Multi-unmanned-system cooperative positioning method and device based on mixed entropy factor graph

Abstract

The invention provides a multi-unmanned-system cooperative positioning method and device based on a mixed entropy factor graph, and the technical key points of the method comprise the steps: S1, abstracting the position information of each unmanned system and the relative distance measurement information between each master unmanned system and each slave unmanned system into factor nodes, building a factor graph model of multi-unmanned-system cooperative positioning, obtaining a constraint and inference result of the relative position relation of each master unmanned system and each slave unmanned system; s2, a cost function based on Gaussian kernel function convex combination mixed entropy of different kernel bandwidths; and S3, using a mixed entropy cost function to receive the observation distance and position information of each main unmanned system at a certain moment at the target slave unmanned system, using a function node to maximize the correlation entropy as a target, performing optimal position estimation on the target slave unmanned system through a sum-product algorithm, and finally obtaining the position coordinates of the target slave unmanned system. And the positioning precision and the anti-interference capability of the unmanned system under the non-Gaussian noise are improved.

Description

Technical Field

[0001] The present application relates to the technical field of collaborative positioning learning of multiple unmanned systems, and in particular to a method and device for collaborative positioning of multiple unmanned systems based on a hybrid entropy factor graph. Background Art

[0002] Unmanned systems play a vital role in the military, agriculture, transportation, and other fields. In complex environments, multiple unmanned systems, through intercommunication and collaboration, effectively address the challenges of a single unmanned system's limited operational coverage and difficulties in perception and environmental modeling. Accurate positioning information is crucial for these systems to complete their operations. In a collaborative positioning system, some unmanned systems are equipped with high-precision inertial navigation systems as the master unmanned system, while the remaining slave unmanned systems utilize low-cost, low-precision navigation and positioning systems. By communicating and measuring with the master unmanned system, the slave unmanned systems utilize collaborative positioning algorithms to correct navigation information, thereby reducing costs and improving overall positioning accuracy.

[0003] In the collaborative localization of multiple unmanned systems, environmental factors such as multipath propagation and varying medium properties, as well as sensor factors such as faults and resolution limitations, can cause measurement noise to exhibit a non-Gaussian distribution. Consequently, directly applying collaborative localization algorithms based on the Gaussian noise assumption to complex noisy environments can lead to divergence, thereby reducing positioning accuracy. In summary, overcoming the impact of non-Gaussian noise on the collaborative localization of multiple unmanned systems is a major challenge in achieving high-precision collaborative localization of multiple unmanned systems in complex environments.

[0004] Existing methods primarily optimize factor graph frameworks and fail to fully consider situations where system noise deviates from the Gaussian assumption. Alternatively, some existing methods consider the robustness issues caused by non-Gaussian measurement noise in underwater acoustic communications and introduce correlation entropy as a local similarity metric to address situations where system noise has non-zero mean and is non-Gaussian. However, these methods employ a single kernel bandwidth, making them incapable of adapting to complex noise variations. Summary of the Invention

[0005] Each exemplary embodiment of the present application provides a multi-unmanned system collaborative positioning method and device based on a hybrid entropy factor graph, so as to have at least the technical effect of significantly improving the robustness and accuracy of system state estimation under non-Gaussian noise conditions.

[0006] According to one aspect of the present application, each exemplary embodiment of the present application provides a method for collaborative positioning of multiple unmanned systems based on a hybrid entropy factor graph, the method comprising the following steps:

[0007] S1: By abstracting the position information of each unmanned system and the relative distance measurement information between each master unmanned system and each slave unmanned system into factor nodes, a factor graph model for multi-unmanned system collaborative positioning is established to obtain the constraints and inference results of the relative position relationship between each master unmanned system and each slave unmanned system;

[0008] S2, based on the impact of non-Gaussian noise environment on the performance of the collaborative localization algorithm, defines a cost function based on the mixed entropy of the convex combination of Gaussian kernel functions with different kernel bandwidths;

[0009] S3, using the obtained hybrid entropy cost function, when the target slave unmanned system receives the observation distance and position information of each main unmanned system at a certain moment, the function node takes maximizing the relevant entropy as the goal, and uses the sum-product algorithm to optimally estimate the position of the target slave unmanned system, and finally obtains the position coordinates of the target slave unmanned system.

[0010] According to another aspect of the present invention, a multi-unmanned system collaborative positioning device based on a hybrid entropy factor graph is also claimed, which applies any of the above-mentioned collaborative positioning methods, including:

[0011] A factor graph model building module is configured to abstract the position information of each unmanned system and the relative distance measurement information between each master unmanned system and each slave unmanned system into factor nodes, thereby building a factor graph model for the collaborative positioning of multiple unmanned systems and obtaining the constraints and inference results of the relative position relationship between each master unmanned system and each slave unmanned system;

[0012] A mixed entropy function calculation module is configured to define a cost function based on the mixed entropy of convex combinations of Gaussian kernel functions with different kernel bandwidths based on the impact of non-Gaussian noise environments on the performance of the collaborative localization algorithm;

[0013] The optimal estimation module is configured to use the obtained hybrid entropy cost function. When the target slave unmanned system receives the observation distance and position information of each master unmanned system at a certain moment, the function node takes maximizing the relevant entropy as the goal, and performs the optimal estimation of the position of the target slave unmanned system through the sum-product algorithm, and finally obtains the position coordinates of the target slave unmanned system.

[0014] This application has the following beneficial effects:

[0015] The present invention takes into account the impact of non-Gaussian noise on the collaborative positioning of multiple unmanned systems, abstracts the position information of each unmanned system and the relative distance measurement information between the master and slave unmanned systems into factor nodes. During the data fusion process, the function nodes aim to maximize the correlation entropy to achieve accurate state estimation of the target slave unmanned system.

[0016] To address the performance degradation of traditional collaborative localization algorithms caused by non-Gaussian noise, this paper proposes a collaborative localization algorithm based on a hybrid entropy robust factor graph. Specifically, a convex combination of two Gaussian kernel functions with different kernel bandwidths is employed in the factor graph-based collaborative localization model. Compared to algorithms with a single kernel bandwidth, this algorithm significantly improves the robustness and accuracy of system state estimation under non-Gaussian noise conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 A schematic diagram of a flow chart of a method according to an embodiment of the present invention;

[0019] Figure 2 Graph 1 is a factor graph-based collaborative positioning model diagram according to an embodiment of the present invention;

[0020] Figure 3 This is a real trajectory diagram of the master-slave unmanned system according to an embodiment of the present invention;

[0021] Figure 4 A parameter selection diagram for a multi-unmanned system collaborative positioning method based on a hybrid entropy factor graph according to an embodiment of the present invention;

[0022] Figure 5 This is a positioning error diagram from an unmanned system under non-Gaussian noise according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the preferred embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0024] Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of this application.

[0025] like Figures 1 to 3 As shown, the present invention provides a multi-unmanned system collaborative positioning method based on a hybrid entropy factor graph, the method comprising the following steps:

[0026] S1: By abstracting the position information of each unmanned system and the relative distance measurement information between each master unmanned system and each slave unmanned system into factor nodes, a factor graph model for multi-unmanned system collaborative positioning is established to obtain the constraints and inference results of the relative position relationship between each master unmanned system and each slave unmanned system;

[0027] S2, based on the impact of non-Gaussian noise environment on the performance of the collaborative localization algorithm, defines a cost function based on the mixed entropy of the convex combination of Gaussian kernel functions with different kernel bandwidths;

[0028] S3, using the obtained hybrid entropy cost function, when the target slave unmanned system receives the observation distance and position information of each main unmanned system at a certain moment, the function node takes maximizing the relevant entropy as the goal, and uses the sum-product algorithm to optimally estimate the position of the target slave unmanned system, and finally obtains the position coordinates of the target slave unmanned system.

[0029] It should be noted that there can be multiple main unmanned aerial vehicle systems. In this patent, two main unmanned aerial vehicle systems are used as an example for illustration, but this does not limit the scope of protection of this application.

[0030] Among them, the factor graph model of multi-unmanned system collaborative positioning is specifically:

[0031] Output:

[0032] Based on the factor graph model, a subsequent cost function can be designed to solve the posterior estimation of the output slave unmanned system coordinates and the relative position estimation results between the master and slave unmanned systems.

[0033] enter:

[0034] The variable nodes and constraint relationships defined in the factor graph model can be used for the hybrid entropy cost function in step S2;

[0035] The prior estimates and observation information from the unmanned system coordinates in the factor graph model can be directly used in the sum-product algorithm in step S3 as the initial state and measurement input.

[0036] Among them, the cost function is:

[0037] The robustness of the algorithm to non-Gaussian noise is enhanced by mixing a convex combination of two Gaussian kernel functions with different kernel bandwidths.

[0038] The relationship between the cost function and the previous and next steps:

[0039] Based on the state transfer function in the factor graph model established by S1 and the constraint relationship between each variable node, the error term in the cost function is defined, such as

[0040] The hybrid entropy cost function serves as the optimization objective of the sum-product algorithm, guiding the message passing process in S3 (such as maximizing the relevant entropy) to update the posterior estimate.

[0041] Three steps of association:

[0042] S1 provides model parameters for S2, and S2 provides optimization criteria for S3.

[0043] S1 output model framework: establish a collaborative positioning model framework for multiple unmanned systems;

[0044] S2 output cost function: defines a robust hybrid entropy cost function to improve algorithm performance under non-Gaussian noise;

[0045] S3 outputs the final position coordinates: By solving the cost function through iterative optimization, it outputs the high-precision position coordinates of the target from the unmanned system.

[0046] In one embodiment, a method for collaborative positioning of multiple unmanned systems based on a hybrid entropy factor graph specifically includes the following steps:

[0047] Step 1: Construct a factor graph model for the multi-unmanned system collaborative positioning system

[0048] The method of this application can be extended to three-dimensional space, such as Figure 2 As shown, here we take a two-dimensional scene as an example. The position information of each unmanned system and the relative distance measurement information between the master and slave unmanned systems are abstracted into factor nodes and variable nodes: Function Node: F x , F y , X i , Y i , D i . Where i = 1, 2. are the horizontal and vertical coordinates of the i-th main unmanned system at time k. is the observation distance between the i-th master unmanned system and the slave unmanned system at time k. Refers to the estimated value of the distance between the i-th master unmanned system and the slave unmanned system in the x direction. Refers to the estimated value of the distance between the i-th master unmanned system and the slave unmanned system in the y direction. The initial position of the slave unmanned system is respectively through the node F x , F y Enter the factor graph, node X i , Y i Used to convert the position information of the master and slave unmanned systems into the position information difference between the x coordinate and the y coordinate, and the ranging information of the master and slave unmanned systems Through node D i Enter the factor graph, and then the three sets of information are at node D i Information fusion is carried out to realize the constraint and inference of the relative position relationship between the master and slave unmanned systems.

[0049] The constraints between the variables in the model are as follows:

[0050]

[0051] Among them, k is the discrete time index, Δt is the sampling time interval, v k represents the velocity at time k, θ k It represents the heading angle of the unmanned system in the geographic coordinate system at time k. is the prior estimate from the unmanned system at time k, It is the posterior estimate from the unmanned system at time k-1.

[0052] Step 2: Use a hybrid entropy method based on a convex combination of two Gaussian kernel functions with different kernel bandwidths

[0053]

[0054] in is the Gaussian kernel function; σ1 and σ2 are different kernel bandwidths, both greater than 0; 0≤a≤1 is the mixing coefficient; They are function nodes F x ,F y ,X i ,Y i ,D i The related entropy of , ||·|| represents the two norm, X k,k-1 ,Y k,k-1 is the state transition function.

[0055] Step 3: Achieve optimal estimation of the target from the unmanned system through the transfer and update of the sum-product algorithm in the factor graph model

[0056] (1) From time k-1 to time k, the estimated state, running speed and heading angle of the unmanned system are updated in real time based on the maximum correlation entropy principle in the factor graph. Yes Take the maximum value solution. That is,

[0057]

[0058] (2) When the observation distance and position information of the two main unmanned systems at time k is received from the unmanned system, the position is estimated using the hybrid entropy algorithm of the factor graph. First, it is necessary to solve get It can be expressed as,

[0059]

[0060] when When Right now,

[0061]

[0062] Similarly,

[0063]

[0064] (3) When the observation distance and position information of the two main unmanned systems at time k is received from the unmanned system, the position is estimated using the hybrid entropy algorithm of the factor graph. First, it is necessary to solve get It can be expressed as,

[0065]

[0066] when When The solution is:

[0067]

[0068] Similarly, we can get:

[0069]

[0070] At this time, the target's position coordinates are obtained from the unmanned system

[0071] The effectiveness of the present invention is further verified by the following specific examples:

[0072] To verify the effectiveness of the positioning method proposed in this invention, this embodiment selects two master unmanned systems and one slave unmanned system for collaborative positioning. Since the depth information of unmanned systems, such as underwater unmanned vehicles, is easily obtained, the positioning accuracy of the collaborative positioning algorithm in a two-dimensional plane is mainly considered. The collaborative positioning algorithm can be easily extended from a two-dimensional plane to a three-dimensional space. In actual measurement, due to the complexity and variability of the actual environment and the influence of factors such as the error of the sensor itself, the distance measurement d k Producing error:

[0073]

[0074] Among them, the position of the main unmanned system is (X k ,Y k ). ν k is the measurement noise and has a non-Gaussian distribution.

[0075] The initial positions of the two main unmanned systems are (200m, 1000m) and (200m, 500m), respectively. The initial position of the slave unmanned system is (0m, 0m). The speed of the three unmanned systems is 1m / s. The sampling interval of the simulation test is 1s, and the simulation time is 500s. The two main unmanned systems are located above the slave unmanned system. Figure 3 shown.

[0076] The measurement noise of the master-slave unmanned system exhibits a non-Gaussian distribution. A Gaussian mixture distribution is used to generate non-Gaussian measurement noise. The performance of different algorithms is compared using the mean square error (RMSE) as the performance metric:

[0077]

[0078] Where N represents the number of simulation steps, is the estimated position from the unmanned system at time k, (x k ,y k ) is the real position of the unmanned system at time k. For the values ​​of a, σ1, σ2 in the proposed algorithm, as follows Figure 3 As shown, a=0.1,σ1=1,σ2=4 are selected.

[0079] In order to better reflect the superiority of this method, the present invention is compared with similar positioning methods, and its positioning error is as follows Figure 5 The results show that the positioning accuracy of the present invention is significantly better than that of similar methods.

[0080] According to another aspect of the present application, a multi-unmanned system collaborative positioning device based on a hybrid entropy factor graph is also claimed, which applies any of the above-mentioned collaborative positioning methods and is characterized by comprising:

[0081] A factor graph model building module is configured to abstract the position information of each unmanned system and the relative distance measurement information between each master unmanned system and each slave unmanned system into factor nodes, thereby building a factor graph model for the collaborative positioning of multiple unmanned systems and obtaining the constraints and inference results of the relative position relationship between each master unmanned system and each slave unmanned system;

[0082] A mixed entropy function calculation module is configured to define a cost function based on the mixed entropy of convex combinations of Gaussian kernel functions with different kernel bandwidths based on the impact of non-Gaussian noise environments on the performance of the collaborative localization algorithm;

[0083] The optimal estimation module is configured to use the obtained hybrid entropy cost function. When the target slave unmanned system receives the observation distance and position information of each main unmanned system at a certain moment, the function node takes maximizing the relevant entropy as the goal, and optimally estimates the position of the target slave unmanned system through the sum-product algorithm, and finally obtains the position coordinates of the target slave unmanned system.

[0084] The above is only a preferred embodiment of the present application. It should be noted that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be considered as the scope of protection of the present application.

[0085] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications based on these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-unmanned system collaborative positioning method based on hybrid entropy factor graph, characterized in that: The method comprises the following steps: S1: By abstracting the position information of each unmanned system and the relative distance measurement information between each master unmanned system and each slave unmanned system into factor nodes, a factor graph model for multi-unmanned system collaborative positioning is established to obtain the constraints and inference results of the relative position relationship between each master unmanned system and each slave unmanned system; S2, based on the impact of non-Gaussian noise environment on the performance of the collaborative localization algorithm, defines a cost function based on the mixed entropy of the convex combination of Gaussian kernel functions with different kernel bandwidths; S3, using the obtained hybrid entropy cost function, when the target slave unmanned system receives the observation distance and position information of each main unmanned system at a certain moment, the function node takes maximizing the relevant entropy as the goal, and uses the sum-product algorithm to optimally estimate the position of the target slave unmanned system, and finally obtains the position coordinates of the target slave unmanned system.

2. The method for collaborative positioning of multiple unmanned systems based on hybrid entropy factor graph according to claim 1, characterized in that: Based on the factor graph model, a subsequent cost function is designed to solve the posterior estimation of the coordinates of the slave unmanned system and the relative position estimation results between the master and slave unmanned systems; wherein, The variable nodes and constraint relationships defined in the factor graph model can be used for the cost function of the mixed entropy in step S2; The prior estimates and observation information from the unmanned system coordinates in the factor graph model can be directly used in the sum-product algorithm in step S3 as the initial state and measurement input.

3. The method for collaborative positioning of multiple unmanned systems based on hybrid entropy factor graph according to claim 1, characterized in that: In the S1, Include: Variable Node: Function Node: F x , F y , X i , Y i , D i ; From the initial position of the unmanned system, pass through node F x , F y Enter the factor graph, node X i , Y i Used to convert the position information of the master and slave unmanned systems into the position information difference between the x coordinate and the y coordinate, and the ranging information of the master and slave unmanned systems Through node D i Enter the factor graph, and then the three sets of information are at node D i Perform information fusion to constrain and infer the relative position relationship between the master and slave unmanned systems; Where i = 1, 2, is the horizontal and vertical coordinates of the i-th master unmanned system at the moment; is the observation distance between the i-th master unmanned system and the slave unmanned system at time k; Refers to the estimated value of the distance between the ith master unmanned system and the slave unmanned system in the x direction; Refers to the estimated value of the distance between the i-th master unmanned system and the slave unmanned system in the y direction.

4. The method for collaborative positioning of multiple unmanned systems based on hybrid entropy factor graph according to claim 3 is characterized in that: The constraints between the variables in the model are as follows: Among them, k is the discrete time index, Δt is the sampling time interval, v k represents the speed at time k, θ k It represents the heading angle of the unmanned system in the geographic coordinate system at time k. is the prior estimate from the unmanned system at time k, It is the posterior estimate from the unmanned system at time k-1.

5. The method for collaborative positioning of multiple unmanned systems based on hybrid entropy factor graph according to claim 1, characterized in that: The S3 specifically includes: in, is the Gaussian kernel function; σ1 and σ2 are different kernel bandwidths, both greater than 0; 0≤a≤1 is the mixing coefficient; They are function nodes F x ,F y ,X i ,Y i ,D i The related entropy of , ||·|| represents the two norm, X k,k-1 ,Y k,k-1 is the state transition function.

6. The method for collaborative positioning of multiple unmanned systems based on hybrid entropy factor graph according to claim 1, characterized in that: The S3 specifically includes: S3.1, from time k-1 to time k, the estimated state, speed and heading angle of the unmanned system are updated in real time based on the maximum correlation entropy principle in the factor graph. Yes Take the maximum value solution, that is S3.2, when the observation distance and position information of the two main unmanned systems at time k is received from the unmanned system, the position is estimated using the hybrid entropy algorithm of the factor graph; first, it is necessary to solve get It can be expressed as, when When And make but, S3.3, in the update When not Estimates of , using only a priori estimates So the right side of the above equation Use a priori estimates; And let, get, Similarly, S3.4, updated Then, the hybrid entropy algorithm of factor graph is used for position estimation. First, it is necessary to solve get It can be expressed as, when When And let, get, Similarly, At this time, the target's position coordinates are obtained from the unmanned system 7. A multi-unmanned system collaborative positioning device based on a hybrid entropy factor graph, applying any collaborative positioning method from weight 1 to weight 6, characterized in that: include: A factor graph model building module is configured to abstract the position information of each unmanned system and the relative distance measurement information between each master unmanned system and each slave unmanned system into factor nodes, thereby building a factor graph model for the collaborative positioning of multiple unmanned systems and obtaining the constraints and inference results of the relative position relationship between each master unmanned system and each slave unmanned system; A mixed entropy function calculation module is configured to define a cost function based on the mixed entropy of convex combinations of Gaussian kernel functions with different kernel bandwidths based on the impact of non-Gaussian noise environments on the performance of the collaborative localization algorithm; The optimal estimation module is configured to use the obtained hybrid entropy cost function. When the target slave unmanned system receives the observation distance and position information of each main unmanned system at a certain moment, the function node takes maximizing the relevant entropy as the goal, and optimally estimates the position of the target slave unmanned system through the sum-product algorithm, and finally obtains the position coordinates of the target slave unmanned system.

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