无人机集群分布式导航的几何构型优化方法

Geometric configuration optimization method for distributed navigation of UAV clusters

现有的无人机集群定位方法多依赖卫星导航或高精度锚机,然而当处于卫星信号干扰地区时,集群整体定位精度将受到严重影响。因此,集群几何构型研究对不依赖卫星和高精度锚机的分布式无人机集群协同导航技术至关重要。为了解决分布式集群定位精度提升和构型优化的问题,提出了一种针对分布式集群的几何构型寻优方法。首先计算节点位置精度因子(PDOP),提出了包含集群定位精度最高准则、个体定位精度均衡准则和通信间距均衡准则的分布式无人机集群最优几何构型评价准则,实现集群构型整体式优化。其次,使用LambdaLR函数对粒子群优化(PSO)算法速度更新公式中的惯性权重和学习因子进行改进,基于上述算法估算并优化集群整体定位精度。仿真结果表明,所提方法在不同规模的集群构型寻优问题中均具有较高的鲁棒性,且满足分布式大规模集群构型优化解算的实时性需求,改进PSO算法相较标准PSO的节点PDOP均值均有3.31%~8.54%的优化效果。

Existing methods for positioning UAV clusters often rely on satellite navigation or high-precision anchors.However,when the UAV cluster operates in areas with satellite signal interference,the overall positioning accuracy of the cluster can be severely affected.Therefore,research on cluster geometric configuration is crucial for distributed UAV cluster collaborative navigation technologies that do not rely on satellites or high-precision anchor machines.To address the issues of improving the positioning accuracy and optimizing the configuration of distributed clusters,a method for optimizing the geometric configuration of distributed clusters is proposed.First,the position dilution of precision(PDOP)factor of node positions is calculated.Then,a set of evaluation criteria for the optimal geometric configuration of distributed UAV clusters is proposed,including criteria for maximising cluster positioning accuracy,balancing individual positioning accuracy,and balancing communication distances.These criteria are used to achieve a holistic optimization of the cluster configuration.Next,the LambdaLR function is used to improve the velocity update formula of the particle swarm optimization(PSO)algorithm by adjusting the inertia weight and learning factor.Based on this algorithm,the overall positioning accuracy of the cluster is estimated and optimized.The simulation results show that the proposed method exhibits high robustness in optimizing cluster configurations of different scales and meets the real-time requirements for distributed large-scale cluster configuration optimization solutions.The improved PSO algorithm achieves optimization effects ranging from 3.31%to 8.54%in terms of the average node PDOP compared to standard PSO.