基于RSS阈值模型的Amorphous算法定位误差抑制

针对无线传感器网络定位算法在不同的通信模型下误差较大的问题，在Amorphous算法离线计算网络平均连通度的基础上，建立了四种RSS阈值模型来抑制Amorphous算法在不同通信模型下的定位误差。由不同阈值模型得到的阈值在不同程度上修正了算法的梯度值，使定位误差得到抑制。仿真结果表明，Amorphous算法在Regular模型下的最优阈值模型从Regular阈值模型和Log-normal阈值模型中选择；算法在Log-normal模型下的最优阈值模型从Regular阈值模型、Log-normal阈值模型、DOI阈值模型和RIM阈值中选择；算法在DOI模型和RIM模型下的最优阈值模型从Log-normal阈值模型、DOI阈值模型和RIM阈值模型中选择，最后得到Amorphous算法在不同的通信模型、通信半径和不规则度下对应的最优阈值模型。

RSS threshold model-based positioning error suppression of Amorphous algorithm

For the problem that the positioning algorithm has a large error in wireless sensor networks under different communication models, based on the average network connectivity of the off-line computation of Amorphous algorithm, four RSS (Received Signal Strength) threshold models are built up to suppress the positioning error of Amorphous algorithm under different communication models. The threshold obtained from different threshold models can be utilized to modify the gradient of the algorithm to different extents, so that the positioning error is suppressed. Simulation results show that, when Amorphous algorithm passes through the Regular model, the optimal threshold model is selected between Regular threshold model and Log-normal threshold model. When the algorithm passes through the Log-normal model, the optimal threshold model is selected among Regular threshold model, Log-normal threshold model, DOI threshold model and RIM threshold model. When the algorithm passes through the DOI model and RIM model, the optimal threshold model is selected among Log-normal threshold model, DOI threshold model and RIM threshold model. Finally, the optimal threshold model of Amorphous algorithm under different communication models, communication radiuses and irregularity degrees can be obtained.