CN-120050222-B - Unmanned aerial vehicle network routing method based on-demand prediction

CN120050222BCN 120050222 BCN120050222 BCN 120050222BCN-120050222-B

Abstract

The invention relates to the technical field of routing, in particular to an unmanned aerial vehicle network routing method based on demand prediction, which comprises the steps of determining state information of a neighbor unmanned aerial vehicle based on an improved Kalman filtering model, predicting the latest state information of the neighbor unmanned aerial vehicle based on the state information, determining the communication state of the unmanned aerial vehicle and the neighbor unmanned aerial vehicle and the message coverage rate of the neighbor unmanned aerial vehicle on a flooding instruction, setting replay delay and replay probability for the neighbor unmanned aerial vehicle, responding to the communication state of the unmanned aerial vehicle and the neighbor unmanned aerial vehicle, determining a distance related factor and a traffic load factor of the unmanned aerial vehicle, calculating a routing cost value of a candidate link, determining an optimal path to forward data, searching a standby link aiming at the failure state of the optimal path to replace the failed optimal path or repairing the path. The invention combines the node mobility prediction and the flooding mechanism of the neighbor coverage to realize the rapid addressing and dynamic optimization of the route.

Inventors

WANG JINGJING
Feng Houze
CHEN JIANRUI
FU HANG
REN PENGFEI
ZHOU LIN

Assignees

北京航空航天大学

Dates

Publication Date: 20260505
Application Date: 20250124

Claims (8)

1. An on-demand predictive unmanned network routing method, comprising: Determining state information of the neighbor unmanned aerial vehicle based on the improved Kalman filtering model, and predicting the latest state information of the neighbor unmanned aerial vehicle based on the state information; determining the communication state of the unmanned aerial vehicle and the neighbor unmanned aerial vehicle and the message coverage rate of the neighbor unmanned aerial vehicle on the flooding instruction based on the latest state information, and setting replay delay and replay probability for the neighbor unmanned aerial vehicle; responding to the communication state of the unmanned aerial vehicle and the neighbor unmanned aerial vehicle, determining a distance related factor and a traffic load factor of the unmanned aerial vehicle, calculating a route cost value of a candidate link, and determining an optimal path to forward data; Searching a standby link aiming at the failure state of the optimal path to replace the failed optimal path; Or, performing path repair; The process of determining message coverage of the flooding instruction by the neighbor drone based on the latest state information includes, Determining a neighbor unmanned aerial vehicle set which does not receive a flooding instruction as an uncovered neighbor set; Determining a self neighbor set of the neighbor unmanned aerial vehicle; determining a message coverage based on the uncovered neighbor set and the own neighbor set; The process of setting replay delay and replay probability for the neighbor drone includes, Determining the overlapping degree of the neighbors of the unmanned aerial vehicle; determining a replay delay based on the neighbor overlap; And determining replay probability based on the message coverage rate and the number of unmanned aerial vehicles.
2. The on-demand predicted unmanned aerial vehicle network routing method of claim 1, wherein the determining the status information of the neighbor unmanned aerial vehicle based on the modified Kalman filtering model comprises, Determining the space coordinates of the neighbor unmanned aerial vehicle and the space speed of the neighbor unmanned aerial vehicle; determining a receiving time interval of adjacent control packets; Determining a position velocity relationship based on the spatial coordinates, the spatial velocity, and the receive time interval; determining a state vector of the neighbor unmanned aerial vehicle based on the position and speed relationship; Determining the state vector as state information of the neighbor unmanned aerial vehicle; the space coordinates and the space speed are obtained through an interactive control packet.
3. The on-demand predicted unmanned aerial vehicle network routing method of claim 1, wherein the predicting the latest state information of a neighbor unmanned aerial vehicle based on the state information comprises, Determining a measurement equation and a state prediction equation; And predicting the latest state information of the neighbor unmanned aerial vehicle based on the measurement equation and the state prediction equation and combining the state information.
4. The on-demand predicted unmanned aerial vehicle network routing method of claim 1, wherein the determining of the communication state of the unmanned aerial vehicle with the neighbor unmanned aerial vehicle is based on the latest state information, wherein, If the distance between the unmanned aerial vehicle and the neighbor unmanned aerial vehicle is smaller than the minimum threshold distance transmitted between the unmanned aerial vehicle and the neighbor unmanned aerial vehicle, the unmanned aerial vehicle and the neighbor unmanned aerial vehicle are considered to be neighbor unmanned aerial vehicle nodes, and direct communication can be achieved; If the distance between the unmanned aerial vehicle and the neighbor unmanned aerial vehicle is greater than or equal to the minimum threshold distance transmitted between the unmanned aerial vehicle and the neighbor unmanned aerial vehicle, the unmanned aerial vehicle and the neighbor unmanned aerial vehicle are not considered to be neighbor unmanned aerial vehicle nodes, and direct communication is not possible; the distance between the unmanned aerial vehicle and the neighbor unmanned aerial vehicle is obtained through an interactive control packet.
5. The on-demand predicted unmanned aerial vehicle network routing method of claim 1, wherein the determining the distance-related factor, traffic-loading factor of the unmanned aerial vehicle comprises, Determining three-dimensional space distance between the unmanned aerial vehicle and the neighbor unmanned aerial vehicle and corresponding path hop count; determining a distance correlation factor based on the three-dimensional spatial distance and the hop count; Determining a flow load factor based on the monitoring time interval, the number of data packets received by the neighbor unmanned aerial vehicle in the monitoring time interval and the average buffer queue data packet number; The monitoring time interval is determined by the adjacent control packets, and the spatial distance, the path hop count, the number of received data packets and the number of data packets are obtained by the interactive control packets.
6. The on-demand predicted unmanned aerial vehicle network routing method of claim 1, wherein the calculating the routing cost value for the candidate link, determining the optimal path to forward the data comprises, Determining a routing cost value based on the distance-related factor and the impact of the traffic load factor; and determining the link corresponding to the lowest routing cost value as the optimal path.
7. The on-demand predicted unmanned aerial vehicle network routing method of claim 1, wherein the process of finding a backup link to replace a failed optimal path comprises, Determining all paths possibly establishing connection with the neighbor unmanned aerial vehicle; and determining the paths with path hops smaller than the hop threshold and delay and/or packet loss rate within a preset range as standby links.
8. The on-demand predicted unmanned aerial vehicle network routing method of claim 1, wherein the process of performing path repair comprises, Inquiring a local routing table of the unmanned aerial vehicle; determining whether communication with the neighbor unmanned aerial vehicle is possible based on the query result; if the communication is not possible, continuing to search until the available path is successfully found.

Description

Unmanned aerial vehicle network routing method based on-demand prediction Technical Field The invention relates to the technical field of routing, in particular to an unmanned aerial vehicle network routing method based on demand prediction. Background Unmanned aerial vehicle ad hoc network communication is a network structure formed by unmanned aerial vehicle nodes in an ad hoc mode and is used for supporting efficient data transmission in a high-dynamic scene. The research in the field includes the problems of route optimization, link quality improvement, dynamic topology adaptability and the like so as to ensure stable communication of unmanned aerial vehicle groups in a complex environment, and a route technology is one of core technologies in network communication and is used for establishing an optimal path between network nodes so as to realize efficient transmission of data. For example, china patent publication No. 114390631A discloses a multipath routing protocol method for unmanned aerial vehicle ad hoc network mobility prediction, which comprises the steps of establishing an unmanned aerial vehicle mobile node system model according to an unmanned aerial vehicle mobile node system model, establishing a main backup route by adopting a multipath routing algorithm according to the established unmanned aerial vehicle mobile node system model, wherein the main backup route comprises a main route and a backup route, acquiring information of a node to be predicted, inputting the node information into the node mobility prediction model to obtain a mobility prediction result of the node, establishing a route list according to the prediction result, and executing route switching according to the route list node. For example, CN118075836A discloses a self-adaptive routing method of an NDN unmanned aerial vehicle self-organizing network based on topology prediction, which comprises the steps of independently controlling each unmanned aerial vehicle node based on the table driving characteristic of NLSR routing protocol, indirectly feeding back the state change frequency of neighbor unmanned aerial vehicle nodes and the dynamic update frequency of name prefixes in the network through the update frequency of LSDB, feeding back the link state of a wireless link, predicting the link state update frequency including the neighbor node state update frequency and the LSDB change frequency through a Holter double-parameter smoothing method, adaptively adjusting the link detection Hello message time interval and the LSDB state synchronization time interval according to the prediction result, reducing the LSDB state update time delay, and improving the route convergence speed. The invention can be applied to the field of large-scale unmanned aerial vehicle cluster communication, and is suitable for the ad hoc network communication requirements of special tasks of complex scenes such as disaster relief, detection and the like. There are problems in the prior art that, In practical situations, the existing unmanned aerial vehicle network routing method is difficult to adapt to the characteristics of high-speed movement and frequent topology change of the unmanned aerial vehicle, so that the reliability and efficiency of data transmission are obviously reduced, in addition, the high control overhead and link interruption problems caused by a flooding mechanism limit the stability and instantaneity of communication, and secondly, the unmanned aerial vehicle can only communicate with the unmanned aerial vehicle of the neighbor unmanned aerial vehicle, and in a network with high dynamic topology change, the unmanned aerial vehicle is difficult to learn the information of the unmanned aerial vehicle of the neighbor unmanned aerial vehicle. Disclosure of Invention Therefore, the invention provides an unmanned aerial vehicle network routing method according to the demand, which is used for solving the problems that the existing unmanned aerial vehicle network routing method is difficult to adapt to the characteristics of high-speed movement and frequent topological change of unmanned aerial vehicles, so that the reliability and efficiency of data transmission are obviously reduced, in addition, the high control overhead and link interruption problems caused by a flooding mechanism limit the stability and instantaneity of communication, and secondly, the unmanned aerial vehicle can only communicate with the unmanned aerial vehicle of the neighbor thereof, and in the network with high dynamic topological change, the unmanned aerial vehicle is difficult to learn the information of the unmanned aerial vehicle of the neighbor thereof. In order to achieve the above object, the present invention provides an unmanned aerial vehicle network routing method according to demand prediction, which includes: Determining state information of the neighbor unmanned aerial vehicle based on the improved Kalman filtering model, and predicting the