Acoustic Position Inference Using Simulated Pseudorange Training Data
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Solution Overview
Problem
Existing position measurement techniques using machine learning require extensive collection of actual environment data and complex simulations to improve accuracy, which is time-consuming and difficult to achieve due to challenges in data collection and simulation complexity.
Innovation Solution
An information processing apparatus and method that utilizes a distance measurement signal receiving unit to receive spread spectrum modulated signals from known positions, and a position inference unit that infers position through machine learning based on pseudo distances and known positions, using a pseudo random noise simulation to generate accurate position data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If actual environment data is collected to improve position measurement accuracy through machine learning, then measurement precision is improved, but loss of time increases due to extensive data collection requirements
Solution Approach 1:
The patent creates virtual copies of actual environment data through simulation. Instead of collecting real-world data under various conditions, the system generates synthetic distance measurement data that replicates real-world scenarios including environmental factors like reflections and obstacles. This copying approach eliminates time-consuming data collection while providing sufficient training data for machine learning models.
Solution Approach 2:
The patent performs preliminary simulation to generate training data sets before actual position measurement is needed. By pre-generating virtual distance measurement data under various environmental conditions, the system prepares training data in advance, eliminating the need for time-consuming data collection during deployment and enabling immediate use of machine learning for position measurement.
2Measurement precision
If complex simulations considering all environmental influences are performed to improve position measurement accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential environmental factors needed for position measurement simulation, such as signal reflection, obstruction, and propagation delays. Instead of modeling all possible environmental influences, the system focuses on extracting and simulating the most impactful factors, thereby reducing simulation complexity while maintaining measurement accuracy.
Solution Approach 2:
The patent uses parameter changes to simplify simulation complexity. By adjusting simulation parameters to represent typical environmental conditions rather than modeling every possible scenario, the system achieves accurate position measurement without requiring excessively complex simulations. The machine learning model learns from varied parameter sets representing different environments without needing to explicitly model each condition.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Facilitates easy improvement in position measurement accuracy by machine learning, reducing the need for extensive data collection and complex simulations, while effectively handling environmental noise factors.
Implementation Method 1
a distance measurement signal receiving unit that receives a distance measurement signal including a spreading code signal obtained by performing spread spectrum modulation on a spreading code
Implementation Method 2
a pseudo distance to a plurality of distance measurement signal output blocks specified from a transmission time that is a time until the distance measurement signals of the plurality of distance measurement signal output blocks are transmitted to and received by the distance measurement signal receiving unit
Data Source
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AI summary
The present disclosure relates to a program, an information processing apparatus, and an information processing method capable of measuring, with high accuracy, the position of an electronic device in a system, the system including a plurality of devices each including a speaker and the electronic device including a microphone. A distance measurement signal including a spreading code signal obtained by performing spread spectrum modulation on a spreading code and output from a plurality of audio output blocks existing at a known position is received, and a position of the distance measurement signal receiving unit is inferred by machine learning on the basis of a pseudo distance to a plurality of distance measurement signal output blocks specified from a transmission time that is a time until the distance measurement signals of the plurality of distance measurement signal output blocks are transmitted to and received by the distance measurement signal receiving unit, and known positions of the plurality of distance measurement signal output blocks. The present disclosure is applicable to game controllers and HMDs.