Sensor Network Localization Using Least Squares Kernel Regression

This paper considers the sensor network localization problem using signal strength. Unlike range-based methods signal strength information is stored in a kernel matrix. Least squares regression methods are then used to get an estimate of the location of unknown sensors. Locations are represented as complex numbers with the estimate function consisting of a linear weighted sum of kernel entries. The regression estimates have similar performance to previous localization methods using kernel classification methods, but at reduced complexity. Simulations are conducted to test the performance of the least squares kernel regression algorithm. Finally, the paper discusses on-line implementations of the algorithm, methods to improve the performance of the regression algorithm, and using kernels to extract other information from distributed sensor networks.