WANG Jun, QIN Zhaotao, GAO Fei, WEI Shaoming. An Approximate Maximum Likelihood Algorithm for Target Localization in Multistatic Passive Radar[J]. Chinese Journal of Electronics, 2019, 28(1): 195-201. doi: 10.1049/cje.2018.02.018
Citation: WANG Jun, QIN Zhaotao, GAO Fei, WEI Shaoming. An Approximate Maximum Likelihood Algorithm for Target Localization in Multistatic Passive Radar[J]. Chinese Journal of Electronics, 2019, 28(1): 195-201. doi: 10.1049/cje.2018.02.018

An Approximate Maximum Likelihood Algorithm for Target Localization in Multistatic Passive Radar

doi: 10.1049/cje.2018.02.018
Funds:  This work is supported by the National Natural Science Foundation of China (No.61501012, No.61671035, No.61771027), the Foundation of ATR Key Laboratory (No.6142503010202).
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  • Corresponding author: GAO Fei (corresponding author) was born in 1975. He received the B.S. and M.S. degree from the Xian Petroleum Institute, Xian, China, in 1996 and 1999, respectively, and the Ph.D. degrees from Beihang University (BUAA), Beijing, China, in 2005. He is currently an associate professor with the School of Electronic and Information Engineering, BUAA. He is interested in radar signal processing, moving target detection and image processing. (
  • Received Date: 2017-09-12
  • Rev Recd Date: 2017-12-04
  • Publish Date: 2019-01-10
  • This paper addresses the problem of target localization using Bistatic range (BR) measurements in a distributed multistatic passive radar system. The rangebased positioning technique employs multiple transmitterreceiver pairs, which provide separate BR measurements. Based on the Maximum likelihood (ML) function, an efficient algebraic Approximate maximum likelihood (AML) algorithm for single target localization is proposed. The closed-form AML solution has neither initial condition requirements nor convergence difficulty. Simulations are included to compare its performance to that of the CramerRao lower bound (CRLB) and the Two-step Weighted least squares (TS-WLS) algorithm. The proposed method is shown to be able to achieve the CRLB accuracy under Gaussian measurement noise. It is more robust to noise than the TS-WLS method, and presents relative insensitivity to target-sensor geometry.
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