Ultrasonic Transducer Adaptive Background Learning for Motion Detection
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Solution Overview
Problem
Ultrasonic transducers face difficulties in detecting moving objects in indoor environments due to natural variability in returned signals caused by factors like sensor noise, temperature variations, and air flow, leading to frequent false positives and negatives.
Innovation Solution
Adaptive background learning techniques are employed to normalize the received signals by removing unwanted variability, allowing for better detection of moving objects by distinguishing their signals from the constant background, and enabling automatic adaptation to changes in the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ultrasonic transducers are used to detect moving objects in indoor environments, then motion detection capability is provided, but false positives and false negatives occur frequently due to signal variability
Solution Approach 1:
The system performs preliminary background scanning to establish a baseline representation of the environment before detecting moving objects. This preliminary action captures the static background characteristics, which are then subtracted from subsequent signals to isolate moving objects and eliminate false detections caused by environmental variability.
Solution Approach 2:
The system continuously updates the background model based on recent scans, creating a dynamic feedback mechanism. When the environment changes, the background representation adapts by incorporating new information while filtering out transient variations, thereby maintaining detection accuracy over time and reducing false positives.
2Measurement precision
If larger passive infrared sensors are used to improve motion detection quality, then detection accuracy is enhanced, but device size increases
Solution Approach 1:
The patent replaces passive infrared sensors with ultrasonic transducers for motion detection. This substitution uses acoustic waves instead of thermal radiation detection, enabling motion detection functionality while significantly reducing device size and allowing integration into compact form factors.
Solution Approach 2:
The system changes the detection parameter from thermal infrared signals to ultrasonic acoustic signals. This parameter change allows the use of smaller transducers while maintaining detection capability, as ultrasonic waves can effectively detect motion through air coupling without requiring large sensor arrays.
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
This approach enhances the ability to detect moving objects with greater accuracy, reducing false positives and negatives, and allows for the use of smaller ultrasonic transducers to replace or complement larger passive infrared sensors, improving motion detection quality and device size.
Implementation Method 1
ultrasonic transducers emit signals in the ultrasonic range... transducers which both transmit sonic signals and receive sonic signals
Implementation Method 2
Piezoelectric Micromachined Ultrasonic Transducers (PMUTs), which may be air-coupled, are one type of sonic transducer, which operates in the ultrasonic range
Data Source
AI summary
A device comprises a processor coupled with an ultrasonic transducer which is configured to emit an ultrasonic pulse and receive corresponding returned signals associated with a distance range of interest in a field of view of the ultrasonic transducer. The processor is configured to: remove a low frequency component from the returned signals to achieve modified returned signals; calculate, from the modified returned signals, a variation in amplitude; determine a quantification of the variation in amplitude for a first subset of the modified returned signals associated with a first subrange of the distance range of interest; employ the quantification to correct for changes in the first subset to achieve first normalized sensor data for the first subrange, where the first normalized sensor data is sensitive to occurrence of change over time in the first subrange; and detect a moving object in the first subrange using the first normalized sensor data.