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

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• 作者：Huagang YU ; Gaoming HUANG ; Jun GAO ; Xinhui WU
• 关键词：approximate maximum likelihood (AML) ; maximum likelihood (ML) ; source localization ; time differences of arrival (TDOA) ; frequency differences of arrival (FDOA)
• 刊名：Chinese Journal of Aeronautics
• 出版年：2012
• 出版时间：August, 2012
• 年：2012
• 卷：25
• 期：4
• 页码：593-597
• 全文大小：150 K

文摘

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 Cram¨¦r-Rao lower bound (CRLB) at a sufficiently high noise level before the threshold effect occurs.