An Improved Localization Algorithm in Wireless Sensor Networks Under Complex Background Noise

To further improve the precision of target localization in the wireless sensor network(WSN), a novel Cubature particle filter algorithm was proposed. Firstly it utilized least square method to estimate the initial target position, the Cubature Kalman filter and Gauss-Newton rule were used to generate more accurate importance density function subsequently by virtue of the measurement updated state variable, which provided proposal distribution for particle filter. The simulation experiment results demonstrate that the proposed algorithm can achieve better locating accuracy and convergence speed under complex background noise, compared with standard PF、 EPF and UPF.