Efficient node self-localization in large ad-hoc wireless networks using interlaced particle filters

Large self-organizing networks of wireless devices have shown potential in many emerging applications. While knowing the physical location of devices in enhances and enables many such applications, the locations of some or most of the wireless devices may be unknown and the devices may be mobile. Several recent works have examined using distance or RSS measurements to estimate the position of such wireless devices. In this work we examine methods to efficiently estimate and track the positions of a subset of mobile wireless nodes in the network and consider how well performance of these algorithms scale with network size. We consider position estimate error and convergence rate of these methods as the size of the network increases. We use simulations to show that a new localization method using interlaced particle filters converges faster and has higher estimation accuracy than existing algorithms.