Acoustic Sensor Array Vehicle Signature Isolation
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Solution Overview
Problem
Existing acoustic monitoring systems face challenges in distinguishing and isolating individual vehicle signals from multiple vehicles due to interference in sound recordings.
Innovation Solution
A system using an array of acoustic sensors that transforms time records into spectrogram matrices, calculates bearing for each time-frequency point, and applies a movement model to form a Hough transform matrix, allowing for the identification of unique vehicle parameters like velocity and time of closest approach, thereby isolating individual vehicle signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If acoustic sensors record sound from multiple vehicles simultaneously, then the system can monitor multiple vehicles, but the sound from one vehicle interferes with detecting sound from another vehicle
Solution Approach 1:
The patent segments the mixed acoustic signal from multiple vehicles into individual vehicle signatures by transforming the time-domain signal into frequency-domain representations (spectrograms). The system divides the complex acoustic environment into separable frequency components that can be individually analyzed and attributed to specific vehicles based on their unique acoustic fingerprints.
Solution Approach 2:
The patent transitions from analyzing signals in the time domain to the frequency domain using spectrogram analysis. This dimensional transformation allows the system to separate overlapping vehicle signals by examining their frequency content over time, creating an additional dimension (frequency) for signal differentiation and isolation.
2Measurement precision
If the system processes acoustic data to identify individual vehicle signatures, then detection accuracy improves, but processing complexity increases
Solution Approach 1:
The patent applies preliminary signal processing steps including Fourier transforms to convert time-domain signals into frequency-domain spectrograms before analysis. By pre-processing the acoustic data into a structured frequency representation, the system prepares the signals in advance for more efficient pattern recognition and vehicle signature identification.
Solution Approach 2:
The patent introduces spectrogram analysis as an intermediary step between raw acoustic signal capture and vehicle identification. This intermediate frequency-domain representation serves as a mediator that simplifies the complex task of separating individual vehicle signatures from mixed acoustic signals by providing a structured view of frequency content over time.
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
Effectively detects and isolates individual vehicle signals, reducing false alarms and improving the accuracy of vehicle detection by associating acoustic data points with specific vehicle trajectories.
Implementation Method 1
An array of acoustic sensors generates a plurality of time records. The time records are transformed into spectrogram matrices
Data Source
AI summary
A system for isolating signals from individual vehicles. An array of sensors generates a plurality of time records. The time records are transformed into spectrogram structures, and for each time-frequency point in the spectrogram structures, a bearing is calculated, to form a movement structure. A Hough transform is used to fit a movement model to the movement structure, to form a Hough transform structure. Peaks in the Hough transform structure correspond to vehicles and their position in the Hough transform structure correspond to motion parameters, such as the velocity of the vehicle, and the time of closest approach of the vehicle to the array of acoustic sensors. A model of the motion of a vehicle is then used to associate, with the vehicle, points (e.g., structure elements in the spectrogram structures) from the data.