【摘要】 在无线传感器网络定位中,基于RSS测量的定位方法是最常用的方法之一。由于传统的最大似然估计(MLE)算法的目标函数具有非线性和非凸性,在应用于无线传感器网络定位时,会产生多个局部最优值。针对该问题提出一种基于半定规划(SDP)的凸优化定位方法。首先采用泰勒级数近似对目标函数进行线性化处理,然后通过引入冗余变量将原无约束优化问题转化为约束优化问题,最后应用半定松弛(SDR)技术将约束优化问题转化为半定规划(SDP)凸优化问题进行求解。通过仿真实验的比较,说明本文提出的优化算法在定位精度、鲁棒性方面优于已有算法。更多还原

【Abstract】 Received signal strength(RSS)based localization is one of the common methods used in wireless sensor networks(WSNs)localization.Due to the non-linearity and non-convexity of the objective function,the traditional maximum likelihood estimator(MLE)can converge to local optimum when applied to WSNs localization.To overcome this problem,we propose a semi-definite programming(SDP)based localization algorithm.Firstly,the Taylor-series approximation is employed to linearize the objective function.Secondly,auxiliary variables are introduced to transform the original problem into a constraint optimization problem.Finally,the semi-definite relaxation(SDR)is applied to transform this constraint optimization problem into a SDP convex optimization problem.Comparison of simulation results shows that the proposed algorithm has higher localization accuracy and is more robust than the existing algorithms.更多还原