| At present,with the development of unmanned aerial vehicle(UAV)technology,the market demand of UAV has experienced a dramatic rise.UAV was initially applied in the military field,and widely used in civil fields later,including express transportation,disaster monitoring,agricultural production and so on.A single UAV has poor survivability and it is unable to complete complex tasks,so a number of UAVs cooperate to complete tasks in the form of UAV swarm.As the key technology of UAV technology,localization has attracted wide attention.Nowadays,global position system(GPS)is often used to locate UAV.However,there is a large error of localization relying solely on GPS,especially in the height measurement;and as a line-of-sight(LOS)technology,GPS is easily fail to work due to signal occlusion.In this context,this paper takes the positioning algorithm of UAV swarm as the research object,and proposes a cooperative positioning algorithm based on multidimensional scaling(MDS).Firstly,the UAV swarm is clustered into patches;secondly,the UAVs in each patch are positioned relatively;finally,the patches are merged to obtain the global coordinates of all UAVs,and the absolute coordinates are obtained through coordinate transformation.There are three innovations in this paper as follows.1.Considering the high dynamic of UAV swarm,this paper proposes a dynamic clustering scheme based on the degree centrality.UAV network is different from traditional wireless sensor network(WSN)because of its high dynamic.Therefore,dynamic clustering scheme needs to be adopted accordingly.In this paper,the degree centrality of UAV is used as the clustering basis,and the nodes with higher degree centrality are selected as the cluster heads to divide the UAV network.2.Considering the drawbacks of the existing localization algorithm based on MDS,an accurate real-time localization algorithm for UAV swarm is proposed.Firstly,this paper studies the localization algorithms of WSN based on MDS,and analyzes the shortcomings of the existing algorithms from both theoretical and simulation perspectives.Then,in the case of sparse connection of UAV swarm,the range-based localization algorithms are not accuracy enough,and the angle measurements are introduced to improve the localization accuracy.Finally,the power method and Nystr(?)m approximation are used to reduce the complexity of matrix decomposition to improve the real-time performance of the algorithm.The relative coordinates of UAVs in each patch can be obtained by this step.3.According to the clustering scheme,this paper proposes a corresponding merging scheme.The scheme merges each patch according to the order of clustering,and gets the global coordinates of all UAVs.After all the patches are merged,the global coordinates are transformed into absolute coordinates based on the available GPS coordinates,so as to realize the absolute positioning of the UAV swarm.If GPS-available UAVs are less than 4in the swarm,the relative positioning of UAV swarm can be realized.Finally,a simple demonstration based on UWB module and GPS module is given to verify the effectiveness of MDS-based cooperative positioning algorithm. |
