Localization of the sensor nodes is a key supporting technology in wireless sensor networks (WSNs). In this paper, a real-time localization estimator of mobile node in WSNs based on extended Kalman filter (KF) is proposed. Mobile node movement model is analyzed and online sequential iterative method is used to compute location result. The detailed steps of mobile sensor node self-localization adopting extended Kalman filter (EKF) is designed. The simulation results show that the accuracy of the localization estimator scheme designed is better than those of maximum likelihood estimation (MLE) and traditional KF algorithm.
Spectrum sensing is the first step of cognitive radio (CR). In this area, previous researches mostly consider distributed local nodes which are under identical channel conditions, hence uniform and fixed detection threshold is set with energy detector. However, the distributions of nodes in real environments are not quite the same. In this paper, the optimal threshold to minimize the total detection error over additive white Gaussion noise (AWGN) channel is derived firstly. Then the dynamic threshold scheme is proposed to reduce the average total detection error. Simulations have shown that, with this scheme, sensing performance is improved.
针对现存的基于通信的列车控制系统(Communication Based Train Control,CBTC)的无线链路在隧道中由于多径效应导致的不可靠,本文对CBTC无线通信采用的802.11g的OFDM技术进行分析,提出一种多子载波冗余传输方法。数值仿真分析表明,该方法从物理层上加强了无线链路通信的可靠性。
