DOA Estimation With Nested Arrays in Impulsive Noise Scenario: An Adaptive Order Moment Strategy
Most of the existing direction of arrival (DOA) estimation methods in impulsive noise scenario are based on the fractional low-order moment statistics (FLOSs), such as the robust covariation-based (ROC), fractional low-order moment (FLOM), and phased fractional low-order moment (PFLOM). However, an...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Signal Processing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10417125/ |
| _version_ | 1797293277676830720 |
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| author | Xudong Dong Jun Zhao Jingjing Pan Meng Sun Xiaofei Zhang Peihao Dong Yide Wang |
| author_facet | Xudong Dong Jun Zhao Jingjing Pan Meng Sun Xiaofei Zhang Peihao Dong Yide Wang |
| author_sort | Xudong Dong |
| collection | DOAJ |
| description | Most of the existing direction of arrival (DOA) estimation methods in impulsive noise scenario are based on the fractional low-order moment statistics (FLOSs), such as the robust covariation-based (ROC), fractional low-order moment (FLOM), and phased fractional low-order moment (PFLOM). However, an unknown order moment parameter <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> needs to be selected in these approaches, which inevitably increases the computational load if the optimal value of the parameter <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> is determined by a large number of Monte Carlo experiments. To address this issue, we propose the adaptive order moment function (AOMF) and improved AOMF (IAOMF), which are applicable to the existing FLOSs-based methods and can also be extended to the case of sparse arrays. Moreover, we analyze the performance of AOMF and IAOMF, and simulation experiments verify the effectiveness of proposed methods. |
| first_indexed | 2024-03-07T20:10:34Z |
| format | Article |
| id | doaj.art-1b65d786ffc446f2aa57f740cd61c929 |
| institution | Directory Open Access Journal |
| issn | 2644-1322 |
| language | English |
| last_indexed | 2024-03-07T20:10:34Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Signal Processing |
| spelling | doaj.art-1b65d786ffc446f2aa57f740cd61c9292024-02-28T00:01:23ZengIEEEIEEE Open Journal of Signal Processing2644-13222024-01-01549350210.1109/OJSP.2024.336089610417125DOA Estimation With Nested Arrays in Impulsive Noise Scenario: An Adaptive Order Moment StrategyXudong Dong0https://orcid.org/0000-0003-4802-6026Jun Zhao1https://orcid.org/0000-0002-7914-196XJingjing Pan2https://orcid.org/0000-0001-7362-0778Meng Sun3https://orcid.org/0000-0002-4565-5399Xiaofei Zhang4https://orcid.org/0000-0003-1464-1987Peihao Dong5https://orcid.org/0000-0002-0684-0516Yide Wang6https://orcid.org/0000-0002-1461-2003College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Electronic and Information Engineering, Tongji University, Shanghai, ChinaCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaIETR, University of Nantes, Nantes, FranceMost of the existing direction of arrival (DOA) estimation methods in impulsive noise scenario are based on the fractional low-order moment statistics (FLOSs), such as the robust covariation-based (ROC), fractional low-order moment (FLOM), and phased fractional low-order moment (PFLOM). However, an unknown order moment parameter <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> needs to be selected in these approaches, which inevitably increases the computational load if the optimal value of the parameter <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> is determined by a large number of Monte Carlo experiments. To address this issue, we propose the adaptive order moment function (AOMF) and improved AOMF (IAOMF), which are applicable to the existing FLOSs-based methods and can also be extended to the case of sparse arrays. Moreover, we analyze the performance of AOMF and IAOMF, and simulation experiments verify the effectiveness of proposed methods.https://ieeexplore.ieee.org/document/10417125/Adaptive order momentdirection of arrival (DOA) estimationimpulsive noisesparse arrays |
| spellingShingle | Xudong Dong Jun Zhao Jingjing Pan Meng Sun Xiaofei Zhang Peihao Dong Yide Wang DOA Estimation With Nested Arrays in Impulsive Noise Scenario: An Adaptive Order Moment Strategy IEEE Open Journal of Signal Processing Adaptive order moment direction of arrival (DOA) estimation impulsive noise sparse arrays |
| title | DOA Estimation With Nested Arrays in Impulsive Noise Scenario: An Adaptive Order Moment Strategy |
| title_full | DOA Estimation With Nested Arrays in Impulsive Noise Scenario: An Adaptive Order Moment Strategy |
| title_fullStr | DOA Estimation With Nested Arrays in Impulsive Noise Scenario: An Adaptive Order Moment Strategy |
| title_full_unstemmed | DOA Estimation With Nested Arrays in Impulsive Noise Scenario: An Adaptive Order Moment Strategy |
| title_short | DOA Estimation With Nested Arrays in Impulsive Noise Scenario: An Adaptive Order Moment Strategy |
| title_sort | doa estimation with nested arrays in impulsive noise scenario an adaptive order moment strategy |
| topic | Adaptive order moment direction of arrival (DOA) estimation impulsive noise sparse arrays |
| url | https://ieeexplore.ieee.org/document/10417125/ |
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