ML-based single-step estimation of the locations of strictly noncircular sources |
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| Affiliation: | 1. National Digital Switching System Engineering and Technology Research Center, Zhengzhou, Henan 450002, PR China;2. Zhengzhou Information Science and Technology Institute, Zhengzhou, Henan 450002, PR China;1. Institute of Industrial Information Technology, Karlsruhe Institute of Technology, 76187 Karlsruhe, Germany;2. Institute for Wireless Communication and Navigation, University of Kaiserslautern, 67663 Kaiserslautern, Germany;1. CONACyT–Instituto de Ingeniería, Universidad Nacional Autónoma de México, Circuito Escolar s/n, Ciudad Universitaria, Coyoacán, Mexico City 04510, Mexico;2. CONACyT–Centro Nacional de Investigación y Desarrollo Tecnológico, Tecnológico Nacional de México, Internado Palmira S/N, Palmira, Cuernavaca 62490, Morelos, Mexico;3. Departamento de Ingeniería Electrónica, Universidad de la Costa – CUC, Calle 58 No. 55–66, Barranquilla, 080002, Colombia;4. Instituto de Ingeniería, Universidad Nacional Autónoma de México, Circuito Escolar s/n, Ciudad Universitaria, Coyoacán, Ciudad de México 04510, Mexico;5. Electronic Department, Tecnológico Nacional de México, Instituto Tecnológico de Tuxtla Gutiérrez, Tuxtla Gutiérrez, Chiapas, Mexico;6. Centro Nacional de Investigación y Desarrollo Tecnológico, Tecnológico Nacional de México, Internado Palmira S/N, Palmira, Cuernavaca 62490, Morelos, Mexico;1. College of Automation, Harbin Engineering University, Harbin 150001, China;2. School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Australia;3. College of Information Science and Technology, Hainan University, Hainan, 570228, China;1. Electronic Engineering Department / Graduate School at Shenzhen, Tsinghua University, Beijing 100084, China;2. Biometrics Research Centre and the Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;3. Biocomputing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China;1. National Laboratory of Radar Signal Processing, Xidian University, Xi''an Shanxi 710071, China;2. Collaborative Innovation Center of Information Sensing and Understanding at Xidian University, Xidian University, Xi''an Shanxi 710071, China |
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| Abstract: | This paper concentrates on the location methods for strictly noncircular sources by widely separated arrays. The conventional two-step methods extract measurement parameters and then, estimate the positions from them. Compared with the conventional two-step methods, direct position determination (DPD) is a promising technique, which locates transmitters directly from original sensor outputs without estimating intermediate parameters in a single step, and thus, improves the location accuracy and avoids the data association problem. However, existing DPD methods mainly focus on complex circular sources without considering noncircular signals, which can be exploited to enhance the localization accuracy. This paper proposes a maximum likelihood (ML)-based DPD algorithm for strictly noncircular sources whose waveforms are unknown. By exploiting the noncircularity of sources, we establish an ML-based function in time domain under the constraint on the waveforms of signals. A decoupled iterative method is developed to solve the prescribed ML estimator with a moderate complexity. In addition, we derive the deterministic Cramér–Rao Bound (CRB) for strictly noncircular sources, and prove that this CRB is upper bounded by the associated CRB for circular signals. Simulation results demonstrate that the proposed algorithm has a fast convergence rate, and outperforms the other location methods in a wide range of scenarios. |
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| Keywords: | Array processing Direct position determination (DPD) Noncircular source Maximum likelihood (ML) Cramér–Rao Bound (CRB) |
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