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
Hauptverfasser: Wang, Xiaofen, Wang, Peng, Zhang, Xiaotong, Wan, Yadong, Liu, Wen, Shi, Haodong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.07.2023
Schlagworte:
algorithms
computers
Damping parameter
Electromagnetic induction
least squares
Levenberg–Marquardt
metals
Parameter inversion
solutions
Underground target detection
Damping parameter
Underground target detection
Parameter inversion
Levenberg–Marquardt
Electromagnetic induction
ISSN:0098-3004, 1873-7803
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Zusammenfassung: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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ISSN:0098-3004
1873-7803
DOI:10.1016/j.cageo.2023.105354