Positive Semi-definite Programming Localization in Wireless Sensor Networks

We propose an algorithm to locate an object with unknown coordinates based on the positive semi-definite programming in the wireless sensor networks, assuming that the squared error of the measured distance follows Gaussian distribution. We first obtain the estimator of the object location; then transform the non-convex problem to convex one by the positive semi-definite relaxation; and finally we take the optimal solution as the estimated location. Simulations results show that our algorithm is superior to the R-LS algorithm.