Multiple-array passive acoustic source localization in urban environments.

Area of Science:

• Acoustics
• Signal Processing
• Wave Propagation

Background:

• Acoustic wave propagation faces challenges from environmental obstacles like terrain and buildings.
• These obstacles cause complex phenomena including multiple paths, reflections, and diffraction, complicating localization.

Purpose of the Study:

• To propose and evaluate matched field processing (MFP) for acoustic localization in two-dimensional scattering environments.
• To investigate the impact of model parameter uncertainties on localization accuracy.

Main Methods:

• Utilized numerical techniques to model complex acoustic propagation where analytical solutions are infeasible.
• Investigated uncertainties in array location, sound speed, and inter-array coherence.
• Employed spatially distributed, multiarray networks and multiarray inverse filter processing techniques.

Main Results:

• Matched field processing demonstrated effectiveness in acoustic localization within scattering environments.
• Spatially distributed multiarray networks were shown to mitigate the adverse effects of parameter uncertainty.
• Multiarray inverse filter processing proved more accurate and flexible than traditional methods like time reversal.

Conclusions:

• MFP is a viable and robust method for acoustic localization in complex, scattering environments.
• Multiarray systems enhance localization performance by reducing sensitivity to environmental and system parameter uncertainties.
• Advanced multiarray inverse filter techniques offer superior accuracy and flexibility over simpler MFP approaches.