Trajectory Estimation Using Doppler Shift and Overlapping Candidates
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Solution Overview
Problem
Existing trajectory estimation methods using microphone arrays require multiple microphones over a wide range, leading to increased costs and reduced accuracy due to short stationary periods of the sound source, and are unable to estimate the trajectory of moving bodies without shock waves or at subsonic speeds.
Innovation Solution
A trajectory estimation device that generates peak waveforms from signals detected by multiple sensors, estimates trajectory parameters using Doppler shift formulas, and calculates wave source direction candidates to determine overlapping trajectory candidates for moving bodies emitting waves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple microphone arrays are installed in a wide range to track moving sound sources, then the sound source can be tracked throughout its movement, but the cost and device complexity increase significantly
Solution Approach 1:
The patent applies dynamics by making the microphone array configuration adaptable and reconfigurable. Instead of using multiple fixed arrays, the system dynamically adjusts the parameters and configuration of microphones within arrays to track moving sound sources, allowing the same physical hardware to serve multiple positions through parameter changes rather than physical relocation or multiplication of arrays.
Solution Approach 2:
The patent utilizes parameter changes by modifying the operational parameters of the microphone arrays (such as spacing, orientation, and weighting) to maintain accurate sound source localization as the target moves. This allows the system to adapt to different sound source positions and velocities without requiring additional physical arrays, thereby reducing hardware complexity while maintaining tracking reliability.
2Area of stationary object
If the distance between microphones is increased to improve detection range, then the coverage area increases, but spatial aliasing distortion occurs
Solution Approach 1:
The patent applies dynamics by making the microphone spacing and array configuration adaptable rather than fixed. The system can dynamically adjust the effective baseline distances and processing parameters based on the sound source position and frequency content, allowing the array to maintain optimal performance across different detection scenarios without suffering from spatial aliasing at fixed large spacings.
Solution Approach 2:
The patent implements universality by designing a microphone array system that can function effectively for both near-field and far-field sound sources, and for various frequencies, through software-based parameter adjustment. The same physical array configuration can be optimized for different operational requirements by changing processing parameters, eliminating the need for multiple specialized arrays with different spacings.
3Measurement precision
If the observation section is extended to obtain sufficient data for accurate estimation, then the estimation accuracy improves, but the sound source moves out of the stationary assumption valid range
Solution Approach 1:
The patent applies dynamics by implementing time-varying parameter estimation and adaptive tracking algorithms that account for the motion of the sound source. Instead of assuming stationarity over extended periods, the system dynamically updates the sound source position and velocity estimates, adjusting the observation window and processing parameters in real-time to maintain accuracy throughout the movement trajectory.
Solution Approach 2:
The patent applies preliminary action by using predictive algorithms that anticipate the sound source position based on previously estimated trajectory parameters. The system pre-compensates for expected motion by adjusting the observation window and processing timing, allowing accurate estimation over longer durations without requiring the sound source to remain stationary.
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
Enables accurate estimation of moving body trajectories with reduced hardware requirements and improved accuracy across various speeds, including subsonic movements without shock waves.
Implementation Method 1
a parameter estimation unit that estimates, from the peak waveforms relating to the waves detected by the at least three sensors, a trajectory parameter relating to a trajectory of a moving body having a wave source of the waves
Data Source
AI summary
A trajectory estimation device that includes a waveform generation unit that generates, by using signals based on waves detected by at least three sensors, peak waveforms consisting of time series data of peak frequencies of the signals, a parameter estimation unit that estimates, from the peak waveforms relating to the waves detected by the at least three sensors, a trajectory parameter relating to a trajectory of a moving body having a wave source of the waves, and a trajectory estimation unit that estimates, for all combinations of two of the peak waveforms selected from among combinations of at least three of the peak waveforms, a wave source direction candidate for each of the waves by using the trajectory parameter, and estimate, as a trajectory of the moving body, overlapping trajectory candidates from among trajectory candidates estimated based on the wave source direction candidates.