中文摘要：

在无线传感器网络中，基于测距的无线定位方法通常基于各类测距模型，以最小二乘法估计位置初值，再利用优化算法提高定位精度。由于测距过程受到各类噪声及其分布变化的影响，在低信噪比情况下传统优化算法存在精度降低、收敛性差等性能恶化的问题，通常导致估计的结果不是最优。针对这一问题，将信赖域算法用于迭代优化过程，使用锥模型函数逼近定位目标函数，将目标函数的优化过程转变为一系列最优化子问题。仿真结果表明，该方法性能稳定，收敛速度快，在低信噪比环境下比传统算法具有更好的定位性能。

英文摘要：

In traditional localization methods based on distance measurement in wireless sensor networks, a lot of optimization algorithms are employed to improve the precision of location. However, the optimization performance may degrade because of the low SNR environment and the non-Gaussian noise distribution. To overcome this problem, the trust region （TR） algorithm based on the conic model is employed for iterative localization. The cone model is used for approximating the objective function of localization, and the optimization process is converted into a series of optimization sub-problems. Simulation results show that the TR based method acquires better performance, especially in low SNR environment.