Node localization is one of the key technologies in the wireless sensor network research field, which is crucial to the high-accuracy localization of mobile nodes, but the positioning error of traditional algorithms such as received signal strength indicator and angle of arrival is more than 4 m, which has almost no practical value. For example, the localization accuracy of the localization algorithm based on received signal strength indicator will be reduced sharply when affected by signal reflection, multipath propagation, and other interference factors. To solve the problem, a three-dimensional localization algorithm of mobile nodes was proposed in this article based on received signal strength indicator–angle of arrival and least-squares support-vector regression, which fused the ranging information of received signal strength indicator algorithm and the angle of arrival algorithm and optimized the estimated distance of unknown nodes. Next, the mobile node model and least-squares support-vector regression modeling mechanism were built according to the hop count of the shortest distance between nodes. Finally, the unknown mobile nodes were localized based on least-squares support-vector regression modeling. The experimental results showed that compared with the localization algorithms without optimized ranging information or least-squares support-vector regression modeling, the algorithm proposed in this study exhibited significantly improved stability, a reduced mean localization error by more than 50%, and increased localization accuracy.

节点定位是无线传感器网络研究领域的关键技术之一，对移动节点的高精度定位至关重要，但接收信号强度指标、到达角等传统算法的定位误差大于4 m，几乎没有实用价值。例如，基于接收信号强度指标的定位算法在受到信号反射、多径传播等干扰因素影响时，其定位精度会急剧下降。针对该问题，本文提出了一种基于接收信号强度指标——到达角和最小二乘支持向量回归的移动节点三维定位算法，融合接收信号强度指示算法的测距信息和到达角算法，优化未知节点的估计距离。其次，根据节点间最短距离的跳数建立移动节点模型和最小二乘支持向量回归建模机制。最后，基于最小二乘支持向量回归建模对未知移动节点进行定位。实验结果表明，与没有优化测距信息或最小二乘支持向量回归建模的定位算法相比，本研究提出的算法稳定性显着提高，平均定位误差降低50%以上，定位精度提高。 .