Dynamic Ad Hoc Network Localization Using Online Least Squares Kernel Subspace Methods

In this paper we apply complex least squares kernel subspace methods to the problem of ad hoc network localization. We use Gaussian kernels and a neighborhood kernel to estimate the locations of mobile nodes. Our algorithms do not require preprocessing of raw data like other statistical methods. Furthermore they use one-step regression directly, instead of existing two-stage classification methods, and work on a fairly small subset of training data. These salient features allow our algorithms to successfully solve the dynamic localization problem with low communication and computational costs. Simulation of ad hoc networks with random node movement demonstrates the success of the algorithms. The methods and algorithms can also be applied in other applications like target tracking and sensor data representation