Efficient and robust Levenberg–Marquardt Algorithm based on damping parameters for parameter inversion in underground metal target detection
The Levenberg–Marquardt (LM) algorithm has been widely used to solve nonlinear least-squares problems in underground target detection. However, the LM algorithm has an unsatisfactory performance of convergence due to the influence of noise in the environment. Therefore, a new modified LM (NMLM) algo...
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| Veröffentlicht in: | Computers & geosciences Jg. 176; S. 105354 |
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01.07.2023
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| Abstract | The Levenberg–Marquardt (LM) algorithm has been widely used to solve nonlinear least-squares problems in underground target detection. However, the LM algorithm has an unsatisfactory performance of convergence due to the influence of noise in the environment. Therefore, a new modified LM (NMLM) algorithm has been proposed in this paper to improve its accuracy and efficiency of parameter estimation with low SNR. The NMLM algorithm can also converge globally under certain conditions and converge quadratically under the error-bound condition, which updates based on the gain ratio and determines the damping factor based on the gradient value. An experimental investigation has been conducted under different SNRs and the amount of data, and the results indicate the new algorithm can accurately and quickly estimate the parameters of underground metal targets under the lower SNRs and smaller amounts of data. More specifically, the estimation accuracy of position, principal axes polarizability, and orientation of the metal target can be increased by up to 1.6 cm, 0.00013, and 2.31 degree, respectively. This paper also verifies that the new algorithm finds the optimal solution for standard numerical problems with low errors and a finite number of iterations.
•A new LM algorithm is proposed to improve the accuracy and convergence of the result.•The performances are analyzed by comparing conventional and NMLM algorithms.•The performance of the NMLM algorithm on the common optimization problem is analyzed. |
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| AbstractList | The Levenberg–Marquardt (LM) algorithm has been widely used to solve nonlinear least-squares problems in underground target detection. However, the LM algorithm has an unsatisfactory performance of convergence due to the influence of noise in the environment. Therefore, a new modified LM (NMLM) algorithm has been proposed in this paper to improve its accuracy and efficiency of parameter estimation with low SNR. The NMLM algorithm can also converge globally under certain conditions and converge quadratically under the error-bound condition, which updates based on the gain ratio and determines the damping factor based on the gradient value. An experimental investigation has been conducted under different SNRs and the amount of data, and the results indicate the new algorithm can accurately and quickly estimate the parameters of underground metal targets under the lower SNRs and smaller amounts of data. More specifically, the estimation accuracy of position, principal axes polarizability, and orientation of the metal target can be increased by up to 1.6 cm, 0.00013, and 2.31 degree, respectively. This paper also verifies that the new algorithm finds the optimal solution for standard numerical problems with low errors and a finite number of iterations.
•A new LM algorithm is proposed to improve the accuracy and convergence of the result.•The performances are analyzed by comparing conventional and NMLM algorithms.•The performance of the NMLM algorithm on the common optimization problem is analyzed. The Levenberg-Marquardt (LM) algorithm has been widely used to solve nonlinear least-squares problems in underground target detection. However, the LM algorithm has an unsatisfactory performance of convergence due to the influence of noise in the environment. Therefore, a new modified LM (NMLM) algorithm has been proposed in this paper to improve its accuracy and efficiency of parameter estimation with low SNR. The NMLM algorithm can also converge globally under certain conditions and converge quadratically under the error-bound condition, which updates based on the gain ratio and determines the damping factor based on the gradient value. An experimental investigation has been conducted under different SNRs and the amount of data, and the results indicate the new algorithm can accurately and quickly estimate the parameters of underground metal targets under the lower SNRs and smaller amounts of data. More specifically, the estimation accuracy of position, principal axes polarizability, and orientation of the metal target can be increased by up to 1.6 cm, 0.00013, and 2.31 degree, respectively. This paper also verifies that the new algorithm finds the optimal solution for standard numerical problems with low errors and a finite number of iterations. |
| ArticleNumber | 105354 |
| Author | Wan, Yadong Wang, Peng Shi, Haodong Wang, Xiaofen Zhang, Xiaotong Liu, Wen |
| Author_xml | – sequence: 1 givenname: Xiaofen orcidid: 0000-0003-0903-2044 surname: Wang fullname: Wang, Xiaofen email: b20200343@xs.ustb.edu.cn organization: School of Computer and Communication Engineering, University of Science and Technology Beijing, 30th Xueyuan Road, Haidian District, Beijing, 100083, China – sequence: 2 givenname: Peng surname: Wang fullname: Wang, Peng email: wangp503@chinaunicom.cn organization: China Unicom Smart City Research Institute, 9th South Capital Gymnasium Road, Haidian District, Beijing, 100033, China – sequence: 3 givenname: Xiaotong surname: Zhang fullname: Zhang, Xiaotong email: zxt@ies.ustb.edu.cn organization: School of Computer and Communication Engineering, University of Science and Technology Beijing, 30th Xueyuan Road, Haidian District, Beijing, 100083, China – sequence: 4 givenname: Yadong surname: Wan fullname: Wan, Yadong email: wyd@ustb.edu.cn organization: School of Computer and Communication Engineering, University of Science and Technology Beijing, 30th Xueyuan Road, Haidian District, Beijing, 100083, China – sequence: 5 givenname: Wen surname: Liu fullname: Liu, Wen email: g20208815@xs.ustb.edu.cn organization: School of Computer and Communication Engineering, University of Science and Technology Beijing, 30th Xueyuan Road, Haidian District, Beijing, 100083, China – sequence: 6 givenname: Haodong surname: Shi fullname: Shi, Haodong email: g20208825@xs.ustb.edu.cn organization: School of Computer and Communication Engineering, University of Science and Technology Beijing, 30th Xueyuan Road, Haidian District, Beijing, 100083, China |
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| Cites_doi | 10.1093/comjnl/3.3.175 10.1016/j.measurement.2020.108330 10.1109/JSEN.2017.2676460 10.1109/ACCESS.2020.3008176 10.1090/qam/10666 10.1137/0111030 10.1109/JSEN.2019.2944752 10.1007/s10957-020-01666-1 10.1109/TIM.2019.2917236 10.1109/TNN.2010.2045657 10.3390/drones6010011 10.1109/JSEN.2022.3175502 10.1093/gji/ggaa483 10.1007/s10915-017-0488-6 10.1109/ACCESS.2021.3049308 10.3390/rs13122343 10.1016/j.ndteint.2021.102571 10.1080/10556788.2016.1179737 10.1007/s00607-004-0083-1 |
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| Keywords | Damping parameter Underground target detection Parameter inversion Levenberg–Marquardt Electromagnetic induction |
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| References | Umar, Sulaiman, Mamat (b19) 2021 Xie, Lai, Dérobert (b24) 2021; 168 Stewart (b15) 1990 Bergou, Diouane, Kungurtsev (b3) 2020; 185 Fan (b5) 2003 Yu, Zhang, Wei (b26) 2020; 70 Hampton, Fletcher (b8) 2022; 125 Tao, Yin, Zhang (b18) 2017; 17 Powell (b13) 1975 Wan, Li, Wang (b20) 2021; 9 Fan, Yuan (b7) 2005; 74 Marquardt (b11) 1963; 11 Zheng, Li, Xing (b29) 2022; 6 Fan, Yuan (b6) 2001 Chen, Zhang, Zhu (b4) 2019; 69 Wang, Wang, Zhang (b22) 2022; 22 Suratgar, Tavakoli, Hoseinabadi (b17) 2005; 6 Alrumaih (b1) 2018 Sun, Akhtar, Song (b16) 2020 Zhao, Fan (b28) 2018; 74 Wilamowski, Yu (b23) 2010; 21 Zhao, Fan (b27) 2016; 31 Rosenbrock (b14) 1960; 3 Mu, Xie, Zhang (b12) 2021; 13 Wan, Wang, Wang (b21) 2020; 8 Ambruš, Vasić, Bilas (b2) 2019; 20 Levenberg (b10) 1944; 2 Yamashita, Fukushima (b25) 2001 Kolster, Døssing (b9) 2021; 224 Fan (10.1016/j.cageo.2023.105354_b5) 2003 Powell (10.1016/j.cageo.2023.105354_b13) 1975 Wan (10.1016/j.cageo.2023.105354_b21) 2020; 8 Xie (10.1016/j.cageo.2023.105354_b24) 2021; 168 Wan (10.1016/j.cageo.2023.105354_b20) 2021; 9 Wilamowski (10.1016/j.cageo.2023.105354_b23) 2010; 21 Bergou (10.1016/j.cageo.2023.105354_b3) 2020; 185 Umar (10.1016/j.cageo.2023.105354_b19) 2021 Sun (10.1016/j.cageo.2023.105354_b16) 2020 Fan (10.1016/j.cageo.2023.105354_b7) 2005; 74 Zhao (10.1016/j.cageo.2023.105354_b27) 2016; 31 Zhao (10.1016/j.cageo.2023.105354_b28) 2018; 74 Kolster (10.1016/j.cageo.2023.105354_b9) 2021; 224 Yu (10.1016/j.cageo.2023.105354_b26) 2020; 70 Wang (10.1016/j.cageo.2023.105354_b22) 2022; 22 Hampton (10.1016/j.cageo.2023.105354_b8) 2022; 125 Zheng (10.1016/j.cageo.2023.105354_b29) 2022; 6 Fan (10.1016/j.cageo.2023.105354_b6) 2001 Levenberg (10.1016/j.cageo.2023.105354_b10) 1944; 2 Stewart (10.1016/j.cageo.2023.105354_b15) 1990 Tao (10.1016/j.cageo.2023.105354_b18) 2017; 17 Mu (10.1016/j.cageo.2023.105354_b12) 2021; 13 Suratgar (10.1016/j.cageo.2023.105354_b17) 2005; 6 Ambruš (10.1016/j.cageo.2023.105354_b2) 2019; 20 Marquardt (10.1016/j.cageo.2023.105354_b11) 1963; 11 Yamashita (10.1016/j.cageo.2023.105354_b25) 2001 Alrumaih (10.1016/j.cageo.2023.105354_b1) 2018 Chen (10.1016/j.cageo.2023.105354_b4) 2019; 69 Rosenbrock (10.1016/j.cageo.2023.105354_b14) 1960; 3 |
| References_xml | – volume: 13 start-page: 2343 year: 2021 ident: b12 article-title: The joint UAV-borne magnetic detection system and cart-mounted time domain electromagnetic system for UXO detection publication-title: Remote Sens. – volume: 17 start-page: 2703 year: 2017 end-page: 2712 ident: b18 article-title: A very-low-frequency electromagnetic inductive sensor system for workpiece recognition using the magnetic polarizability tensor publication-title: IEEE Sens. J. – start-page: 1 year: 2001 end-page: 11 ident: b6 article-title: On the Convergence of a New Levenberg-Marquardt Method – volume: 74 start-page: 23 year: 2005 end-page: 39 ident: b7 article-title: On the quadratic convergence of the Levenberg-Marquardt method without nonsingularity assumption publication-title: Computing – volume: 22 start-page: 13835 year: 2022 end-page: 13852 ident: b22 article-title: Target electromagnetic detection method in underground environment: A review publication-title: IEEE Sens. J. – volume: 6 start-page: 11 year: 2022 ident: b29 article-title: Processing and interpretation of UAV magnetic data: A workflow based on improved variational mode decomposition and Levenberg–Marquardt algorithm publication-title: Drones – start-page: 1 year: 2018 end-page: 6 ident: b1 article-title: The construction of a robotic vehicle metal detector as a tool for searching archaeology sites publication-title: ICCAIS – volume: 6 start-page: 46 year: 2005 end-page: 48 ident: b17 article-title: Modified Levenberg-Marquardt method for neural networks training publication-title: World Acad. Sci. Eng. Technol. – volume: 74 start-page: 1146 year: 2018 end-page: 1162 ident: b28 article-title: On a new updating rule of the Levenberg–Marquardt parameter publication-title: J. Sci. Comput. – volume: 20 start-page: 968 year: 2019 end-page: 979 ident: b2 article-title: Comparative study of planar coil EMI sensors for inversion-based detection of buried objects publication-title: IEEE Sens. J. – volume: 8 start-page: 126401 year: 2020 end-page: 126413 ident: b21 article-title: A comparative study of inversion optimization algorithms for underground metal target detection publication-title: IEEE Access – start-page: 12018 year: 2021 ident: b19 article-title: On damping parameters of Levenberg-Marquardt algorithm for nonlinear least square problems publication-title: Journal of Physics: Conference Series, Vol. 1734 – start-page: 1 year: 1975 end-page: 27 ident: b13 article-title: Convergence properties of a class of minimization algorithms publication-title: Nonlinear Programming 2 – volume: 224 start-page: 468 year: 2021 end-page: 486 ident: b9 article-title: Scalar magnetic difference inversion applied to UAV-based UXO detection publication-title: Geophys. J. Int. – year: 1990 ident: b15 article-title: Matrix perturbation theory – volume: 185 start-page: 927 year: 2020 end-page: 944 ident: b3 article-title: Convergence and complexity analysis of a Levenberg–Marquardt algorithm for inverse problems publication-title: J. Optim. Theory Appl. – volume: 9 start-page: 7384 year: 2021 end-page: 7401 ident: b20 article-title: Robust and efficient classification for underground metal target using dimensionality reduction and machine learning publication-title: IEEE Access – volume: 31 start-page: 805 year: 2016 end-page: 814 ident: b27 article-title: Global complexity bound of the Levenberg–Marquardt method publication-title: Optim. Methods Softw. – volume: 21 start-page: 930 year: 2010 end-page: 937 ident: b23 article-title: Improved computation for Levenberg–Marquardt training publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 168 year: 2021 ident: b24 article-title: GPR-based depth measurement of buried objects based on constrained least-square (CLS) fitting method of reflections publication-title: Measurement – volume: 125 year: 2022 ident: b8 article-title: A comparison of non-linear optimisation algorithms for recovering the conductivity depth profile of an electrically conductive block using eddy current inspection publication-title: NDT E Int. – start-page: 625 year: 2003 end-page: 636 ident: b5 article-title: A modified Levenberg-Marquardt algorithm for singular system of nonlinear equations publication-title: J. Comput. Math. – start-page: 239 year: 2001 end-page: 249 ident: b25 article-title: On the rate of convergence of the Levenberg-Marquardt method publication-title: Topics in Numerical Analysis – start-page: 626 year: 2020 end-page: 643 ident: b16 article-title: Simultaneous detection and tracking with motion modelling for multiple object tracking publication-title: ECCV – volume: 11 start-page: 431 year: 1963 end-page: 441 ident: b11 article-title: An algorithm for least-squares estimation of nonlinear parameters publication-title: J. Soc. Ind. Appl. Math. – volume: 3 start-page: 175 year: 1960 end-page: 184 ident: b14 article-title: An automatic method for finding the greatest or least value of a function publication-title: Comput. J. – volume: 2 start-page: 164 year: 1944 end-page: 168 ident: b10 article-title: A method for the solution of certain non-linear problems in least squares publication-title: Q. Appl. Math. – volume: 70 start-page: 1 year: 2020 end-page: 11 ident: b26 article-title: A distributed phase measurement method of frequency-domain electromagnetic detection publication-title: IEEE Trans. Instrum. Meas. – volume: 69 start-page: 1728 year: 2019 end-page: 1736 ident: b4 article-title: Accurate measurement of characteristic response for unexploded ordnance with transient electromagnetic system publication-title: IEEE Trans. Instrum. Meas. – volume: 3 start-page: 175 issue: 3 year: 1960 ident: 10.1016/j.cageo.2023.105354_b14 article-title: An automatic method for finding the greatest or least value of a function publication-title: Comput. J. doi: 10.1093/comjnl/3.3.175 – volume: 168 year: 2021 ident: 10.1016/j.cageo.2023.105354_b24 article-title: GPR-based depth measurement of buried objects based on constrained least-square (CLS) fitting method of reflections publication-title: Measurement doi: 10.1016/j.measurement.2020.108330 – volume: 17 start-page: 2703 issue: 9 year: 2017 ident: 10.1016/j.cageo.2023.105354_b18 article-title: A very-low-frequency electromagnetic inductive sensor system for workpiece recognition using the magnetic polarizability tensor publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2017.2676460 – volume: 8 start-page: 126401 year: 2020 ident: 10.1016/j.cageo.2023.105354_b21 article-title: A comparative study of inversion optimization algorithms for underground metal target detection publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3008176 – volume: 2 start-page: 164 issue: 2 year: 1944 ident: 10.1016/j.cageo.2023.105354_b10 article-title: A method for the solution of certain non-linear problems in least squares publication-title: Q. Appl. Math. doi: 10.1090/qam/10666 – start-page: 12018 year: 2021 ident: 10.1016/j.cageo.2023.105354_b19 article-title: On damping parameters of Levenberg-Marquardt algorithm for nonlinear least square problems – start-page: 625 year: 2003 ident: 10.1016/j.cageo.2023.105354_b5 article-title: A modified Levenberg-Marquardt algorithm for singular system of nonlinear equations publication-title: J. Comput. Math. – volume: 11 start-page: 431 issue: 2 year: 1963 ident: 10.1016/j.cageo.2023.105354_b11 article-title: An algorithm for least-squares estimation of nonlinear parameters publication-title: J. Soc. Ind. Appl. Math. doi: 10.1137/0111030 – start-page: 1 year: 1975 ident: 10.1016/j.cageo.2023.105354_b13 article-title: Convergence properties of a class of minimization algorithms – volume: 20 start-page: 968 issue: 2 year: 2019 ident: 10.1016/j.cageo.2023.105354_b2 article-title: Comparative study of planar coil EMI sensors for inversion-based detection of buried objects publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2019.2944752 – start-page: 1 year: 2001 ident: 10.1016/j.cageo.2023.105354_b6 – volume: 6 start-page: 46 issue: 1 year: 2005 ident: 10.1016/j.cageo.2023.105354_b17 article-title: Modified Levenberg-Marquardt method for neural networks training publication-title: World Acad. Sci. Eng. Technol. – volume: 185 start-page: 927 issue: 3 year: 2020 ident: 10.1016/j.cageo.2023.105354_b3 article-title: Convergence and complexity analysis of a Levenberg–Marquardt algorithm for inverse problems publication-title: J. Optim. Theory Appl. doi: 10.1007/s10957-020-01666-1 – start-page: 239 year: 2001 ident: 10.1016/j.cageo.2023.105354_b25 article-title: On the rate of convergence of the Levenberg-Marquardt method – volume: 69 start-page: 1728 issue: 4 year: 2019 ident: 10.1016/j.cageo.2023.105354_b4 article-title: Accurate measurement of characteristic response for unexploded ordnance with transient electromagnetic system publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2019.2917236 – volume: 21 start-page: 930 issue: 6 year: 2010 ident: 10.1016/j.cageo.2023.105354_b23 article-title: Improved computation for Levenberg–Marquardt training publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNN.2010.2045657 – start-page: 1 year: 2018 ident: 10.1016/j.cageo.2023.105354_b1 article-title: The construction of a robotic vehicle metal detector as a tool for searching archaeology sites – volume: 70 start-page: 1 year: 2020 ident: 10.1016/j.cageo.2023.105354_b26 article-title: A distributed phase measurement method of frequency-domain electromagnetic detection publication-title: IEEE Trans. Instrum. Meas. – volume: 6 start-page: 11 issue: 1 year: 2022 ident: 10.1016/j.cageo.2023.105354_b29 article-title: Processing and interpretation of UAV magnetic data: A workflow based on improved variational mode decomposition and Levenberg–Marquardt algorithm publication-title: Drones doi: 10.3390/drones6010011 – volume: 22 start-page: 13835 issue: 14 year: 2022 ident: 10.1016/j.cageo.2023.105354_b22 article-title: Target electromagnetic detection method in underground environment: A review publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2022.3175502 – year: 1990 ident: 10.1016/j.cageo.2023.105354_b15 – volume: 224 start-page: 468 issue: 1 year: 2021 ident: 10.1016/j.cageo.2023.105354_b9 article-title: Scalar magnetic difference inversion applied to UAV-based UXO detection publication-title: Geophys. J. Int. doi: 10.1093/gji/ggaa483 – volume: 74 start-page: 1146 issue: 2 year: 2018 ident: 10.1016/j.cageo.2023.105354_b28 article-title: On a new updating rule of the Levenberg–Marquardt parameter publication-title: J. Sci. Comput. doi: 10.1007/s10915-017-0488-6 – volume: 9 start-page: 7384 year: 2021 ident: 10.1016/j.cageo.2023.105354_b20 article-title: Robust and efficient classification for underground metal target using dimensionality reduction and machine learning publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3049308 – start-page: 626 year: 2020 ident: 10.1016/j.cageo.2023.105354_b16 article-title: Simultaneous detection and tracking with motion modelling for multiple object tracking – volume: 13 start-page: 2343 issue: 12 year: 2021 ident: 10.1016/j.cageo.2023.105354_b12 article-title: The joint UAV-borne magnetic detection system and cart-mounted time domain electromagnetic system for UXO detection publication-title: Remote Sens. doi: 10.3390/rs13122343 – volume: 125 year: 2022 ident: 10.1016/j.cageo.2023.105354_b8 article-title: A comparison of non-linear optimisation algorithms for recovering the conductivity depth profile of an electrically conductive block using eddy current inspection publication-title: NDT E Int. doi: 10.1016/j.ndteint.2021.102571 – volume: 31 start-page: 805 issue: 4 year: 2016 ident: 10.1016/j.cageo.2023.105354_b27 article-title: Global complexity bound of the Levenberg–Marquardt method publication-title: Optim. Methods Softw. doi: 10.1080/10556788.2016.1179737 – volume: 74 start-page: 23 issue: 1 year: 2005 ident: 10.1016/j.cageo.2023.105354_b7 article-title: On the quadratic convergence of the Levenberg-Marquardt method without nonsingularity assumption publication-title: Computing doi: 10.1007/s00607-004-0083-1 |
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| Snippet | The Levenberg–Marquardt (LM) algorithm has been widely used to solve nonlinear least-squares problems in underground target detection. However, the LM... The Levenberg-Marquardt (LM) algorithm has been widely used to solve nonlinear least-squares problems in underground target detection. However, the LM... |
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| SubjectTerms | algorithms computers Damping parameter Electromagnetic induction least squares Levenberg–Marquardt metals Parameter inversion solutions Underground target detection |
| Title | Efficient and robust Levenberg–Marquardt Algorithm based on damping parameters for parameter inversion in underground metal target detection |
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