Application of covariance cross in distributed sensor network positioningAuthor(s): Xiang-Yang Chen
Node localization accuracy in many applications in a distributed sensor network plays a vital role. Currently positioning method is more concerned mainly include TDOA and RSS. These two methods are non-independent and positioning accuracy susceptible to noise. If using the traditional manner fusion Kalman filter data, you can reduce the estimation error. But assumes zero covariance between data, so the results are not conservative and reliable. This article will cross covariance data fusion algorithm is applied to such problems, namely in the Poisson distribution and uniform distribution of node localization process distributed sensor network to be simulated. The results show that the algorithm is more reliable crosscovariance and improves positioning accuracy, ideal for distributed sensor networks.