Positioning of Wireless Sensor Networks Based on Semidefinite Relaxation Optimization

Aiming at the problem of positioning accuracy analysis of wireless sensor nodes in complex environments, the semi-definite relaxation optimization estimation algorithm based on the Kalman filter (SC-SDP) is proposed to achieve the accurate estimation of the node position in a wireless sensor network. This paper proposes a positioning technology based on semi-definite relaxation optimization estimation, establishes a system model and asks questions, and re-elaborates the optimization problem by finding the lower bound of the initial non-convex objective function. The nonlinear and nonconvex problems are relaxed and optimized separately, and suboptimal solutions are obtained. The unscented Kalman algorithm is used to filter the noise, and a filter that captures the true mean and covariance more accurately is obtained in the paper. The traceless transformation can make the Gaussian inputting signal accurate to the third order, and the non-Gaussian inputting signal to the second order. The experimental results show that the positioning root mean square error (RMSE) of the sensor complex network using semidefinite relaxation optimization( SC-SDP) algorithm in the wireless sensor network is better than the positioning root mean square error (RMSE) of the Gaussian mixture model via semidefinite relaxation(GM- SDP)、weighed least msquares (WLS) and cramer-rao lower(CRLB), and the accuracy is higher than other algorithms, which improves the wireless sensor network. The positioning accuracy and anti-interference performance of the wireless sensor network are improved, and the noise and positioning error are alleviated, and the performance of the semi-definite relaxation algorithm is improved to a certain extent.