中文摘要：

位置感知是智能空间中的重要技术．在分析了现有基于移动自组网的位置感知方法后，提出了一种基于多维定标（multidimensional scaling，简称MDS）的新的位置感知方法——SSOLA（smart space oriented location awareness method），可以对智能空间中的无线通信节点进行精确定位，获得节点之间的相对位置（坐标）；当有极少数位置已知的锚节点（2维定标存在3个以上锚节点，3维定标存在4个以上锚节点）时，可以得到全网所有节点的绝对位置（坐标）．SSOLA算法的设计思想是：以MDS分析为核心，采用Euclidean测距方法计算节点间距离矩阵，采用1跳和2跳局部图相结合的自适应选择机制构建局部图，各节点独立计算局部图，最终合成全局位置图．此外，SSOLA还可以与OLSR路由算法相融合，从整体上减轻了SSOLA算法的执行开销，提高了定位效率．仿真实验结果表明，SSOLA具有对锚节点依赖小、定位精度高、可扩展性好、执行速度快等优点，对原始测量误差也有较强的鲁棒性，可以应用于战术互联网、智能战场等大规模无线通信环境中．

英文摘要：

Smart space is a result of pervasive computing embodying the integration of computer, communication and digital media technology, which makes it possible to integrate the physical world and the virtual world in the information space together as a whole. Location awareness is a key technology of smart space, and is the basic service needed by other applications. Multidimensional scaling （MDS） is a technique in mathematical psychology, which can the distance or dissimilarity measures between points and produce a representation of the data in a small number of dimensions. In the paper, MDS is used to derive node locations that fit those estimated distances, and a smart space oriented location awareness method （SSOLA） is proposed, which can position all the nodes of the networks accurately only by means of the connectivity information--who is within communications range of whom. Provided with known positions for several anchor nodes, the absolute positions for all nodes can be got by SSOLA. Simulation studies demonstrate that SSOLA is more robust to measurement error, and has less positioning error, less time cost and better scalability than previous proposals in the same conditions. Furthermore, it can achieve comparable results using much fewer anchor nodes than previous methods, and even yields relative coordinates when no anchor nodes are available. SSOLA can be used in large and heavy traffic wireless environment, such as intelligent battlefield, tactical internet, etc.