一种简化鲁棒的传感网络节点三维估计算法

针对无线传感网络中进行节点三维状态估计时受到重尾或突变性质噪声干扰的问题，提出了加权质心定位和简化最大互相关熵无迹卡尔曼滤波结合的传感网络节点三维估计算法．首先，通过信号强度的测距方式得到信标节点和传感节点的观测距离；然后，利用质心定位的方法得到节点的近似估计，并结合节点估计模型和最大互相关熵准则对非高斯、非线性问题的鲁棒性，推导出一种简化最大互相关熵无迹卡尔曼滤波算法；最后，得到精确估计．仿真结果表明，新算法在具有重尾非高斯观测噪声的传感网络中对节点三维估计的效果比典型的方法更好，不仅降低了一般最大互相关熵无迹卡尔曼滤波的时间复杂度，还提高了节点估计的精度．

Simplified and robust algorithm for three-dimensional estimation of nodes in sensor networks

A node three-dimensional estimation algorithm that combines the weighted centroid localization algorithm and simplified maximum correntropy unscented Kalman filter is proposed for solving the problems that the observation noise, which appears in estimating the three-dimensional states of the nodes, is heavy-tailed or has some sudden change in the wireless sensor network. First, the algorithm obtains the observation distance of beacon nodes and sensor nodes by using the method of signal strength ranging and gets the approximate estimation of nodes with the centroid localization method. And then a simplified maximum correntropy unscented Kalman filter algorithm is deduced by combining the node estimation model and the robustness of the maximum correntropy criterion for non-Gaussian and nonlinear problems. Finally, the accurate estimation is obtained by using it. Simulation results show that the new algorithm has a better performance for the three-dimensional state estimation of nodes than the classic methods in sensor networks with heavy-tailed non-Gaussian noise. It not only reduces the time complexity of the general maximum correntropy unscented Kalman filter, but also improves the accuracy of node estimation.