基于多维缩放和自适应加权迭代的WSN定位算法
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东南大学仪器科学与工程学院南京210096

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TP393; TN929.5

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国家自然科学基金(51979041,61973079)项目资助


Localization algorithm for wireless sensor networks based on multidimensional scaling and adaptive weighting iteration
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School of Instrument Science and Engineering, Southeast Universality, Nanjing 210096, China

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    摘要:

    针对基于测距的接收信号强度(RSSI)的定位方法存在的误差较大,以及针对基于复杂函数缩放(SAMCOF)的迭代方法的加权策略单一,无法适应距离量测值的不稳定性,影响定位精度的问题,提出了一种基于扩展卡尔曼滤波(EKF)和多维尺度(MDS)的自适应加权迭代算法。首先,利用EKF算法融合由RSSI得到的距离量测值和加速度信息,以获得优化后的距离状态量;然后,基于协方差矩阵中距离状态量的置信度动态调整不同通信节点对之间的权重,并采用优化的距离构建距离矩阵,进行多维标度定位(MDS-MAP),得到初始位置;最后,采用基于SMACOF的迭代优化方法对初始位置进行迭代,以消除不完全链路观测对定位精度的影响,从而获得更精确的位置估计。仿真实验表明无论在何种网络分布、通信半径、节点数量以及噪声水平下,所提出的定位算法相比MDS-MAP、vMDS、wMDS均表现出了更好的效果,提高了变化的网络中的定位技术精确性和鲁棒性。同时,基于ZigBee CC2530的定位系统的半物理实验结果验证了所提出算法在室内和室外场景上定位效果,克服了传统方法在复杂环境中的局限性。

    Abstract:

    To address the issues of the large errors in received signal strength indicator (RSSI)-based ranging methods and the limited adaptability of the iterative method based on the scaling by majorizing a complicated function (SAMCOF), which cannot adapt to the instability of distance measurements and degrading localization precision, this paper proposes an adaptive weighting algorithm based on the extended Kalman filter (EKF) and multidimensional scaling (MDS). The algorithm first fuses the distance measurements obtained from RSSI and acceleration information using EKF to obtain optimized distance states. Then, the weights for different communication node pairs are dynamically adjusted based on the confidence of the distance states in the covariance matrix, and an optimized distance matrix is constructed for MDS-MAP positioning to obtain the initial positions. Finally, the SMACOF-based iterative optimization method is employed to refine the initial positions, reduce the negative impact of incomplete link observations and enhance positioning accuracy. Simulation experiments show that the proposed localization algorithm outperforms MDS-MAP, vMDS, and wMDS in various network distributions, communication radii, node numbers, and noise levels, improving positioning precision and robustness in dynamic networks. Additionally, the semi-physical experiment results of the positioning system based on ZigBee CC2530 validate the effectiveness of the algorithm’s effectiveness in both indoor and outdoor scenarios, overcoming the limitations of traditional methods in complex environments.

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江美娟,刘锡祥,盛广润.基于多维缩放和自适应加权迭代的WSN定位算法[J].电子测量与仪器学报,2025,39(5):29-39

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  • 在线发布日期: 2025-07-04
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