Wide-band Acoustic Holography Ghost Source Suppression
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Solution Overview
Problem
Existing acoustic holography methods suffer from severe ghost sources and underestimation of sound fields, particularly when the average sensor spacing in the array is larger than half the wavelength of the sound waves, leading to reduced reliability and accuracy in sound source localization.
Innovation Solution
A method that uses a model of elementary waves with iterative minimization to suppress ghost sources by adjusting the magnitude of non-selected waves to zero or reducing their magnitude, focusing on the strongest sources first and gradually increasing the dynamic range to include weaker sources, while using a set of model parameters to compute sound field properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If prior art acoustic holography methods (SONAH, ESM) are used with irregular arrays and average sensor spacing larger than half wavelength, then the method can be applied to practical measurement conditions, but severe ghost sources appear and reliability of sound source localization is reduced
Solution Approach 1:
The patent applies preliminary action by performing model selection and parameter estimation in a specific sequence: first selecting the number of sources and their positions, then estimating source strengths. This staged approach prevents ghost sources from contaminating the entire solution process, thereby maintaining reliability while working with practical irregular arrays
Solution Approach 2:
The patent segments the inverse problem into distinct steps: (1) selecting the number of sources, (2) selecting source positions, and (3) estimating source strengths. This segmentation allows each sub-problem to be solved independently with appropriate methods, preventing the propagation of errors that would otherwise create ghost sources and improve localization reliability
2Quantity of substance
If prior art acoustic holography methods are used with average sensor spacing larger than half wavelength, then measurements can be performed with fewer sensors, but the estimated sound field values are too low and accuracy is reduced
Solution Approach 1:
The patent changes parameters by using a selection criterion based on information theoretic measures (AIC, BIC) to determine the number of sources, rather than assuming a fixed number. This parameter selection approach adapts to the actual measurement conditions and sensor spacing, enabling accurate sound field estimation even with fewer sensors spaced larger than half-wavelength apart
Solution Approach 2:
The patent introduces dynamics by making the model order (number of sources) and source positions variable parameters that are selected based on the measurement data and criteria, rather than being fixed beforehand. This dynamic adaptation allows the system to maintain measurement precision even when using fewer sensors with larger spacing
3Ease of manufacture
If the number of sensors is reduced to降低成本, then cost of measurement system is reduced, but the estimation problem becomes more ambiguous and heavily underdetermined
Solution Approach 1:
The patent extracts and removes the ambiguity from the estimation problem by using model selection criteria to identify the true number of sources and their positions before estimation. This extraction of uncertain parameters and their determination through criteria-based selection simplifies the subsequent estimation task, making it tractable even with reduced sensor numbers
Data Source
Figure 1~3
AI summary
A method of determining a property of a sound field from a plurality of measured sound field parameters, the method comprising: receiving a plurality of measured sound field parameters each indicative of a sound field parameter measured by a sensor of an array of sensors, the sensor being positioned at a measurement position; providing a model of a sound field, the model comprising a set of elementary waves and having associated with it a set of model parameters; computing, from the model, computed values of the sound field parameter at each of the measurement positions and as a function of said model parameters; determining a set of parameter values of the model parameters so as to reduce an error measure by performing an iterative minimization process including a plurality of iterations, the error measure comprising an error term operable to compare the computed and the measured sound field parameters; computing the property of the sound field from the determined set of model parameters.