A Multilevel Filter Based Variable Feasible Region Projection for Wireless Sensor Networks

In recent years, the wireless sensor network (WSN) has been widely applied to a variety of applications. Localization and tracking of a mobile node in a mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environment is one of the most main application fields of WSN technology. In this paper, we unite a square-root unscented Kalman filter (SRUKF) and a particle filter (PF). We develop a PF-SRUKF-PF multistage filter with a variable constraint. First, we use PF to preprocess the measured data. Then SRUKF is used to operate on the processed data, and after the operation, the feasible domain is adjusted according to the number of sigma points outside the feasible domain, and then the results are projected into the feasible domain. The projected results are passed to the PF for secondary filtering. Through simulations, we show that the proposed algorithm can achieve higher localization accuracy compared with other methods.