Explosive sound source localization in indoor and outdoor environments using modified Levenberg Marquardt algorithm

• Published in: Measurement : journal of the International Measurement Confederation Vol. 187; p. 110362
• Main Authors: Mahapatra, Chinmayi, Mohanty, A.R.
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.01.2022; Elsevier Science Ltd
• Subjects: Algorithms; Computer simulation; Convergence; Explosive sources; Indoor environments; Iterative methods; Modified Levenberg-Marquardt algorithm; Multilateration; Noise; Numerical analysis; Optimization; Simulation; Sound localization; Sound sources; Time Difference of Arrival; Modified Levenberg-Marquardt algorithm; Explosive sources; Time Difference of Arrival; Multilateration
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2021.110362

•Explosive sound source positioning by using modified Levenberg Marquardt algorithm.•A comparative analysis of different positioning methods.•Modified LMA performance validation through simulation and experimental studies.•A short and wide range of sound source positioning by varying r between 0.002 and 2.•Sound source localization for both indoor and outdoor environments. In this paper, a modified Levenberg-Marquardt algorithm (MLMA) is proposed to localize the ‘point of burst’ of an explosive sound source over the range of (0.5–2500) m. The objective function for minimization is formulated through the time difference of arrival based multilateration approach. The developed method uses four exclusive steps to satisfy global convergence along with fast computational speed. The performance of the proposed method is validated through both indoor-outdoor experiments and simulation studies and compared to other well-known methods. The experimental results show that the non-iterative approaches perform satisfactorily only if the ratio of microphone spacing to source rangeris greater than 0.30. However, the iterative approaches outperform non-iterative approaches for anyr. It is also observed that the MLMA converges globally at least five times faster than other algorithms. The numerical simulation results also demonstrate that the MLMA provides optimal solutions at lower and higher noise thresholds.