Localization in Wireless Sensor Networks with Nonlinear Suboptimal Recursion and Simplex Search Methods

Wireless sensor networks (WSNs) conventionally consist of a large number of low-cost, low-power, densely distributed, and mostly heterogeneous sensors. For the localization application, the target signal strength (RSS) in a WSN is usually reported by sensors with quantized levels and all quantized data are collected in a fusion center to estimate the target location 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 cost function of least squares is significantly deteriorated due to nonlinear parameter estimation. To solve this problem, some signal compression technique is considered for robust position estimation. Two nonlinear least squares estimation methods, Gauss-Newton and Nelder-Mead, will be explored in this work. Some nummerical experiments and analysis will be studied for the proposed method from the aspects of the mean square error estimation performance and computational complexity, compared with the ML method.