Abstract:

For the source localization problem in the presence of sensor position errors, this paper presents a timedifferenceofarrival(TDOA)technique based on the modified Newton(MNT)algorithm. It is known that the NT algorithm suffers from the initialization problem. A bad initial may cause the iteration divergence. In addition, inaccurate sensor position condition also causes the NT algorithm computationally intensive due to the highdimension matrix and the slow downtrend of the objective function. The new algorithm uses the maximum likelihood method to determine the objective function. For the problem of the illcondition Hessian matrix caused by the bad initial, the algorithm introduces the regularization theory to modify the Hessian matrix, which ensures the iteration convergence. Meanwhile, the proposed algorithm reduces the degree of the highdimension matrix and increases the downtrend of the objective〖JP〗 function, and makes the solution escape from the local minimum value via a simplified iterative criterion. Experiment results show that compared with the classical Newton method, this new algorithm is robust to the initial value, and is still able to ensure its convergence even with an inaccurate initial value of large errors, it also speeds up the convergent rate and cuts the computation. Compared with some other closedform source location methods, the new algorithm has better accuracy in large noise levels.