Unmanned aerial vehicle cluster communication positioning fusion device and method in GNSS denial environment

Abstract

The application discloses a kind of unmanned plane cluster communication positioning fusion device and method under GNSS denial environment, it is related to unmanned plane cooperative positioning technical field, device includes sensor perception unit, obtains distance information, inertial data information and / or GNSS information;Cooperative positioning iterative optimization unit, according to the information in neighbor table, position estimation is carried out and cooperative positioning performance evaluation is carried out, and the position estimation of self that meets evaluation result is regarded as self position information;Network construction unit, periodically broadcast Hello data packet to adjacent unmanned plane, data in the Hello data packet received is stored in neighbor table;Routing decision unit, carry out routing strategy selection based on position information, obtain optimal transmission path;Data transmission unit, transmit data along optimal transmission path.The positioning function and communication function of the present application promote each other by the design of cooperative positioning algorithm and routing strategy, improve the autonomous cooperative communication and positioning ability of unmanned plane cluster in specific environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle cluster ad hoc network and unmanned aerial vehicle cooperative positioning, and particularly relates to a device and method for unmanned aerial vehicle cluster communication and positioning fusion in GNSS denial environment. BACKGROUND

[0002] Unmanned aerial vehicles (UAVs) have been widely used in emergency rescue, forest search, military operations and other tasks due to their high-speed movement, wide field of view and flexible operation characteristics. In these complex tasks, UAVs usually work cooperatively in the form of clusters, so efficient ad hoc network routing technology and accurate position information are crucial to the success of the task. On the one hand, data transmission between cluster UAVs relies on ad hoc network, and in complex UAV environment, mutual interference and variable conditions can cause significant decline in communication efficiency. On the other hand, electromagnetic interference, complex terrain and other external factors can affect the positioning stability and accuracy of UAVs, thus causing adverse effects on task execution.

[0003] Compared with traditional topology-based routing, location information-based routing technology can better cope with the dynamic environment of UAV clusters, and location information can help optimize and make decisions for routing algorithms, improving the efficiency of cluster communication. For example, Yajuan Cui et al. [Y. Cui, H. Tian, C. Chen, W. Ni, H. Wu and G. Nie, "New Geographical Routing Protocol for Three-Dimensional Flying Ad-Hoc Network Based on New Effective Transmission Range," in IEEE Transactions on Vehicular Technology, vol. 72, no. 12, pp. 16135-16147, Dec. 2023, doi: 10.1109 / TVT.2023.3296082.] proposed to select a reliable transmission distance according to the time-varying channel conditions, and to determine the optimal next hop node by considering the link duration, distance and angle between nodes, so as to enhance the location information-based routing protocol and adapt to the variable environment requirements in UAV tasks. Similarly, when the UAV cluster appears unstable or inaccurate positioning, a cooperative positioning system can be constructed using the communication network, mutual distance and other information of the UAVs to provide positioning information and improve positioning accuracy, as proposed by Xiaobo Gu et al. [X. Gu, C. Zheng, Z. Li, G. Zhou, H. Zhou and L. Zhao, "Cooperative Positioning for UAVs in GNSS-Denied Environments," in IEEE Transactions on Vehicular Technology, vol. 72, no. 12, pp. 16135-16147, Dec. 2023, doi: 10.1109 / TVT.2023.3296082.]

[0004] "Cooperative Localization for UAV Systems From the Perspective of Physical Clock Synchronization," in IEEE Journal on Selected Areas in Communications, vol. 42, no. 1, pp. 21-33, Jan. 2024, doi: 10.1109 / JSAC.2023.3322797.] Based on radio ranging measurements between UAVs, a framework is proposed to jointly estimate clock errors and relative distances for relative positioning, and a closed loop consisting of synchronized two-way ranging, parameter estimation, and clock tuning is constructed to improve positioning accuracy.

[0005] In complex environments, drones can improve positioning accuracy and reliability by integrating communication and positioning technologies, leveraging multi-source data and ensuring efficient and reliable data transmission under complex conditions. Lu Sun et al. [L.Sun, J.Wang, L.Wan, K.Li, X.Wang and Y.Lin, "Human-UAV Interaction Assisted Heterogeneous UAVSwarm Scheduling for Target Searching in Communication Denial Environment," in IEEE Transactions on Automation Science and Engineering, doi: 10.1109 / TASE.2024.3412005.] proposed a scheduling time slot model specifically for scenarios with poor communication quality. This model allows drones to transmit data during communication time slots and uses route fitting and Kalman filtering to predict drone positions during communication denial time slots, optimizing drone scheduling management. Chinese patent CN118488379A proposes a drone ultra-wideband positioning coordination method based on a distributed out-of-band network. This method dynamically manages drone positioning nodes by establishing a distributed out-of-band network of drones, coordinating ranging and positioning between drone nodes to achieve efficient communication and collaborative positioning among multiple drones. Chinese patent CN117685953A proposes a multi-drone collaborative positioning method that integrates UWB and vision. This method achieves mutual collaboration and resource sharing through a drone network, using visual and UWB sensors to perceive the status of other drones, and constructing and solving pose graphs to achieve multi-drone collaborative positioning in the absence of a global reference frame.

[0006] Unmanned aerial vehicles (UAVs) have great potential in cooperative positioning and data transmission, especially in scenarios where positioning is inaccurate. UAVs can be used for cooperative positioning and communication to perform tasks. However, on the one hand, traditional topology-based routing protocols are difficult to adapt to the dynamic environment of high-speed movement and rapid changes in topology of UAVs, while location-based routing protocols can better adapt to UAV networks. However, inaccurate or frequent changes in location information can lead to routing failure, which in turn affects communication quality. On the other hand, cooperative positioning of UAVs relies on efficient communication between UAVs to ensure real-time synchronization of node location information. How to maintain stable communication in a dynamic environment and ensure the accuracy and timely updating of location information is a key challenge to improve the performance of UAV cooperative positioning systems. Currently, research in the field of UAV communication and positioning mainly focuses on routing strategies and information sharing for cooperative positioning in UAV networking, with few studies on the impact of inaccurate location information on UAV routing and how to optimize routing strategies to improve the effectiveness of cooperative positioning of UAV groups. Therefore, there is a broad development prospect for the in-depth integration of communication and positioning systems to optimize the communication and positioning performance of UAV groups. SUMMARY

[0007] To solve the above problems, the application provides a UAV cluster communication positioning fusion device and method in a GNSS denial environment. In the cooperative positioning process, the cooperative positioning iterative optimization unit of the device uses the self-attitude, speed, proximity distance, and historical position information collected by the sensor perception unit, as well as the attitude, speed, position, and connection state information of neighboring nodes shared by the network construction unit, to perform iterative optimization of UAV position information, thereby achieving accurate cooperative positioning of the UAV cluster. In the routing process, the routing decision unit selects the optimal transmission path for data packets based on the cooperative positioning results and shared information from the network construction unit, and then the data transmission unit completes the transmission of data packets along the path to achieve the routing function. At the same time, routing information is fed back to the network construction unit to optimize the neighbor node table and eliminate routing holes, further improving the accuracy of topology information. In this way, cooperative optimization of communication and positioning functions of the UAV cluster is achieved.

[0008] On the one hand, a UAV cluster communication positioning fusion device in a GNSS denial environment includes the following:

[0009] The positioning module includes a sensor perception unit and a cooperative positioning iterative optimization unit.

[0010] The sensor perception unit acquires inertial data information in real time, and acquires GNSS information as its own position information when outside the GNSS denial environment area range.

[0011] The cooperative positioning iterative optimization unit calculates the trustworthiness of each neighbor node according to the node degree when the sensor perception unit is in the GNSS denial environment area range, and marks the neighbor node with the trustworthiness higher than the preset trustworthiness threshold as a trusted neighbor node; constructs a local factor graph according to the trusted neighbor node, and performs iterative update of the local factor graph by minimizing the target function based on the inertial data information and the distance information, and obtains the self-position estimation from the finally obtained local factor graph; performs cooperative positioning performance evaluation on the self-position estimation, and if the cooperative positioning performance evaluation result exceeds the preset performance threshold, takes the self-position estimation as the self-position information; the target function is based on the inertial data information;

[0012] The routing module includes a network construction unit, a routing decision unit and a data transmission unit.

[0013] The network construction unit receives a Hello data packet and periodically broadcasts the Hello data packet to the adjacent unmanned aerial vehicle node; the Hello data packet includes the self-position information and the node degree; when the network construction unit broadcasts the Hello data packet, the sensor perception unit acquires the distance information;

[0014] The routing decision unit performs routing strategy selection to obtain the optimal transmission path: before the cooperative positioning iterative optimization unit performs cooperative positioning, the on-demand routing protocol is adopted to obtain the optimal transmission path; during the cooperative positioning performed by the cooperative positioning iterative optimization unit, it is checked whether the position information is complete; if the position information is not complete, the on-demand routing protocol is adopted to obtain the optimal transmission path; if the position information is complete, the neighbor node with the maximum utility function value is selected as the forwarding node as the optimal transmission path; the position information includes the self-position information, the self-position information of the neighbor node and the self-position information of the target node;

[0015] The data transmission unit performs data transmission according to the optimal transmission path of the routing decision unit.

[0016] Preferably, the distance information and the data in the received Hello data packet are stored in the neighbor table.

[0017] Preferably, the network construction unit receives the routing information of the routing decision unit, and eliminates the routing hole and optimizes the neighbor table by using the routing information.

[0018] Preferably, the trustworthiness of each neighbor node is calculated according to the node degree, and specifically as follows:

[0019] For the current unmanned aerial vehicle i, the trustworthiness of the neighbor unmanned aerial vehicle j is represented as:

[0020]

[0021] wherein C ij denotes the credibility of UAVj relative to UAVi; D j denotes the node degree of UAVj; delay ij denotes the time delay of sending Hello packet between UAVi and UAVj; BER ij denotes the bit error rate; ω1, ω2, ω3, ω4 respectively denote the weight coefficients, ω1+ω2+ω3+ω4=1; denotes the Fisher information value of UAVj.

[0022] Preferably, the Fisher information value of UAVj is expressed as:

[0023]

[0024] wherein x j denotes the position state of UAVj; E denotes the expected value; n(j) denotes the neighbor of UAVj; z jk denotes the measured distance of UAVj and k in the distance information; h(·) denotes the relative distance of two nodes; ||·|| 2 denotes the Euclidean distance; σ jk denotes the standard deviation of distance measurement of UAVj and k; denotes the expected value of the position of UAVk; the standard deviation of the position of UAVk.

[0025] Preferably, the local factor graph is iteratively updated by minimizing the objective function based on the weighted least squares method of time window, expressed as:

[0026]

[0027] wherein, denotes the optimal solution of the position state of UAV in the local factor graph; denotes the parameter x that makes the objective function reach the minimum value; x i denotes the position state of UAVi; L denotes the sliding time window; N denotes the number of factor nodes of the local factor graph constructed by the current UAV; C ij denotes the credibility of UAVj relative to UAVi; z ij denotes the distance observation value between UAVi and UAVj in the distance information; R D denotes the distance measurement covariance matrix; denotes the Euclidean distance with the weighted matrix R D ; R I denotes the IMU pre-integration covariance matrix; denotes the Euclidean distance with the weighted matrix R I ; Error representing the position change of the UAV i obtained from the inertial data information.

[0028] Preferably, the utility function for calculating the utility function value is represented as:

[0029] U i,j (t) = ω LET ·f LET (x i , x j ) + ω d ·f d (x j , x D ) + ω θ ·f θ (x i , x j , x D ) + ω c ·C ij (t)

[0030] wherein U i,j (t) represents the utility function of the UAV j relative to the UAV i; ω LET , ω d , ω, ω c represent weight coefficients respectively, ω LET + ω d + ω θ + ω c = 1; x i represents the position state of the node i; x j represents the position state of the neighbor node j; D represents the target node; f LET (x i , x j ) represents the link duration of the UAV i and j; f d (x j , x D ) represents the distance of the UAV j to the target node D; f θ (x i , x j , x D ) represents the size of the angle formed by the UAV i as the vertex and j and D as the two sides; C ij represents the credibility of the UAV j relative to the UAV i.

[0031] In another aspect, a UAV cluster communication positioning fusion method in a GNSS denial environment includes a UAV cluster communication positioning fusion device in a GNSS denial environment for each UAV in the UAV cluster; and the method steps are as follows:

[0032] S1, initialize all UAV parameters, all UAVs at the edge of GNSS denial environment area range obtain GNSS information as their own position information through the sensor sensing unit;

[0033] S2, each UAV periodically broadcasts Hello data packet to adjacent UAVs through the network construction unit, the Hello data packet includes its own position information and node degree; each UAV stores the information in the received Hello data packet in the neighbor table;

[0034] S3, each UAV is a node; when there is a data transmission demand between UAVs, the routing decision unit of the data transmission source node UAV obtains the RREP response of other UAVs by initiating RREQ request, and obtains the routing path; the data transmission unit of the data transmission source node UAV transmits data packets according to the routing path;

[0035] S4, the cooperative positioning iterative optimization unit of each UAV calculates the credibility of each neighbor node according to the node degree in the neighbor table, and marks the neighbor nodes with credibility higher than the preset credibility threshold as credible neighbor nodes;

[0036] S5, the cooperative positioning iterative optimization unit of each UAV constructs a local factor graph according to the credible neighbor nodes, and performs iterative update of the local factor graph by minimizing the objective function, and obtains its own position estimate from the final obtained local factor graph;

[0037] S6, each UAV node performs cooperative positioning performance evaluation on its own position estimate, if the cooperative positioning performance evaluation result exceeds the preset performance threshold, the own position estimate is taken as the own position information, and is used for broadcasting the Hello data packet in the next period;

[0038] S7, when there is a data transmission demand between UAVs in the cooperative positioning process, the routing decision unit of the UAV checks whether there is its own position information, the position information of neighbor nodes and the position information of target nodes; if the position information is complete, the routing decision unit selects the neighbor node with the maximum utility function value as the forwarding node as the optimal transmission path; if the position information is incomplete, the routing decision unit obtains the RREP response by initiating RREQ request, and obtains the routing path as the optimal transmission path; the data transmission unit of the UAV transmits data packets according to the optimal transmission path.

[0039] Preferably, if the position information is incomplete, the routing decision unit obtains the RREP response by initiating RREQ request, and obtains the routing path as the optimal transmission path, which is as follows:

[0040] The routing decision unit initiates a RREQ request; once a target node is found or an intermediate node of the path of the target node is mastered, the node returns a RREP response, informing the data transmission source node of the available routing path of the UAV; the intermediate node can provide a complete path through its routing table or adopt a greedy forwarding strategy to select a forwarding node for data packet forwarding by using the utility function value of the neighbor node.

[0041] Compared with the prior art, the present application has the following beneficial effects:

[0042] In the routing process, the routing decision unit selects the optimal transmission path of the data packet based on the cooperative positioning result and the shared information of the network construction unit, and then the data transmission unit completes the transmission of the data packet along the path to realize the routing function; at the same time, the routing information is fed back to the network construction unit to further improve the accuracy of the topology information by optimizing the neighbor node table and eliminating routing holes; the cooperative optimization of the UAV cluster in the communication and positioning functions is realized. BRIEF DESCRIPTION OF DRAWINGS

[0043] The present application will be further described below in conjunction with the drawings.

[0044] Figure 1 The device schematic diagram of the UAV cluster communication positioning fusion device in the GNSS denial environment of the embodiment of the present application;

[0045] Figure 2 The system model diagram of the UAV cluster communication positioning fusion method in the GNSS denial environment of the embodiment of the present application;

[0046] Figure 3 The system flowchart of the UAV cluster communication positioning fusion method in the GNSS denial environment of the embodiment of the present application;

[0047] Figure 4 The system timing diagram of the UAV cluster communication positioning fusion device in the GNSS denial environment of the embodiment of the present application. DETAILED DESCRIPTION

[0048] The present application will be further described below in conjunction with the drawings.

[0049] As shown in the drawings, a UAV cluster communication positioning fusion device in a GNSS denial environment comprises: Figure 1 The positioning module 100 comprises a cooperative positioning iterative optimization unit 101 and a sensor perception unit 102 to realize the cooperative positioning function.

[0050]

[0051] ​The cooperative positioning iterative optimization unit 101 uses the self-attitude, speed, proximity distance, historical position information collected by the sensor perception unit 102 and the attitude, speed, position, connection state information of the neighbor nodes shared by the network construction unit to perform iterative optimization of the unmanned aerial vehicle position information in the cooperative positioning process, so as to realize accurate cooperative positioning of the unmanned aerial vehicle cluster. Specifically, when the sensor perception unit 102 is in the GNSS denial environment area range, the cooperative positioning iterative optimization unit 101 calculates the credibility of each neighbor node according to the node degree in the neighbor table, and marks the neighbor nodes with credibility higher than the preset credibility threshold as credible neighbor nodes; according to the local factor graph of the credible neighbor nodes, and by minimizing the objective function based on the inertial data information and the distance information, the local factor graph is iteratively updated, and the self-position estimate is obtained from the final obtained local factor graph; the cooperative positioning performance of the self-position estimate is evaluated, and if the cooperative positioning performance evaluation result exceeds the preset performance threshold, the self-position estimate is taken as the self-position information.

[0052] The sensor perception unit 102 includes a ranging unit, an inertial measurement unit (IMU) and a global navigation satellite system (GNSS). The sensor perception unit 102 collects information, including self-attitude, speed, proximity distance (distance information), inertial data information, GNSS information and historical position information.

[0053] The routing module 200 includes a network construction unit 201, a routing decision unit 202 and a data transmission unit 203 to realize the routing function.

[0054] The network construction unit 201 periodically broadcasts a Hello data packet to the adjacent unmanned aerial vehicle nodes; the Hello data packet includes self-position information and node degree; the data in the received Hello data packet is stored in the neighbor table, including attitude, speed, position, connection state information.

[0055] During the routing process, the routing decision unit 202 selects the optimal transmission path for the data packet based on the collaborative positioning results and the shared information of the network construction unit 201. The data transmission unit 203 then completes the transmission of the data packet along this path to implement the routing function. Simultaneously, the routing information is fed back to the network construction unit 201, which further improves the accuracy of the topology information by optimizing the neighbor node table and eliminating routing holes. Specifically, before the collaborative positioning iterative optimization unit 101 performs collaborative positioning, an on-demand routing protocol is used, and the resulting routing path is the optimal transmission path. During the collaborative positioning iterative optimization unit 101 performs collaborative positioning, the completeness of the location information is checked. If the location information is incomplete, an on-demand routing protocol is used, and the resulting routing path is the optimal transmission path. If the location information is complete, the neighbor node with the largest utility function value is selected as the forwarding node, which serves as the optimal transmission path. In this embodiment, the location information includes the target node's own location information, the location information of the neighbor nodes in the neighbor table, and the target node's own location information.

[0056] The data transmission unit 203 transmits the data packet along the optimal transmission path.

[0057] See also Figure 2 、 Figure 3 and Figure 4 As shown in Figure 1, a UAV cluster communication positioning fusion method in a GNSS-denied environment is described as follows:

[0058] Step 1: Define system parameters and configure drones. Drone behavior is described using a Gaussian-Markov model. Each drone has a positioning module and a routing module. The drone's velocity and position follow a Gaussian distribution. In a GNSS-denied environment, strong interference in the inner areas prevents drones from obtaining position information. However, in the outer areas, where interference is less, drones can still receive partial GNSS signals, achieving accurate positioning.

[0059] In this embodiment, the system parameters include: the UAV flight space is 1000×1000 meters, the flight altitude H∈[10,500]m, the number of UAVs n∈[80,160], the transmission power is 20dBm, the channel transmission rate is 1Mbps, the IEEE802.11n MAC layer protocol is adopted, the signal bandwidth is 20MHz, and the transmission range is 200 meters.

[0060] Step two: network topology initialization: the network construction unit 201 of all UAVs periodically broadcasts Hello data packets, sends its own position, speed and node degree information to neighbor nodes, and stores the received information in the neighbor table, preparing for subsequent cooperative positioning and routing. Among them, the edge area UAV can broadcast its position information initially, while the position information of the internal UAV needs to be updated to a certain extent before it can be obtained and broadcast.

[0061] Step three: preliminary routing establishment. When the UAV performing the cluster task needs to transmit task data to the specified target node, the routing decision unit 202 of the source node will initiate a routing request (RREQ, Router Request), which is forwarded by neighbor nodes hop by hop until it reaches the target node or finds a routing path to the target. Then the target node returns a routing reply (RREP, Router Reply), and the source node transmits data packets according to the routing path determined in the routing reply.

[0062] Step four: neighbor node credibility evaluation. The cooperative positioning iterative optimization unit 101 of each UAV calculates the credibility C of each neighbor node based on the neighbor table, and marks the nodes with credibility higher than the preset threshold as credible nodes, preparing for further factor graph optimization.

[0063] Specifically, for the current UAV node i, the credibility C of its neighbor node j is calculated as follows: ij As follows:

[0064]

[0065] Where D j represents the node degree of UAV j, delay ij represents the time delay of sending hello data packets between UAV i and j, BER ij represents the bit error rate. ω1, ω2, ω3, ω4 represent weight coefficients, and their relationship satisfies ω1+ω2+ω3+ω4=1.

[0066] represents the Fisher information (FI) value of UAV j, which is specifically as follows:

[0067]

[0068] Where D j represents the node degree of UAV j, delay ij represents the time delay of sending hello data packets between UAV i and j, BER ij represents the bit error rate. n(j) represents the neighbor of j, z jkdenotes the measured distance between UAV j and k in the range information, h(·) denotes the Euclidean distance between two node coordinates, σ jk denotes the standard deviation of the distance measurement between UAV j and k, and denote the expectation and standard deviation of the position of UAV k, respectively. The node selects the neighbor nodes with higher credibility as factor nodes to participate in the factor graph algorithm optimization according to the credibility of the neighbor nodes and the set credibility threshold.

[0069] Step five: factor graph optimization. The cooperative positioning iterative optimization unit 101 of each UAV constructs a factor graph according to the credible neighbor nodes, and iteratively updates its position estimate by using a weighted least squares method based on a time window.

[0070] Specifically, based on the selected factor nodes, the cooperative positioning iterative optimization unit 101 uses a weighted least squares method based on a time window to iteratively update the factor graph, and the specific formula is as follows:

[0071]

[0072] wherein, denotes the optimal solution of the position state of the UAV in the local factor graph, denotes the parameter x that makes the objective function minimum, x i denotes the position state of the UAV i, L is a sliding time window, N is the number of factor nodes of the local factor graph currently constructed by the UAV, C ij denotes the credibility between the UAV i and the UAV j, z ij denotes the distance observation value between the node i and the node j in the range information, R D is a distance measurement covariance matrix, denotes the Euclidean distance with the weighted matrix R D , R I is an IMU pre-integration covariance matrix, denotes the Euclidean distance with the weighted matrix R I , denotes the error of the position change of the UAV i obtained from the inertial data information.

[0073] Step six: performance evaluation of the cooperative positioning result. The UAV needs to calculate the Fisher information (FI) between itself and the credible neighbor nodes. If the FI value exceeds the preset threshold FI th , the positioning result is considered reliable, and the UAV broadcasts its position information in the next period; otherwise, it does not broadcast.

[0074] Step seven: data packet forwarding decision. In the cooperative positioning process, the data transmission unit 203 of the unmanned aerial vehicle checks whether the location information of the unmanned aerial vehicle itself, the neighbor and the target node is available before forwarding the data packet. If the condition is met, the forwarding node is selected according to the utility function U of the neighbor node; in the request process, if the source node location information is insufficient, the RREQ request can be initiated. Once the target node is found or the intermediate node of the target node path is mastered, the node returns the RREP response to inform the source node of the available path. The intermediate node can provide the complete path through its routing table, or can use the greedy forwarding strategy to select the forwarding node for data packet forwarding by using the utility function U value of the neighbor node, and finally deliver the data to the target node.

[0075] Specifically, when the unmanned aerial vehicle has a data packet to send, the routing decision unit 202 selects the forwarding node according to the maximum value of the utility function U of the unmanned aerial vehicle and the neighbor link, as follows:

[0076] U i,j (t)=ω LET ·f LET (x i ,x j )+ω d ·f d (x j ,x D )+ω θ ·f θ (x i ,x j ,x D )+ω c ·C ij (t)

[0077] Wherein, ω LET , ω d , ω θ , ω c represent weight coefficients, satisfying ω LET + ω d + ω θ + ω c = 1. x i and x j represent the position state of node i and its neighbor node j respectively, D represents the target node, f LET (x i ,x j ) represents the link duration of the unmanned aerial vehicle i and j, f d (x j ,x D ) represents the distance from the unmanned aerial vehicle j to the target node D, f θ (x i ,x j ,xD represents the size of the angle formed by the two sides of the drone i as the vertex, j and D ij represents the credibility of the drone j relative to the drone i.

[0078] The above is only a specific embodiment of the present application, but the design concept of the present application is not limited thereto, and any non-essential modification of the present application using this concept shall be deemed to be an infringement of the protection scope of the present application.

Claims

1. A UAV cluster communication positioning fusion device in a GNSS-denied environment, characterized by: include: Positioning module, including sensor perception unit and collaborative positioning iterative optimization unit; The sensor perception unit acquires inertial data information in real time; When outside the GNSS-denied environment, the device obtains GNSS information as its own location information; The collaborative positioning iterative optimization unit calculates the credibility of each neighbor node based on the node degree when the sensor perception unit is within the GNSS-denied environment area, and marks the neighbor nodes with credibility higher than a preset credibility threshold as trusted neighbor nodes; constructs a local factor graph based on the trusted neighbor nodes, and iteratively updates the local factor graph by minimizing the objective function based on inertial data information and distance information, and obtains its own position estimate from the final local factor graph; performs collaborative positioning performance evaluation on the self-position estimate, and if the collaborative positioning performance evaluation result exceeds the preset performance threshold, uses the self-position estimate as the self-position information; Routing module, including network construction unit, routing decision unit and data transmission unit; The network construction unit receives a Hello data packet and periodically broadcasts the Hello data packet to neighboring drone nodes; the Hello data packet includes its own location information and node degree; when the network construction unit broadcasts the Hello data packet, the sensor perception unit obtains distance information; The routing decision unit selects a routing strategy to obtain an optimal transmission path: before the collaborative positioning iterative optimization unit performs collaborative positioning, an on-demand routing protocol is used, and the resulting routing path is the optimal transmission path; during the collaborative positioning iterative optimization unit performs collaborative positioning, the completeness of the location information is checked; if the location information is incomplete, an on-demand routing protocol is used, and the resulting routing path is the optimal transmission path; if the location information is complete, the neighboring node with the largest utility function value is selected as the forwarding node as the optimal transmission path; the location information includes the location information of the own neighboring node, the location information of the own target node, and the location information of the own target node; The data transmission unit performs data transmission according to the optimal transmission path of the routing decision unit.

2. According to the UAV cluster communication positioning fusion device in a GNSS-denied environment according to claim 1, the distance information and the data in the received Hello data packet are stored in a neighbor table.

3. The UAV cluster communication positioning fusion device in a GNSS-denied environment according to claim 2 is characterized in that: The network construction unit receives the routing information from the routing decision unit and uses the routing information to eliminate routing holes and optimize the neighbor table.

4. The UAV cluster communication positioning fusion device in a GNSS-denied environment according to claim 1 is characterized in that: The credibility of each neighbor node is calculated based on the node degree, as follows: For the current drone i, the credibility of its neighboring node drone j is expressed as: Among them, C ij Denotes the credibility of drone j relative to drone i; D j represents the node degree of drone j, delay ij represents the delay of sending Hello packets between UAV i and UAV j; BER ij represents the bit error rate; ω1, ω2, ω3, and ω4 represent weight coefficients, ω1+ω2+ω3+ω4=1; represents the Fisher information value of UAV j.

5. The UAV cluster communication positioning fusion device in a GNSS-denied environment according to claim 4 is characterized in that: The Fisher information value of the UAV j is expressed as: Among them, x j represents the position state of UAV j; E represents the expected value; n(j) represents the neighbor of UAV j; z jk represents the measured distance between UAVs j and k in the distance information; h(·) represents the relative distance between two nodes; ||·|| 2 represents the Euclidean distance; σ jk represents the standard deviation of the measured distance between UAVs j and k; represents the expected position of UAV k; The standard deviation of the k-position of the drone.

6. The UAV cluster communication positioning fusion device in a GNSS-denied environment according to claim 1 is characterized in that: The weighted least squares method based on the time window is used to iteratively update the local factor graph by minimizing the objective function, which is expressed as: in, Represents the optimal solution of the UAV position state in the local factor graph; Indicates finding the parameter x that makes the objective function achieve the minimum value; x i Represents the position state of UAV i; L represents the sliding time window; N represents the number of factor nodes of the local factor graph constructed by the current UAV; C ij represents the credibility of drone j relative to drone i; z ij R represents the distance observation value between UAV i and UAV j in the distance information; D represents the distance measurement covariance matrix; Represents a weighted matrix R D Euclidean distance of I Represents the IMU pre-integrated covariance matrix; Represents a weighted matrix R I The Euclidean distance of It represents the error of the position change of UAV i obtained from the inertial data information.

7. The UAV cluster communication positioning fusion device in a GNSS-denied environment according to claim 1 is characterized in that: The utility function for calculating the utility function value is expressed as: U i,j (t)=ω LET ·f LET (x i ,x j )+ω d ·f d (x j ,x D )+ωθ·f θ (x i ,x j ,x D )+ω c ·C ij (t) Among them, U i,j (t) represents the utility function of drone j relative to drone i; ω LET 、ω d 、ω θ 、ω c Represent the weight coefficients, ω LET +ω d +ω θ +ω c =1;x i represents the position state of node i; x j represents the location status of neighbor node j; D represents the target node; f LET (x i ,x j ) represents the link duration between UAVs i and j; f d (x j ,x D ) represents the distance from UAV j to target node D; f θ (x i ,x j ,x D ) represents the angle formed by UAV i as the vertex and j and D as the two sides; C ij Represents the credibility of drone j relative to drone i.

8. The UAV cluster communication positioning fusion device in a GNSS-denied environment according to claim 1 is characterized in that: The collaborative positioning performance evaluation specifically involves calculating the Fisher information between the UAV itself and its trusted neighbor nodes.

9. A method for fusion of UAV cluster communication positioning in a GNSS-denied environment, characterized in that: Each drone in the drone cluster includes the drone cluster communication positioning fusion device in a GNSS-denied environment according to any one of claims 1 to 8; the method steps are as follows: S1, initialize all drone parameters. All drones at the edge of the GNSS-denied environment obtain GNSS information as their own location information through sensor perception units; S2, each UAV regularly broadcasts a Hello packet to neighboring UAVs through the network construction unit. The Hello packet includes its own location information and node degree; each UAV stores the information in the received Hello packet in the neighbor table; In S3, each drone is a node. When there is a need for data transmission between drones, the routing decision unit of the data transmission source node drone initiates a RREQ request to obtain RREP responses from other drones and obtain the routing path. The data transmission unit of the data transmission source node drone transmits the data packet according to the routing path; S4, the collaborative positioning iterative optimization unit of each UAV calculates the credibility of each neighbor node according to the node degree in the neighbor table, and marks the neighbor node with a credibility higher than the preset credibility threshold as a trusted neighbor node; In step S5, the collaborative positioning iterative optimization unit of each UAV constructs a local factor graph based on the trusted neighbor nodes, and iteratively updates the local factor graph by minimizing the objective function, and obtains its own position estimate from the final local factor graph; In step S6, each UAV node performs collaborative positioning performance evaluation on its own position estimate. If the collaborative positioning performance evaluation result exceeds the preset performance threshold, the UAV node uses its own position estimate as its own position information and uses it for the Hello packet broadcast in the next cycle. S7, when there is a need for data transmission between UAVs during the collaborative positioning process, the UAV's routing decision unit checks whether it has its own location information, the location information of neighboring nodes, and the location information of the target node; If the location information is complete, the routing decision unit selects the neighbor node with the largest utility function value as the forwarding node as the optimal transmission path; if the location information is incomplete, the routing decision unit initiates an RREQ request to obtain an RREP response and obtains the routing path as the optimal transmission path; the data transmission unit of the drone transmits the data packet according to the optimal transmission path.

10. The method for fusion of UAV cluster communication and positioning in a GNSS-denied environment according to claim 9, characterized in that: If the location information is incomplete, the routing decision unit initiates an RREQ request to obtain an RREP response and obtains a routing path as the optimal transmission path, as follows: The routing decision unit initiates an RREQ request; once the target node or the intermediate node that knows the path to the target node is found, the node returns an RREP response to inform the data transmission source node of the available routing path for the drone; the intermediate node can either provide a complete path through its routing table or adopt a greedy forwarding strategy, using the utility function value of the neighboring node to select a forwarding node for data packet forwarding.

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