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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.
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. 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 encouraging high noise level before the threshold effect o ccurs.