Accelerated Algorithms of Distributed Estimation Based on Relative and Absolute Measurements
Xiangya Cao, Dunbiao Niu, Enbin Song, Tingting Wang · 2019
This paper discusses the problem of the least squares distributed estimation from relative and absolute measurements in sensor networks. Comparing with the state of art, we provide several algorithms to solve the problem by exploiting the special structure of the problem. Moreover, all of our provided accelerated algorithms not only allow each node to compute its own estimate by using only information that is directly available at the node itself or from its immediate neighbors, but also attain a faster rate of convergence. Furthermore, the conjugate gradient algorithm achieves the best performance among these algorithms. The numerical results indicate that our algorithms show their superiority in comparison with the existing algorithm.