Microphone Self-Localization Using Iterative Rank-3 Approximation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining the location of sensors, such as microphones, fail to accurately account for unknown internal delays, leading to issues with local minima and inefficiencies in localization algorithms.
Innovation Solution
An iterative approximation algorithm is employed to find a rank-3 approximation of internal delays and event times, using a criterion like the Frobenius norm to converge to a global solution, allowing for accurate computation of sensor locations and acoustic event positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional localization methods are used to determine sensor locations, then the computation is simpler, but the accuracy deteriorates due to unknown internal delays causing local minima
Solution Approach 1:
The patent performs preliminary estimation of internal delays and event times before final localization computation. This preliminary action separates the complex problem into manageable stages, where initial estimates are generated and then refined, avoiding direct confrontation with the full complexity of simultaneous unknowns.
Solution Approach 2:
The patent segments the localization problem into distinct computational stages: generating initial estimates, performing iterative approximation to find rank-3 approximation, and computing final locations. This segmentation breaks down the complex optimization problem into sequential steps that are easier to solve and converge reliably.
2Measurement precision
If iterative maximum-likelihood algorithms are used to minimize squared error criterion, then the localization accuracy improves, but the computation time increases due to convergence issues
Solution Approach 1:
The patent generates initial estimates for internal delays and event times before running the iterative maximum-likelihood algorithm. This preliminary action provides a better starting point for the iterative optimization, reducing the number of iterations needed to converge and thus decreasing computation time while maintaining accuracy.
Solution Approach 2:
The patent uses an iterative approximation algorithm that incorporates feedback from previous iterations to refine estimates of internal delays and event times. This feedback mechanism allows the system to converge to the global solution more efficiently by learning from each iteration and adjusting parameters accordingly.
3Productivity
If unknown internal delays are not considered in the localization model, then the computation is faster, but the reliability deteriorates due to systematic errors
Solution Approach 1:
The patent enables the system to self-determine internal delays and event times through the iterative approximation algorithm. Instead of requiring external calibration or known reference values, the system automatically estimates these parameters from the observed acoustic events, ensuring reliability without sacrificing computation speed.
Solution Approach 2:
The patent changes the approach from assuming known or zero internal delays to dynamically estimating these parameters as unknowns. By treating internal delays as variables to be solved for rather than fixed constants, the system achieves both reliability through accurate modeling and computational efficiency through rank-3 approximation.
Data Source
AI summary
Provided are methods and systems for finding the location of sensors (e.g., microphones) with unknown internal delays based on a set of events (e.g., acoustic events) with unknown event time. A localization algorithm may iteratively run to compute the acoustic event times, the observation delays, and the relative locations of the events and the sensors.