An improved hybrid Time of Arrival (ToA)/ Angle of Arrival (AoA) location algorithm by adopting Gauss-Newton iterative algorithm is proposed. It is with the advantage of fast convergence and combining with the grid-se...An improved hybrid Time of Arrival (ToA)/ Angle of Arrival (AoA) location algorithm by adopting Gauss-Newton iterative algorithm is proposed. It is with the advantage of fast convergence and combining with the grid-search-based method to optimize the initial object coordinates of the iteration, meanwhile, under the condition of small measurement errors caused by noises of ToA and AoA, the algorithm performance can be improved effectively. In the Non-Line-of-Sight (NLoS) environments of the Wireless Sensor Network (WSN), simulation results show that improved accuracy is gained with moderate flexibility and fast steady convergence compared with the existing algorithms.展开更多
Coordinate descent method is a unconstrained optimization technique. When it is applied to support vector machine （SVM）, at each step the method updates one component of w by solving a one-variable sub-problem while...Coordinate descent method is a unconstrained optimization technique. When it is applied to support vector machine （SVM）, at each step the method updates one component of w by solving a one-variable sub-problem while fixing other components. All components of w update after one iteration. Then go to next iteration. Though the method converges and converges fast in the beginning, it converges slow for final convergence. To improve the speed of final convergence of coordinate descent method, Hooke and Jeeves algorithm which adds pattern search after every iteration in coordinate descent method was applied to SVM and a global Newton algorithm was used to solve one-variable subproblems. We proved the convergence of the algorithm. Experimental results show Hooke and Jeeves＇ method does accelerate convergence specially for final convergence and achieves higher testing accuracy more quickly in classification.展开更多
基金supported by National Natural Science Foundation of China under Grant No.61172073State Key Laboratory of Networking and Switching Technology (Beijing Universityof Posts and Telecommunications) under Grant No.SKLNST-2009-1-09+1 种基金Open Research Fund of National Mobile Communications Research Laboratory, Southeast University, P. R.ChinaChina Fundamental Research Funds for the Central Universities:Beijing Jiaotong University
文摘An improved hybrid Time of Arrival (ToA)/ Angle of Arrival (AoA) location algorithm by adopting Gauss-Newton iterative algorithm is proposed. It is with the advantage of fast convergence and combining with the grid-search-based method to optimize the initial object coordinates of the iteration, meanwhile, under the condition of small measurement errors caused by noises of ToA and AoA, the algorithm performance can be improved effectively. In the Non-Line-of-Sight (NLoS) environments of the Wireless Sensor Network (WSN), simulation results show that improved accuracy is gained with moderate flexibility and fast steady convergence compared with the existing algorithms.
基金supported by the National Natural Science Foundation of China (6057407560705004)
文摘Coordinate descent method is a unconstrained optimization technique. When it is applied to support vector machine （SVM）, at each step the method updates one component of w by solving a one-variable sub-problem while fixing other components. All components of w update after one iteration. Then go to next iteration. Though the method converges and converges fast in the beginning, it converges slow for final convergence. To improve the speed of final convergence of coordinate descent method, Hooke and Jeeves algorithm which adds pattern search after every iteration in coordinate descent method was applied to SVM and a global Newton algorithm was used to solve one-variable subproblems. We proved the convergence of the algorithm. Experimental results show Hooke and Jeeves＇ method does accelerate convergence specially for final convergence and achieves higher testing accuracy more quickly in classification.
