Unmanned cluster single-pilot collaborative navigation method based on relative ranging information

Abstract

The invention discloses an unmanned cluster single-pilot collaborative navigation method based on relative ranging information. The method comprises the following steps: sequentially establishing dead reckoning models of an unmanned cluster collaborative navigation coordinate system; the extended Kalman filtering algorithm model is composed of a state space model and a measurement model, and the extended Kalman filtering algorithm model is an unmanned cluster single-pilot collaborative navigation algorithm based on relative distance measurement information. The relative distance measurement information of an unmanned carrier which is provided with a high-precision navigation device or has absolute position information as an unmanned carrier which is provided with a low-precision navigation device or does not have absolute position information is used as quantity measurement, and Kalman filtering estimation is carried out on the state quantity of the unmanned carrier by using an extended Kalman filtering algorithm model; collaborative navigation position information of the child nodes is obtained; according to the method, the problem that an existing collaborative navigation method still has the defect that the high-precision navigation positioning requirement cannot be met is solved, the navigation precision of collaborative navigation is effectively improved, the operation is simple, the navigation precision is high, and the practicability is good.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative navigation, and in particular to an unmanned cluster single-pilot collaborative navigation method based on relative ranging information. Background Art

[0002] Collaborative navigation is a technology that uses precise relative measurement and navigation information sharing between individuals within a swarm to ensure the smooth completion of collaborative tasks, such as formation stability and formation reconfiguration. This technology effectively improves individual navigation and positioning accuracy and increases the completion rate of collaborative tasks. It is fundamental for unmanned swarms to accomplish complex tasks and holds significant research significance in the navigation field.

[0003] Published patent CN116222583A proposes a fast calculation method for collaborative navigation based on a sliding window. This method solves the problems of large computational complexity, long calculation time, and poor real-time performance of collaborative navigation during the optimization solution of the objective function based on the factor graph model, thereby further improving navigation accuracy and navigation solution efficiency. Published patent CN117739978A provides a multi-AUV parallel collaborative navigation and positioning method and system based on a factor graph. This method solves the problem of multiple AUVs navigating and positioning in an unknown underwater environment using heterogeneous and heterogeneous self-navigation information sources and relative observation information sources between each AUV. Published patent CN114440889A provides a multi-domain cluster collaborative navigation method for the dynamic evolution of real-time areas of interest. This method improves the unoptimized cluster unmanned vehicle positioning algorithm to achieve better positioning accuracy. However, these existing collaborative navigation methods still have the disadvantage of not being able to meet the requirements of high-precision navigation and positioning. Therefore, it is necessary to further optimize the existing collaborative navigation methods. Summary of the Invention

[0004] The purpose of the present invention is to provide an unmanned cluster single pilot collaborative navigation method based on relative ranging information to solve the problem that the existing collaborative navigation method still has the insufficiency of meeting the requirements of high-precision navigation and positioning.

[0005] To this end, the technical solution of the present invention is as follows:

[0006] An unmanned swarm single-pilot collaborative navigation method based on relative ranging information, comprising the following steps:

[0007] S1. Based on the kinematic information of the unmanned vehicle, a dead reckoning model for the vehicle in the unmanned swarm collaborative navigation system is constructed to obtain the position information of the unmanned vehicle at each time point;

[0008] S2. Based on the dead reckoning model constructed in step S1, a kinematic model of the cooperative navigation system capable of describing the motion state of the unmanned vehicle on a two-dimensional plane is established; further, based on the extended Kalman filter theory, a state space model of the unmanned vehicle on a two-dimensional plane is established; based on the relative distance measurement between the unmanned vehicle and its single pilot unmanned vehicle as an observation quantity, a measurement model of the unmanned vehicle on a two-dimensional plane is established; the state space model and the measurement model are combined to obtain an extended Kalman filter algorithm model of the unmanned swarm single pilot cooperative navigation coordinate system;

[0009] S3. In the unmanned cluster collaborative navigation coordinate system, unmanned carriers equipped with high-precision navigation devices or with absolute position information are used as main nodes, and unmanned carriers equipped with low-precision navigation devices or without absolute position information are used as child nodes; a main node is configured for each child node, and the relative distance information between the main node and the child node is used as the quantity measurement; the extended Kalman filter algorithm model constructed in step S2 is used to perform Kalman filter estimation on the state quantity of the child node, and the collaborative navigation position information of the child node is obtained by solving it.

[0010] Furthermore, in step S1, the steps for constructing the dead reckoning model of the carrier are:

[0011] S101, obtaining kinematic information of the carrier, including initial kinematic information obtained before the carrier moves, and real-time kinematic information continuously obtained during the movement of the carrier;

[0012] S102. Perform dead reckoning based on the carrier's kinematic information to obtain the carrier's position information at each time point. The specific dead reckoning formula is:

[0013]

[0014] Where x0 is the x-axis position information of the carrier at the initial moment, y0 is the y-axis position information of the carrier at the initial moment, θ0 is the heading angle of the carrier at the initial moment, and x k For the carrier in t k The x-axis position information at the moment, y k For the carrier in t k The y-axis position information at the moment, θ k For the carrier in t k The heading angle at the moment, r fk For the carrier t k The forward movement distance at the moment, r lk For the carrier t k Normal motion distance at time, ω k For the carrier t k Angular velocity of motion at the moment;

[0015] S103, using the left moment of the integral interval, r k After discretization, the dead reckoning model of the unmanned swarm collaborative navigation system is obtained, which is expressed as follows:

[0016]

[0017] Where, v fk For the carrier in t k The forward speed at time v lk For the carrier in t k Normal velocity at time, a fk For the carrier t k Forward acceleration at moment a lk For the carrier t k is the normal acceleration at the moment, and Δt is the interval time.

[0018] Furthermore, in step S101 , the kinematic information of the carrier is measured by an inertial measurement unit installed inside.

[0019] Furthermore, in step S2, the kinematic model of the collaborative navigation system is expressed as:

[0020]

[0021] Where x b is the horizontal coordinate of the unmanned vehicle in the unmanned cluster in the vehicle coordinate system on the two-dimensional plane, y b is the vertical coordinate of the unmanned vehicle in the unmanned cluster in the vehicle coordinate system on the two-dimensional plane, x n is the horizontal coordinate of the unmanned vehicle in the unmanned cluster in the navigation coordinate system on the two-dimensional plane, y n is the ordinate of the unmanned carrier in the unmanned cluster in the navigation coordinate system on the two-dimensional plane, and θ is the heading angle of the unmanned carrier in the unmanned cluster in the navigation coordinate system on the two-dimensional plane.

[0022] Furthermore, in step S2, the steps of constructing the state space model are:

[0023] i) Establish a state space model of the unmanned vehicle on a two-dimensional plane, which is expressed as follows:

[0024]

[0025] Where x is the horizontal coordinate of the unmanned vehicle in the navigation coordinate system on the two-dimensional plane, and y is the horizontal coordinate of the unmanned vehicle in the navigation coordinate system on the two-dimensional plane; is the x-axis speed of the unmanned vehicle in the navigation coordinate system on the two-dimensional plane, v is the y-axis velocity of the unmanned vehicle in the navigation coordinate system on the two-dimensional plane; xis the forward velocity of the unmanned vehicle along the moving direction of the unmanned vehicle on the two-dimensional plane, v y is the normal velocity of the unmanned carrier in the two-dimensional plane perpendicular to the direction of motion of the unmanned carrier; θ is the heading angle of the unmanned carrier in the navigation coordinate system on the two-dimensional plane, is the heading angular rate of the unmanned vehicle on the two-dimensional plane; a x is the x-axis acceleration output by the x-axis accelerometer in the inertial measurement unit installed on the unmanned carrier, a y is the y-axis acceleration output by the y-axis accelerometer in the inertial measurement unit installed on the unmanned carrier, ω is the angular velocity output by the heading gyroscope in the inertial measurement unit installed on the unmanned carrier, and ε x is the measurement noise of the x-axis accelerometer in the inertial measurement unit installed on the unmanned vehicle, ε y is the measurement noise of the y-axis accelerometer in the inertial measurement unit installed on the unmanned vehicle, ε ω The measured noise of the heading gyroscope in the inertial measurement unit installed on the unmanned vehicle;

[0026] ii) Define the 5-dimensional state vector as the state quantity X of the extended Kalman filter algorithm model, and its expression is:

[0027] X=[xyv x v y θ] T ;

[0028] iii) Based on the extended Kalman filter theory, the state space model of the unmanned vehicle as a child node in the cooperative navigation coordinate system is defined as follows:

[0029]

[0030] Where, is the state quantity of the extended Kalman filter algorithm model of the child node; F is the state transfer matrix, and its expression is: G is the system noise driving matrix, which is expressed as: W is the system noise matrix, W=[ε x ε y ε ω ] T ; t is time.

[0031] Furthermore, in step S2, the measurement model is constructed as follows:

[0032] i) Single pilot mode is defined as: the unmanned vehicle only measures the relative distance between itself and another unmanned vehicle, and uses this as the observation value;

[0033] ii) Establish a measurement model for the single pilot cooperative navigation algorithm model to obtain measurement information in the single pilot mode. The expression is:

[0034] Z k =r 1,k +v k ,

[0035] Where r 1,k For t k The relative distance measurement between the child node and the main node at the moment, For t k The position information of the child node S in the absolute reference system on the two-dimensional plane at the moment; For t k The position information of the master node M in the absolute reference system on the two-dimensional plane at the moment; v k For child nodes at t k Speed ​​at the moment;

[0036] iii) Then the observation equation in single pilot mode is obtained, which is expressed as:

[0037] Z k =H k X+V,

[0038] Where H is the observation matrix, V is the measurement noise.

[0039] Compared with the existing technology, the unmanned cluster single-pilot cooperative navigation method based on relative ranging information successively establishes a dead reckoning model of the unmanned cluster cooperative navigation coordinate system, and an extended Kalman filter algorithm model of the unmanned cluster single-pilot cooperative navigation algorithm based on relative ranging information composed of a state space model and a measurement model, and then uses the relative ranging information of an unmanned carrier equipped with a high-precision navigation device or with absolute position information as a quantity measurement, and uses the extended Kalman filter algorithm model to perform Kalman filter estimation on the state quantity of the latter to obtain the cooperative navigation position information of the child node; this method solves the problem that the existing cooperative navigation method still cannot meet the needs of high-precision navigation and positioning, effectively improves the navigation accuracy of cooperative navigation, and has simple operation, high navigation accuracy and good practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of the unmanned swarm single-pilot collaborative navigation method based on relative ranging information of the present invention;

[0041] Figure 2This is a comparison diagram of the actual motion trajectory of the unmanned vehicle S as a child node, the motion trajectory without collaborative navigation, and the motion trajectory with collaborative navigation using the master node M in the simulation experiment of an embodiment of the present invention;

[0042] Figure 3 This is a comparison chart of the position errors of the unmanned vehicle S as a child node in the simulation experiment of an embodiment of the present invention without collaborative navigation and using the master node M for collaborative navigation. DETAILED DESCRIPTION

[0043] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the following embodiments are by no means intended to limit the present invention in any way.

[0044] See also Figure 1 In this embodiment, the carrier is an unmanned vehicle as an example, and the specific implementation steps of the unmanned cluster single pilot cooperative navigation method based on relative ranging information are described as follows.

[0045] S1. Construct a dead reckoning model for the carrier within the unmanned swarm collaborative navigation coordinate system.

[0046] S101. Obtain kinematic information of the unmanned vehicle, including initial kinematic information obtained before the unmanned vehicle moves, and real-time kinematic information continuously obtained during the movement of the unmanned vehicle.

[0047] The initial kinematic information includes: the x-axis position information x0 of the unmanned vehicle at the initial moment, the y-axis position information y0 of the unmanned vehicle at the initial moment, and the heading angle θ0 of the unmanned vehicle at the initial moment.

[0048] Real-time kinematic information includes: unmanned vehicle t k Angular velocity ω at the moment k 、Unmanned vehicle t k The forward acceleration a at time fk , and unmanned vehicles k Normal acceleration a at time lk .

[0049] The above two parts of kinematic information are measured and provided by the inertial measurement unit installed inside the unmanned vehicle. Then, according to the initial kinematic information and real-time kinematic information of the unmanned vehicle, the unmanned vehicle can be further determined at t k The x-axis position information x at the moment k 、Unmanned vehicles in t k The y-axis position information y at the moment k And unmanned vehicles in t k The heading angle θ at the moment k .

[0050] S102. Perform dead reckoning based on the kinematic information of the unmanned vehicle to obtain the position information of the unmanned vehicle at each time point. The specific dead reckoning formula is:

[0051]

[0052] Where x0 is the x-axis position information of the unmanned vehicle at the initial moment, y0 is the y-axis position information of the unmanned vehicle at the initial moment, θ0 is the heading angle of the unmanned vehicle at the initial moment, and x k For unmanned vehicles in t k The x-axis position information at the moment, y k For unmanned vehicles in t k The y-axis position information at the moment, θ k For unmanned vehicles in t k The heading angle at the moment, r fk For unmanned vehicles k The forward movement distance at the moment, r lk For unmanned vehicles k Normal motion distance at time, ω k For unmanned vehicles k Angular velocity of motion at the moment;

[0053] S103, because the integration time of the car is very short during the movement, the unmanned car automatically k-1 Time to t k The difference in the changes of the parameters of the various directions of motion at the moment is very small; for the convenience of calculation, the left moment of the integral interval is uniformly used, that is, r k Perform discretization processing to obtain the dead reckoning model of the unmanned swarm collaborative navigation coordinate system;

[0054] Specifically, the dead reckoning model of the unmanned swarm collaborative navigation coordinate system is expressed as:

[0055]

[0056]

[0057] Where, v fk For unmanned vehicles in t k The forward speed at time v lk For unmanned vehicles in t k Normal velocity at time, a fk For unmanned vehicles k Forward acceleration at moment a lk For unmanned vehicles k is the normal acceleration at the moment, and Δt is the interval time.

[0058] S2. Construct an extended Kalman filter algorithm model for the unmanned cluster single-pilot cooperative navigation algorithm based on relative ranging information, which is specifically composed of a state space model and a measurement model.

[0059] The specific implementation steps of step S2 are described as follows.

[0060] S201. Construct a kinematic model of the collaborative navigation coordinate system.

[0061] In order to accurately describe the motion problem of the unmanned vehicle on the two-dimensional plane, the kinematic model of the cooperative navigation coordinate system in the unmanned cluster cooperative navigation algorithm is established based on the dead reckoning model of the unmanned cluster cooperative navigation coordinate system constructed in step S1. Its expression is:

[0062]

[0063] Where x b is the horizontal coordinate of the unmanned vehicle in the unmanned cluster in the carrier coordinate system on the two-dimensional plane, y b is the vertical coordinate of the unmanned vehicle in the unmanned cluster in the carrier coordinate system on the two-dimensional plane, x n is the horizontal coordinate of the navigation coordinate system of the unmanned vehicle in the unmanned cluster on the two-dimensional plane, y n is the vertical coordinate of the unmanned vehicle in the unmanned cluster in the navigation coordinate system on the two-dimensional plane, and θ is the heading angle of the unmanned vehicle in the unmanned cluster in the navigation coordinate system on the two-dimensional plane.

[0064] S202. Construct a state space model of the unmanned swarm single pilot collaborative navigation coordinate system.

[0065] The unmanned swarm single-pilot cooperative navigation algorithm problem based on relative ranging information belongs to the multi-sensor information fusion target positioning and tracking problem. Selecting the optimal measurement model from the measurement information is of great significance to improving the positioning accuracy of the unmanned vehicle; and when the amount of observation data is limited, a good state space model can reduce the computational complexity and complexity of the motion model. Therefore, it is necessary to construct a sub-node state space model of the unmanned swarm cooperative navigation coordinate system.

[0066] According to the kinematic model of the cooperative navigation coordinate system constructed in step S201, a state space model of the unmanned vehicle on a two-dimensional plane is established, and its expression is:

[0067]

[0068] Where x is the horizontal coordinate of the unmanned vehicle in the navigation coordinate system on the two-dimensional plane, and y is the horizontal coordinate of the unmanned vehicle in the navigation coordinate system on the two-dimensional plane, that is, x in step S201 n and y n ; is the x-axis speed of the unmanned vehicle in the navigation coordinate system on the two-dimensional plane, v is the y-axis speed of the unmanned vehicle in the navigation coordinate system on the two-dimensional plane; x is the forward velocity of the unmanned vehicle along the moving direction of the unmanned vehicle on the two-dimensional plane, v y is the normal velocity of the unmanned vehicle in the two-dimensional plane perpendicular to the direction of motion of the unmanned vehicle; θ is the heading angle of the unmanned vehicle in the navigation coordinate system on the two-dimensional plane, is the angular velocity of the unmanned vehicle on the two-dimensional plane; a x is the x-axis acceleration output by the x-axis accelerometer in the inertial measurement unit installed on the unmanned vehicle, a y is the y-axis acceleration output by the y-axis accelerometer in the inertial measurement unit installed on the unmanned vehicle, ω is the angular velocity output by the heading gyroscope in the inertial measurement unit installed on the unmanned vehicle, and ε x is the measurement noise of the x-axis accelerometer in the inertial measurement unit installed on the unmanned vehicle, ε y is the measurement noise of the y-axis accelerometer in the inertial measurement unit installed on the unmanned vehicle, ε ω This is the measurement noise of the heading gyroscope in the inertial measurement unit installed on the unmanned vehicle.

[0069] Since the inertial measurement device has been strictly calibrated under normal use, the scale coefficient calibration error and non-orthogonality error of the gyroscope and accelerometer are not considered, and the gyroscope drift error is a random constant offset.

[0070] Based on this, the 5-dimensional state vector is defined as the state quantity X of the extended Kalman filter algorithm model, and its expression is:

[0071] X=[xyv x v y θ] T ,

[0072] Where x is the horizontal coordinate of the unmanned vehicle in the navigation coordinate system on the two-dimensional plane, y is the horizontal coordinate of the unmanned vehicle in the navigation coordinate system on the two-dimensional plane, and v is x is the forward speed of the unmanned vehicle on the two-dimensional plane, v y is the normal velocity of the unmanned vehicle on the two-dimensional plane, and θ is the heading angle of the unmanned vehicle in the navigation coordinate system on the two-dimensional plane;

[0073] Furthermore, according to the extended Kalman filter theory, the state space model of the unmanned vehicle as a child node in the cooperative navigation coordinate system is defined, and its expression is:

[0074]

[0075] Where, is the state quantity of the extended Kalman filter algorithm model of the child node;

[0076] F is the state transfer matrix, and its expression is: t is time;

[0077] G is the system noise driving matrix, which is expressed as: t is time;

[0078] W is the system noise matrix, W=[ε x ε y ε ω ] T .

[0079] S203. Construct a measurement model for the unmanned swarm single-pilot collaborative navigation coordinate system.

[0080] In this step, the measurement equation of the extended Kalman filter is constructed using the relative distance measurement information between the master node and the slave node. Then, based on the single pilot mode, the relative distance measurement is performed between the child nodes of the unmanned cluster and only one master node, and the relative distance measurement is used as the observation quantity to construct the measurement model of the single pilot cooperative navigation algorithm model to obtain the measurement information Z under the single pilot mode. k , whose expression is:

[0081] Z k =r 1,k +v k ,

[0082] Where r 1,k For t k The relative distance measurement between the child node and the main node at the moment, v k For child nodes at t k The measurement noise at the time;

[0083] Among them, the relative distance between the child node and the main node is measured as r 1,k The expression is:

[0084]

[0085] Where, For t k The position information of the child node S in the absolute reference system on the two-dimensional plane at the moment; For t k The position information of the master node M in the absolute reference system on the two-dimensional plane at the moment.

[0086] Then, the observation equation in single pilot mode is obtained, which is expressed as follows:

[0087] Zk =H k X+V,

[0088] Where V is the measurement noise, H is the observation matrix, and H k Corresponding to t k moment observation matrix;

[0089] Among them, the expression of the observation matrix H is:

[0090]

[0091] S204 , by combining the state space model constructed in step S202 and the measurement model constructed in step S203 , an extended Kalman filter algorithm model of the unmanned swarm single pilot cooperative navigation coordinate system is constructed.

[0092] S3. Design an unmanned cluster single-pilot collaborative navigation algorithm based on relative ranging information.

[0093] In the unmanned cluster collaborative navigation coordinate system, a carrier equipped with a high-precision navigation device (or with absolute position information) is used as the main node, and correspondingly, a carrier equipped with a low-precision navigation device (or without absolute position information) is used as the child node; in the single-pilot collaborative navigation process of the unmanned cluster, a master node is configured for each child node, and the relative distance information between the master node and the child node is used as the quantity measurement, and the 5-dimensional state quantity of the child node is Kalman filtered and estimated using the extended Kalman filter algorithm model established in step S2, so as to obtain the collaborative navigation position information of the child node.

[0094] In order to verify the effectiveness of the method proposed in this invention, a simulation experiment is carried out using an unmanned vehicle equipped with a high-precision navigation device as the main node M and an unmanned vehicle equipped with a low-precision navigation device as the child node S. In this simulation experiment, the node error parameter settings of the unmanned cluster vehicle in the single-pilot collaborative navigation algorithm simulation experiment are shown in Table 1 below.

[0095] Table 1: Node error parameter settings in simulation experiments

[0096]

[0097]

[0098] It should be noted that in the actual navigation process, the unmanned vehicle may lose data during the communication process due to obstacles, communication data attenuation, etc. Therefore, in this simulation experiment, the following assumptions are made: (1) All unmanned vehicle nodes in the unmanned cluster can move in an obstacle-free environment; (2) The two unmanned vehicles are always within the perception range, that is, the two unmanned vehicles can perceive each other.

[0099] In the simulation experiment, the initial coordinates of the unmanned vehicle M, which serves as the main node, are X-coordinate: -300m, Y-coordinate: 0m, and heading angle: 98°; the initial coordinates of the unmanned vehicle S, which serves as the child node, are X-coordinate: 0m, Y-coordinate: 0m, and heading angle: 90°. The driving time of the main node M and the child node S is 1800s. The real-time motion information of the unmanned vehicles M and S during the movement process is obtained by actual measurement and calculation using the inertial measurement units installed on the two vehicles.

[0100] like Figure 2 The figure shows the comparison of the actual motion trajectory of the unmanned vehicle S as a child node, the motion trajectory without cooperative navigation, and the motion trajectory using cooperative navigation of the master node M. Figure 3 The figure shows the position error comparison between the child node without cooperative navigation and the cooperative navigation with the master node M.

[0101] As can be seen from the above two figures, the accumulated error of the unmanned vehicle S as a child node using the conventional dead reckoning algorithm gradually increases over time and shows a divergent trend at a relatively fast rate. However, after using the unmanned cluster single-pilot cooperative navigation algorithm proposed in the present invention, the positioning error of the unmanned vehicle S as a child node is reduced from 31.7m to 16.5m. Compared with the position error caused by dead reckoning, the divergence trend is significantly reduced. It can be seen that the method of the present invention can effectively suppress the positioning error and improve the positioning accuracy of the child node. The simulation results verify the effectiveness of the single-pilot cooperative navigation algorithm. In addition, the above comparison results also show that the method of the present invention can also correct the accumulated error of the child node caused by dead reckoning, further demonstrating the effectiveness of the method proposed in the present invention.

[0102] In summary, the unmanned cluster single-pilot cooperative navigation method based on relative ranging information of the present invention solves the problem that the existing cooperative navigation method still cannot meet the needs of high-precision navigation and positioning, effectively improves the navigation accuracy of cooperative navigation, and has simple operation, high navigation accuracy and good practicality.

[0103] Portions of the present invention not disclosed in detail are known in the art. Although the above description of illustrative embodiments of the present invention is intended to facilitate understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the specific embodiments. As long as various modifications are obvious to those skilled in the art within the spirit and scope of the present invention as defined and determined by the appended claims, all inventions and creations utilizing the concepts of the present invention are protected.

Claims

1. An unmanned swarm single-pilot collaborative navigation method based on relative ranging information, characterized in that: The steps are: S1. Based on the kinematic information of the unmanned vehicle, a dead reckoning model for the vehicle in the unmanned swarm collaborative navigation system is constructed to obtain the position information of the unmanned vehicle at each time point; S2. Based on the dead reckoning model constructed in step S1, a kinematic model of the cooperative navigation system capable of describing the motion state of the unmanned vehicle on a two-dimensional plane is established; further, based on the extended Kalman filter theory, a state space model of the unmanned vehicle on a two-dimensional plane is established; based on the relative distance measurement between the unmanned vehicle and its single pilot unmanned vehicle as an observation quantity, a measurement model of the unmanned vehicle on a two-dimensional plane is established; the state space model and the measurement model are combined to obtain an extended Kalman filter algorithm model of the unmanned swarm single pilot cooperative navigation coordinate system; S3. In the unmanned cluster collaborative navigation coordinate system, an unmanned vehicle equipped with a high-precision navigation device or with absolute position information is used as the master node, and an unmanned vehicle equipped with a low-precision navigation device or without absolute position information is used as the child node; A master node is configured for each child node to utilize the relative distance information between the master node and the child node as the quantity measurement; the extended Kalman filter algorithm model constructed in step S2 is used to perform Kalman filter estimation on the state quantity of the child node to obtain the collaborative navigation position information of the child node.

2. The unmanned swarm single-pilot collaborative navigation method based on relative ranging information according to claim 1, characterized in that: In step S1, the steps for constructing the dead reckoning model of the carrier are: S101, obtaining kinematic information of the carrier, including initial kinematic information obtained before the carrier moves, and real-time kinematic information continuously obtained during the movement of the carrier; S102. Perform dead reckoning based on the carrier's kinematic information to obtain the carrier's position information at each time point. The specific dead reckoning formula is: Where x0 is the x-axis position information of the carrier at the initial moment, y0 is the y-axis position information of the carrier at the initial moment, θ0 is the heading angle of the carrier at the initial moment, and x k For the carrier in t k The x-axis position information at the moment, y k For the carrier in t k The y-axis position information at the moment, θ k For the carrier in t k The heading angle at the moment, r fk For the carrier t k The forward movement distance at the moment, r lk For the carrier t k Normal motion distance at time, ω k For the carrier t k Angular velocity of motion at the moment; S103, using the left moment of the integral interval, r k After discretization, the dead reckoning model of the unmanned swarm collaborative navigation system is obtained, which is expressed as follows: Where, v fk For the carrier in t k The forward speed at time v lk For the carrier in t k Normal velocity at time, a fk For the carrier t k Forward acceleration at moment a lk For the carrier t k is the normal acceleration at the moment, and Δt is the interval time.

3. The unmanned swarm single pilot collaborative navigation method based on relative ranging information according to claim 2, characterized in that: In step S101 , the kinematic information of the carrier is measured by an inertial measurement unit installed inside.

4. The unmanned swarm single-pilot collaborative navigation method based on relative ranging information according to claim 1, characterized in that: In step S2, the kinematic model of the collaborative navigation system is expressed as: Where x b is the horizontal coordinate of the unmanned vehicle in the unmanned cluster in the vehicle coordinate system on the two-dimensional plane, y b is the vertical coordinate of the unmanned vehicle in the unmanned cluster in the vehicle coordinate system on the two-dimensional plane, x n is the horizontal coordinate of the unmanned vehicle in the unmanned cluster in the navigation coordinate system on the two-dimensional plane, y n is the ordinate of the unmanned carrier in the unmanned cluster in the navigation coordinate system on the two-dimensional plane, and θ is the heading angle of the unmanned carrier in the unmanned cluster in the navigation coordinate system on the two-dimensional plane.

5. The unmanned swarm single pilot collaborative navigation method based on relative ranging information according to claim 4 is characterized in that: In step S2, the steps of constructing the state space model are: i) Establish a state space model of the unmanned vehicle on a two-dimensional plane, which is expressed as follows: Where x is the horizontal coordinate of the unmanned vehicle in the navigation coordinate system on the two-dimensional plane, and y is the horizontal coordinate of the unmanned vehicle in the navigation coordinate system on the two-dimensional plane; is the x-axis speed of the unmanned vehicle in the navigation coordinate system on the two-dimensional plane, v is the y-axis velocity of the unmanned vehicle in the navigation coordinate system on the two-dimensional plane; x is the forward velocity of the unmanned vehicle along the moving direction of the unmanned vehicle on the two-dimensional plane, v y is the normal velocity of the unmanned carrier in the two-dimensional plane perpendicular to the direction of motion of the unmanned carrier; θ is the heading angle of the unmanned carrier in the navigation coordinate system on the two-dimensional plane, is the heading angular rate of the unmanned vehicle on the two-dimensional plane; a x is the x-axis acceleration output by the x-axis accelerometer in the inertial measurement unit installed on the unmanned carrier, a y is the y-axis acceleration output by the y-axis accelerometer in the inertial measurement unit installed on the unmanned carrier, ω is the angular velocity output by the heading gyroscope in the inertial measurement unit installed on the unmanned carrier, and ε x is the measurement noise of the x-axis accelerometer in the inertial measurement unit installed on the unmanned vehicle, ε y is the measurement noise of the y-axis accelerometer in the inertial measurement unit installed on the unmanned vehicle, ε ω The measured noise of the heading gyroscope in the inertial measurement unit installed on the unmanned vehicle; ii) Define the 5-dimensional state vector as the state quantity X of the extended Kalman filter algorithm model, and its expression is: X=[x y v x v y θ] T ; iii) Based on the extended Kalman filter theory, the state space model of the unmanned vehicle as a child node in the cooperative navigation coordinate system is defined as follows: Where, is the state quantity of the extended Kalman filter algorithm model of the child node; F is the state transfer matrix, and its expression is: G is the system noise driving matrix, which is expressed as: W is the system noise matrix, W=[ε x ε y ε ω ] T ; t is time.

6. The unmanned swarm single-pilot collaborative navigation method based on relative ranging information according to claim 4, characterized in that: In step S2, the measurement model is constructed as follows: i) Single pilot mode is defined as: the unmanned vehicle only measures the relative distance between itself and another unmanned vehicle, and uses this as the observation value; ii) Establish a measurement model for the single pilot cooperative navigation algorithm model to obtain measurement information in the single pilot mode. The expression is: With k =r 1,k +v k , Where r 1,k For t k The relative distance measurement between the child node and the main node at the moment, For t k The position information of the child node S in the absolute reference system on the two-dimensional plane at the moment; For t k The position information of the master node M in the absolute reference system on the two-dimensional plane at the moment; v k For child nodes at t k The measurement noise at the time; iii) Then the observation equation in single pilot mode is obtained, which is expressed as: Z k =H k X+V, Where H is the observation matrix, V is the measurement noise.

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