基于改进的鲸鱼优化迭代算法的水下传感器网络节点定位方法  

Underwater Sensor Network Node Locator Based on Improved Whale Optimization Iterative Algorithm

作　　者：向丹 翟晨凯 林利彬 何登玉 王惠华 高攀 邱海洋 XIANG Dan;ZHAI Chenkai;LIN Libin;HE Dengyu;WANG Huihua;GAO Pan;QIU Haiyang(College of Information and Communication Engineering,Guangzhou Navigation College,Guangzhou 510725,China;College of Automation,Guangdong Polytechnic Normal University,Guangzhou 510665,China;School of Mechanical and Electrical Engineering,Guangzhou University,Guangzhou 510006,China;College of Electronic Information,Guangdong Polytechnic Normal University,Guangzhou 510665,China;Industrial Center,Guangdong Polytechnic Normal University,Guangzhou 510665,China;School of Naval Architecture and Ocean Engineering,Guangzhou Navigation College,Guangzhou 510725,China)

机构地区：[1]广州航海学院信息与通信工程学院,广州510725 [2]广东技术师范大学自动化学院,广州510665 [3]广州大学机械与电气工程学院,广州510006 [4]广东技术师范大学电子信息学院,广州510665 [5]广东技术师范大学工业中心,广州510665 [6]广州航海学院船舶与海洋工程学院,广州510725

出　　处：《华南师范大学学报（自然科学版）》2024年第2期119-128,共10页Journal of South China Normal University(Natural Science Edition)

基　　金：国家自然科学基金项目(52101358);广东省自然科学基金项目(2022A1515010127);广东省普通高校重点专项项目(2023ZDZX3017);广州市教育局高校科研项目(202234607)。

摘　　要：针对水下无线传感器网络中锚节点较少、迭代误差大导致节点定位精度低的问题,文章提出了一种基于改进的鲸鱼优化-牛顿迭代的水下三维节点定位算法(Improved Whale Optimization-Newton Iteration,IWONI)。该算法首先使用牛顿迭代算法对节点距离远近关系建立对应法则,并利用目标位置估计值和修正因子为改进的鲸鱼优化算法提供动态搜索区域;其次,建立以测量误差为权重的适应度函数作为判断基准,采用改进的鲸鱼优化算法进行迭代求解,以获得最优解;最后,利用定位方程得到网络节点位置。为了验证IWONI算法的性能,将IWONI算法与时间差定位算法(TDOA-CHAN、TDOA-Taylor)、测距定位算法(最小二乘法、高斯牛顿迭代法)和牛顿迭代算法进行定位误差、收敛性能和定位成功率对比实验,并验证了节点数量对定位精度的影响。实验结果表明:(1)IWONI算法的定位误差和收敛速度明显优于其他对比算法。(2)IWONI算法在测量噪声大时的定位成功率高达92%,明显优于其他对比算法。(3)在通信半径不变的情况下,选择5~7个传感器节点可以在IWONI算法中实现定位精度与成本开销的平衡。To address the issues of low node localization accuracy caused by the limited number of anchor nodes and large iteration errors in underwater wireless sensor networks,an improved whale optimization-Newton iteration(IWONI)algorithm for underwater three-dimensional node localization was proposed.IWONI first uses the Newton iteration algorithm to establish a corresponding rule for the distance relationship between nodes,and utilizes the estimated target position and correction factor to provide a dynamic search area for the improved whale optimization algorithm.Secondly,a fitness function weighted by measurement error is established as the judgment criterion,and the improved whale optimization algorithm is used for iterative solution to obtain the optimal solution.Finally,the network node positions are calculated through the localization equation.To validate the performance of the IWONI algorithm,comparative experiments were conducted on localization error,convergence performance,and localization success rate against time difference of arrival algorithms(TDOA-Taylor,TDOA-CHAN),ranging algorithms(least squares method,Gauss-Newton iteration),and Newton iteration algorithm.The impact of the number of nodes on localization accuracy was also investigated.The comparison results show that:(1)The IWONI algorithm has significantly lower localization error and faster convergence speed than other compared algorithms.(2)The IWONI algorithm has a high localization success rate of 92%even in the presence of high measurement noise,which is significantly better than other compared algorithms.(3)In the case of a constant communication radius,employing 5 to 7 sensor nodes can achieve a balance between localization accuracy and cost effectiveness in the IWONI algorithm.

关 键 词：水下无线传感器网络 三维节点定位 鲸鱼优化 牛顿迭代算法 

分 类 号：TN929.3[电子电信—通信与信息系统] TP212.9[电子电信—信息与通信工程]