中文摘要：

当节点初始坐标精度较差时，大多数基于负梯度搜索的最小二乘类迭代定位算法容易陷入局部最优，产生较大的定位误差。作者通过引入网络部署时先验的限制性条件，提出了一种基于软约束模式的加权最小二乘节点定位算法（SCLS）。该算法根据2跳邻居节点间必须满足的最小和最大测距限制性条件，在加权最小二乘优化代价函数中引入惩罚项，迫使负梯度搜索往节点真实位置方向前进，从而提高定位算法精度。仿真实验结果显示，SCLS定位算法精度明显优于经典加权最小二乘定位算法。在测距误差较大或节点初始坐标精度较低情况下，SCLS算法具有良好鲁棒性。

英文摘要：

A robust localization approach was proposed which employed weighted least squares scaling with soft constrains （SCLS）. It incorporates the a priori deployment constraints, i.e., minimum and maximum node separation among 2-hop nodes, into localization as soft constraints and penalizes pairs of 2-hop nodes whose assigned coordinates violate the minimum and maximum constraints. Combined with applying statistical filtering of ranging measurements, the location estimates were evidently improved compared with classical least squares scaling. For received signal strength based range measurements, extensive simulation results confirm that this localization scheme outperforms classical least squares scaling （LLS） and is resilient against large ranging errors and sparse range measurements, which are common in wireless sensor network deployments.