中文摘要：

为了减4、多跳距离估计误差对三维无线传感器网络节点定位的影响，提出了一种结合BP神经网络的多跳定位（BPL）算法。建立了BP神经网络模型，用来修正不相邻节点间的多跳距离估计值；根据锚节点间的位置关系提取样本对其进行训练，使其能够反映三维无线传感器网络几何结构的主要特性，未知节点利用训练好的BP神经网络估计出自身到一定跳数范围内锚节点的距离，并求出自身三维坐标。仿真结果表明，BPL算法可以有效降低多跳距离估计误差，提高传感网的定位精度。在节点稀疏部署的网络环境中，BPL算法具有更加明显的优势。

英文摘要：

To reduce the impact of the multi-hop distance estimation errors on the performance of localization in 3D wireless sensor networks, a BP neural network based localization （BPL） algorithm is proposed. The BPL algorithm constructs a BP neural network model to correct the multi-hop estimative distances of non-adjacent nodes and extracts the training samples of the BP network according to the location relationships between pairwise anchor nodes. After a training procedure, the BP neural network can reflect the main property of global space structure of 3D wireless sensor networks. Each unknown node uses the trained BP network to estimate its Euclidean distances to anchor nodes within the range of a certain hop count and then calculates its 3D coordinates. Numerous simulations show that the multi-hop estimative distances and the localization accuracy can be effectively improved by the BPL algorithm, especially in the sparse networks.