Apply Modified Method of Nonlinear Optimization to Improve Localization Accuracy in WSN

As is known, location information is playing an important role in most application of WSN, and thus increasing the location accuracy is crucial to this application. However, most of the location refinement algorithms used at present cannot meet the requirement of WSN very well. The linear optimization can achieve small calculating amount at the cost of reducing the positioning accuracy. The traditional unconstrained nonlinear optimization has a better performance in accuracy but always demands large calculating amount. Basing on the principle of unconstrained nonlinear optimization and combining with the characteristics of WSN, this paper proposes two improved novel refinement algorithms: NSSD and MNSQN. Simulation results show that the algorithms proposed in the paper are efficient to relieve the contradiction between calculating amount and localization accuracy by improving the traditional algorithms. The two optimization methods have several advantages: high localization accuracy, relatively low calculating amount, without requiring extra communicational cost, etc.