TDOA passive location based on improvement of second cone relaxation and Taylor-series estimation

Since the second order cone relaxation algorithm cannot locate the target , which is out of the convex hull of the problem. In this paper, improved TDOA location second order cone relaxation and Taylor series expansion algorithm can be based on. First of all, the weighted least squares model of TDOA is given in the line of sight conditions with receiving station location information without error; secondly, taking the original non convex optimization model into a convex optimization model of relaxation, relaxation of the equality constraints adding a new penalty term based on traditional second order cone relaxation algorithm. In order to solve the convex hull problem for second order cone relaxation, it should make the constraint relaxation a further approximation of the original problem constraints; thirdly, the problem can be solved after the relaxation model turns into second-order cone form; finally, an estimated value of second order cone relaxation algorithm is used as the initial value of Taylor iteration to further improve the precision of estimates. The simulation results show that the algorithm is effective, the positioning performance is better than the traditional second order cone relaxation algorithm, which can approach to the CRB bound.