Ultrasonic Sensor Signal Adjustment via Adaptive Thresholding

The method pre-calculates and stores reference curves during system operation or calibration phases. These reference curves represent expected signal patterns under various conditions. During actual measurement, the system compares current signals against these pre-established references, significantly reducing real-time computational requirements while maintaining high detection accuracy.

The system automatically updates and adapts its own reference curves based on accumulated measurement data and environmental conditions. The threshold value function self-adjusts by analyzing deviation values between measured signals and reference patterns, enabling the system to improve its detection accuracy autonomously without external intervention or complex manual calibration.

The method combines signals from multiple ultrasonic sensors and processes them through a unified threshold value function that considers spatial relationships and signal correlations. By merging sensor data and using reference curves that account for multi-sensor configurations, the system achieves high detection reliability with optimized sensor arrangements, avoiding the need for excessive sensor quantities.

The threshold value function is designed to be universal, handling data from single or multiple sensors through the same computational framework. The reference curve approach works equally well for one sensor or many sensors, allowing the system to scale reliability by adding sensors without proportionally increasing processing complexity, as the same algorithm adapts to any sensor configuration.