無線感測網路(WSNs) 是由具有感測能力、計算能力及無線通信能力的感測器所組成，這些感測器具備著體積小、低成本、低功耗的特性，在感測器有著前述的特性之下，本篇研究是以接收訊號強度(RSS) 等數據並且對其做量化(Quantization)，處理中心(Fusion center) 以此量化訊號(Quantized data) 並利用最小平方估計法做定位，當量化階數少的時候，為了降低量化誤差造成目標函式過多的誤差，在最小平方估計裡加入μ ? law 壓縮原始訊號，得到不錯的效果；這裡的問題屬於非線性最小平方估計，研究中也依據高斯-牛頓法以及NM-單純形搜索法解決此最佳化問題；在量化臨界值(Quantization threshold) 上，利用Numirical 方式提出適合最小平方估計的最佳量化臨界值，其均方根(Root Mean Square, RMS)位置誤差也接近MLE 以及CRLB；此外，並延伸出找到適合所有位置的量化臨界值，使用在此最小平方估計問題中。Wireless sensor networks (WSNs) conventionally consist ofa large number of low-cost, low-power, densely distributed, and mostly heterogeneous sensors. For the localization application, the target signal strength in a WSN is usually reported by sensors with quantized levels and all quantized data are collected in a fusion center to estimate the targetlocation based on a nonlinear relationship between distance and signal strength. Instead of using the computation-intensive maximum likelihood (ML) method, we study the least squares method by which the least squares cost function is significantly deteriorated due to nonlinear parameter estimation. To solve this problem, the μ-law compression technique is considered for robust position estimation. Two nonlinear least squares estimation methods, Gauss-Newton and Nelder-Mead, are discussed in our work. Numerical results show that the proposed method can achievea good mean square error performance close to the ML method with lower computation loading.
