for implementing the cooperative navigation of its own node;

[0063] S4: Create a CoopNavCluster object for implementing the initialization of nodes within a cluster, and formulate an information interaction strategy between nodes;

[0064] S5: Externally call the CoopNavCluster.stepthrough() function to run the cooperative navigation algorithm within the cluster.

[0065] The embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0066] In the first aspect, see Figure 1As shown in the figure, an embodiment of the present application provides a cooperative navigation method based on MATLAB system objects, and the method includes the following steps:

[0067] S1: Create an SdInertNavInt object for performing inertial navigation numerical integration calculation of a single node;

[0068] S2: Create a KalmFilt object for performing Kalman filtering calculation of a single node;

[0069] S3: Create a CoopNavNode object for implementing cooperative navigation of its own node;

[0070] S4: Create a CoopNavCluster object for implementing initialization of nodes within a cluster, and formulate an information interaction strategy between nodes;

[0071] S5: Externally call the CoopNavCluster.stepthrough() function to run the cooperative navigation algorithm within the cluster.

[0072] Regarding the problems of difficult expansion, difficult reuse, and difficult maintenance existing in the traditional process-oriented cooperative navigation algorithm architecture. In the embodiment of the present application, the coupling degree between modules is reduced, which is convenient for performing multi-node parallel cooperative navigation calculations. This architecture can be compatible with master-slave cooperative navigation and parallel cooperative navigation, can be compatible with centralized filtering and distributed filtering, and can be compatible with measurement types such as absolute measurement (position, velocity, pseudorange, pseudorange rate) and relative measurement (relative ranging, relative velocity measurement, relative angle measurement).

[0073] Further, the CoopNavNode object is compatible with absolute measurement and relative measurement.

[0074] Further, the absolute measurement includes position, velocity, pseudorange, and pseudorange rate.

[0075] Further, the relative measurement includes relative ranging, relative velocity measurement, and relative angle measurement.

[0076] Further, in the step of creating a CoopNavCluster object for implementing initialization of nodes within a cluster and formulating an information interaction strategy between nodes, a centralized filtering process is included, and the centralized filtering process includes the following steps:

[0077] Each node within the cluster performs inertial navigation numerical integration;

[0078] Each node within the cluster transmits data to the master node;

[0079] Perform centralized filtering calculation at the master node within the cluster;

[0080] The centralized filtering result is transmitted back to each node for update.

[0081] Furthermore, in creating the CoopNavCluster object for initializing the nodes within the cluster and formulating the information interaction strategy among nodes, a distributed filtering process is included. The distributed filtering process includes the following steps:

[0082] Formulate a communication strategy according to the algorithm design;

[0083] Each node within the cluster performs inertial navigation numerical integration;

[0084] According to the communication strategy, information interaction is carried out within the cluster;

[0085] Each node performs cooperative navigation filtering processing;

[0086] According to the algorithm design, the result after cooperative navigation filtering processing is transmitted to relevant nodes for update.

[0087] Based on the technical solution of the embodiment of the present application, in specific implementation, the situation is as follows:

[0088] S1: Create an SdInertNavInt object for performing inertial navigation numerical integration calculation of a single node;

[0089] S11: The SdInertNavInt object contains the following properties:

[0090] % Initial parameters

[0091] InitCon_LLA_n % Initial position

[0092] InitCon_vN_m % Initial velocity InitCon_CBL % Initial attitude matrix

[0093] % Constants

[0094] Earth_RE % Semi-major axis Earth_f % Flattening Earth_mu % Earth gravitational constant Earth_omegaIE % Earth's angular velocity of rotation % Intermediate parameters

[0095] DS_CycCnt % Cycle count DS_h_n % Altitude DS_CNE_n % Position matrix DS_vN_m % Velocity DS_CB_mL_n % Attitude matrix S12: The SdInertNavInt object contains the following methods: function SdInertNavInt()

[0096] % Constructor

[0097] end function setInitCon()

[0098] % Navigation initialization

[0099] end function resetImpl()

[0100] % Complete the initialization of intermediate parameters end function updateImpl()

[0101] % Perform inertial navigation numerical integration using the velocity increment and angular increment of the current period provided by the gyroscope and accelerometer, and update the intermediate parameters

[0102] end

[0103] function outputImpl()

[0104] % Periodically output intermediate parameters

[0105] end

[0106] function setPos()

[0107] % Reset the position

[0108] end

[0109] function setHeight()

[0110] % Reset the height

[0111] end

[0112] function setVel()

[0113] % Reset the velocity

[0114] end

[0115] function setAttDCM()

[0116] % Reset the attitude matrix

[0117] end

[0118] S2: Create a KalmFilt object for single-node kalman filter calculation;

[0119] S21: The KalmFilt object contains the following properties:

[0120] % Initial parameters

[0121] InitCon_x % Initial value of the state vector

[0122] Initial value of the InitCon_P% state error covariance matrix

[0123] Initial value of the InitCon_F_1% state transition matrix

[0124] Initial value of the InitCon_GPQGPT_1% process noise term

[0125] % Intermediate parameters

[0126] DS_ExtrapCycCnt% Extrapolation cycle count

[0127] DS_UpdCnt % Update cycle count

[0128] DS_x % State vector

[0129] DS_P % State error covariance matrix

[0130] DS_F_1 % State transition matrix

[0131] DS_GPQGPT_1% Process noise term

[0132] DS_K% Filter gain

[0133] S22: The KalmFilt object contains the following methods:

[0134] function KalmFilt()

[0135] % Constructor, initializes the filtering parameters in the form of key-value pairs

[0136] end

[0137] function setInitCon()

[0138] % Initial parameter initialization

[0139] end

[0140] function resetImpl()

[0141] % Completes the initialization of intermediate parameters

[0142] end

[0143] function predict()

[0144] % Calls obj.updateImpl() to complete the extrapolation calculation and calls obj.outputImpl() to output the latest results

[0145] end

[0146] function updateImpl()

[0147] % Complete state estimation and error covariance extrapolation calculation

[0148] end

[0149] function correct()

[0150] % Call obj.correctImpl() to complete the update calculation and call obj.outputImpl() to output the latest result

[0151] end

[0152] function correctImpl()

[0153] % Complete state estimation and error covariance update calculation

[0154] end

[0155] function outputImpl()

[0156] % Periodically output intermediate parameters

[0157] end

[0158] function setState()

[0159] % Reset DS_x, DS_P

[0160] end

[0161] S3: Create a CoopNavNode object to implement the cooperative navigation of its own node, compatible with types such as absolute measurements (position, velocity, pseudorange, pseudorange rate) and relative measurements (relative ranging, relative velocity measurement, relative angle measurement);

[0162] S31: The CoopNavNode object contains the following properties:

[0163] % Initial parameters

[0164] InitCon_LLA_n % Initial position

[0165] InitCon_vN_m % Initial velocity

[0166] InitCon_CBL % Initial attitude matrix

[0167] Initial value of the state vector of InitCon_KF_x%

[0168] Initial value of the state error covariance matrix of InitCon_KF_P%

[0169] % Class object

[0170] SINI(1,1)SdInertNavInt% Inertial numerical integration class object

[0171] KF(1,1)KalmFilt% Kalman filter class object

[0172] S32: The CoopNavNode object contains the following methods:

[0173] function CoopNavNode()

[0174] % Constructor, creating SdInertNavInt and KalmFilt objects

[0175] end

[0176] function resetImpl()

[0177] % Completing the initialization of SdInertNavInt and KalmFilt objects

[0178] end

[0179] function updateImpl()

[0180] % Conducting inertial navigation numerical integration for each node in the CNN (compatible with absolute measurement integrated navigation in forms such as position, velocity, pseudorange, pseudorange rate, and angle measurement)

[0181] end

[0182] function getState_co()

[0183] % Receiving relevant navigation data from cooperative nodes for navigation correction (different data is received depending on the algorithm design)

[0184] end

[0185] function updatemessage()

[0186] % Conducting cooperative navigation Kalman filter calculation based on the algorithm design and using data from cooperative nodes and its own node (compatible with relative measurements in forms such as relative ranging, relative velocity measurement, and relative angle measurement)

[0187] end

[0188] function correct()

[0189] % Use the Kalman filter result (the source and result of the Kalman filter are different for different algorithm designs) to complete the correction of the navigation parameters of its own node

[0190] end

[0191] function outputImpl()

[0192] % Periodically output the relevant data of the SdInertNavInt and KalmFilt objects

[0193] end

[0194] S4: Create a CoopNavCluster object to initialize the nodes within the cluster and formulate the information interaction strategy between nodes, run the cooperative navigation algorithm, and be compatible with master-slave and parallel cooperative navigation.

[0195] S41: The CoopNavCluster object contains the following properties:

[0196] CNN % CoopNavNode class object structure

[0197] KF(1,1) KalmFilt % Centralized filtering class object

[0198] S42: The CoopNavCluster object contains the following methods:

[0199] function CoopNavDssCluster

[0200] % Constructor, initialize CNN as a structure that contains multiple CoopNavNode objects

[0201] end

[0202] function stepthrough()

[0203] % Select centralized filtering or distributed filtering according to the algorithm design

[0204] % I. Centralized filtering

[0205] % 1) Perform inertial navigation numerical integration on each node within the cluster;

[0206] obj.outputImpl();

[0207] obj.updateImpl();

[0208] %2) Each node within the cluster transmits the data to the master node;

[0209] %3) Perform centralized filtering calculation at the master node within the cluster;

[0210] obj.KF.correct();

[0211] %4) Transmit the centralized filtering result back to each node and update.

[0212] % II. Distributed filtering

[0213] %1) Develop a communication strategy according to the algorithm design;

[0214] %2) Each node within the cluster performs inertial navigation numerical integration;

[0215] obj.outputImpl();

[0216] obj.updateImpl();

[0217] %3) According to the communication strategy, perform information interaction within the cluster;

[0218] %4) Each node performs cooperative navigation filtering processing.

[0219] obj.CNN{i,1}.updatemessage();

[0220] %5) According to the algorithm design, transmit the result after cooperative navigation filtering processing to the relevant nodes for update

[0221] end

[0222] function resetImpl()

[0223] % Complete the initialization of each node CoopNavNode in the obj.CNN structure

[0224] end

[0225] function updateImpl()

[0226] % Call the updateImpl() and outputImpl() functions of each node CoopNavNode in the obj.CNN structure to complete the inertial navigation numerical integration calculation of each node and update the output

[0227] end

[0228] function outputImpl()

[0229] Output the results of each node CoopNavNode in the obj.CNN structure with % periodicity

[0230] end

[0231] S5: Externally call the CoopNavCluster.stepthrough() function to run the in-cluster cooperative navigation algorithm

[0232] In the second aspect, see Figure 2 As shown, the embodiment of the present application provides a cooperative navigation device based on MATLAB system objects. The device includes:

[0233] An object creation module, which is used to create an SdInertNavInt object for inertial navigation numerical integration calculation of a single node;

[0234] The object creation module is also used to create a KalmFilt object for kalman filtering calculation of a single node;

[0235] The object creation module is also used to create a CoopNavNode object for realizing the cooperative navigation of its own node;

[0236] The object creation module is also used to create a CoopNavCluster object for initializing the nodes in the cluster and formulating an information interaction strategy between nodes;

[0237] A function call module, which is used to externally call the CoopNavCluster.stepthrough() function to run the in-cluster cooperative navigation algorithm.

[0238] Aiming at the problems of difficult expansion, difficult reuse, and difficult maintenance existing in the traditional process-oriented cooperative navigation algorithm architecture. The embodiment of the present application reduces the coupling degree between modules and facilitates multi-node parallel cooperative navigation calculation. This architecture can be compatible with master-slave cooperative navigation and parallel cooperative navigation, can be compatible with centralized filtering and distributed filtering, and can be compatible with measurement types such as absolute measurement (position, speed, pseudorange, pseudorange rate) and relative measurement (relative ranging, relative speed measurement, relative angle measurement).

[0239] Furthermore, the CoopNavNode object is compatible with absolute measurement and relative measurement.

[0240] Furthermore, the absolute measurement includes position, speed, pseudorange, and pseudorange rate.

[0241] Furthermore, the relative measurement includes relative ranging, relative speed measurement, and relative angle measurement.

[0242] Further, the object creation module is further configured to execute a centralized filtering process, and the centralized filtering process includes the following steps:

[0243] Each node within the cluster performs inertial navigation numerical integration;

[0244] Each node within the cluster transmits data to the master node;

[0245] Centralized filtering calculation is performed at the master node within the cluster;

[0246] The centralized filtering result is transmitted back to each node for update.

[0247] Further, the object creation module is further configured to execute a distributed filtering process, and the distributed filtering process includes the following steps:

[0248] A communication strategy is formulated according to the algorithm design;

[0249] Each node within the cluster performs inertial navigation numerical integration;

[0250] According to the communication strategy, information interaction is performed within the cluster;

[0251] Each node performs cooperative navigation filtering processing;

[0252] According to the algorithm design, the result after cooperative navigation filtering processing is transmitted to relevant nodes for update.

[0253] It should be noted that the cooperative navigation device based on the MATLAB system object mentioned in the second aspect is similar to the technical principle of the cooperative navigation algorithm based on the MATLAB system object mentioned in the first aspect in terms of technical problems, technical means, and technical effects, and will not be elaborated here.

[0254] In the description of the present application, it should be noted that the orientation or positional relationship indicated by terms such as "upper" and "lower" is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present application. Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0255] It should be noted that in this application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

[0256] The above are only specific embodiments of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features claimed herein.

Claims

1. A collaborative navigation method based on MATLAB system objects, characterized in that: The method comprises the following steps: S1: Create a SdInertNavInt object for single-node inertial navigation numerical integration calculation; S2: Create a KalmFilt object for single-node Kalman filter calculation; S3: Create a CoopNavNode object for implementing collaborative navigation of its own node; S4: Create a CoopNavCluster object for initializing nodes in the cluster and formulate an information interaction strategy between nodes; S5: Call CoopNavCluster.stepthrough() function externally to run the intra-cluster collaborative navigation algorithm.

2. The collaborative navigation method based on MATLAB system objects as claimed in claim 1, characterized in that: The CoopNavNode object is compatible with both absolute and relative measurements.

3. The collaborative navigation method based on MATLAB system objects as claimed in claim 2, characterized in that: The absolute measurements include position, velocity, pseudorange, and pseudorange rate.

4. The collaborative navigation method based on MATLAB system objects as claimed in claim 2: The relative measurement includes relative distance measurement, relative speed measurement and relative angle measurement.

5. The collaborative navigation method based on MATLAB system objects as claimed in claim 1, characterized in that: The creation of the CoopNavCluster object for initializing the nodes in the cluster and the formulation of the information interaction strategy between the nodes include a centralized filtering process, which includes the following steps: Each node in the cluster performs numerical integration of inertial navigation; Each node in the cluster transmits data to the master node; Centralized filtering calculation is performed at the master node in the cluster; The centralized filtering results are sent back to each node and updated.

6. The collaborative navigation method based on MATLAB system objects as claimed in claim 1, characterized in that: The creation of a CoopNavCluster object for initializing nodes in the cluster and formulating an information interaction strategy between nodes includes a distributed filtering process, which includes the following steps: Develop communication strategies based on algorithm design; Each node in the cluster performs numerical integration of inertial navigation; According to the communication strategy, information exchange is carried out within the cluster; Each node performs collaborative navigation filtering processing; According to the algorithm design, the results of collaborative navigation filtering processing are transmitted to relevant nodes for updating.

7. A collaborative navigation device based on MATLAB system objects, characterized in that: The device comprises: An object creation module, which is used to create an SdInertNavInt object for performing single-node inertial navigation numerical integration calculation; The object creation module is also used to create a KalmFilt object for performing Kalman filter calculation on a single node; The object creation module is also used to create a CoopNavNode object for implementing collaborative navigation of its own node; The object creation module is also used to create a CoopNavCluster object for initializing nodes in the cluster and formulate an information interaction strategy between nodes; The function call module is used to externally call the CoopNavCluster.stepthrough() function to run the intra-cluster collaborative navigation algorithm.

8. The collaborative navigation device based on MATLAB system object as claimed in claim 7, characterized in that: The CoopNavNode object is compatible with both absolute and relative measurements.

9. The collaborative navigation device based on MATLAB system object as claimed in claim 8, characterized in that: The absolute measurements include position, velocity, pseudorange, and pseudorange rate.

10. The collaborative navigation device based on MATLAB system object as claimed in claim 8, characterized in that: The relative measurement includes relative distance measurement, relative speed measurement and relative angle measurement.

Images