Globally Convergent Localization in Sensor Networks

定位是指一组传感器使用与它们相对于源的位置相关的信息来估计源的精确位置。该信息可以是距离、方位、功率水平和到达时间差。定位是许多新兴应用的基本组成部分，包括生物威胁的检测、普适计算，其中定位打印机和计算机允许计算机将其打印作业发送到最近的打印机，在传感器网络中，各个传感器必须知道它们自己的位置，以及一个新兴的数十亿美元的无线本地化行业。确保本地化以高效和时间关键的方式发生是很重要的。这反过来又取决于传感器是如何部署的。因此，本研究考虑的问题，传感器的部署，以及开发计算效率高的算法，利用这种部署，以实现高效和时间关键的定位。先前的工作仅从可用于每个源的相对位置测量的数量的角度考虑这一点。这种方法在很大程度上忽略了执行定位的实际算法的特征。一般来说，线性定位算法具有较差的噪声性能，相反，最好考虑可能具有假静止点的非线性算法。因此，研究者将提供传感器周围的非平凡区域，使得如果源位于这些区域内，则定位算法全局收敛。

Localization refers to a set of sensors estimating the precise location of a source using information related to their relative position to the source. This information can be distance, bearing, power level, and time difference of arrival. Localization is a fundamental component of a number of emerging applications, spanning the detection of biological threats, pervasive computing, where locating printers and computers permits a computer to send its print job to the nearest printer, in sensor networks, where individual sensors must know their own positions, and an emerging multibillion dollar wireless localization industry.It is important to ensure that localization occurs in an efficient and time critical manner. This in turn depends on how the sensors are delpoyed. Thus, this study considers the issue of sensor deployment, as well as the development of computationally efficient algorithms that exploit this deployment to achieve efficient and time critical localization. Prior work considers this only from the perspective of the number of relative position measurements that are available for each source. This approach largely ignores the characteristics of the actual algorithms that perform localization. In general linear localization algorithms have poor noise performance, and instead it is better to consider nonlinear algorithms that may have false stationary points. Accordingly, the investigator will provide nontrivial regions surrounding sensors such that should the source lie within these regions then the localization algorithms are globally convergent.