曲指数族流形在无线传感器网络定位中的应用

利用信息几何中的统计流形理论和自然梯度流形学习定位方法，研究了基于接收信号强度（RSS）的无线传感器网络自定位问题.首先，通过概率密度函数构造了一个曲指数族定位模型；然后，针对给定初始状态值的未知目标节点定位问题，结合梯度下降法，提出了基于此模型的最优非线性估计方法及其改进算法.梯度下降法的良好性质和仿真结果表明，这些算法有很好的收敛效果和更高的定位精度. 

Curved Exponential Family Manifold for Localization in Wireless Sensor Networks

Using statistical manifold theory in information geometry and natural gradient manifold learning localization method, the self-localization problem of wireless sensor networks based on received signal strength (RSS) was studied. First, a curved exponential family localization model was constructed according to probability density function. Then, aiming at the problem of locating unknown target nodes with given initial state values, combining gradient descent method, an optimal non-linear estimation method based on this model and its improved algorithm were proposed. The good properties of gradient descent method and simulation results show that these algorithms possess better convergence effect and higher positioning accuracy.