Approximate Maximum Likelihood Algorithm for Moving Source Localization Using TDOA and FDOA Measurements

A closed-form approximate maximum likelihood(AML) algorithm for estimating the position and velocity of a moving source is proposed by utilizing the time difference of arrival(TDOA) and frequency difference of arrival(FDOA) measurements of a signal received at a number of receivers.The maximum likelihood(ML) technique is a powerful tool to solve this problem.But a direct approach that uses the ML estimator to solve the localization problem is exhaustive search in the solution space,and it is very computationally expensive,and prohibits real-time processing.On the basis of ML function,a closed-form approximate solution to the ML equations can be obtained,which can allow real-time implementation as well as global convergence.Simulation results show that the proposed estimator achieves better performance than the two-step weighted least squares(WLS) approach,which makes it possible to attain the Cramér-Rao lower bound(CRLB) at a sufficiently high noise level before the threshold effect occurs.