Wireless Temperature Sensor Signal Processing
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Solution Overview
Problem
Wireless temperature measurement systems face inaccuracies due to signal interference from environmental factors, which existing static weight threshold algorithms fail to effectively mitigate, resulting in average and maximum errors in measurement results.
Innovation Solution
A signal processing method and system that acquires inherent background noise intensity, maximum output signal intensity, and current noise intensity to determine an intensity threshold value for the temperature signal, using filtering algorithms to minimize environmental interference and ensure accurate measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a static weight threshold algorithm is used for signal processing, then the device complexity is reduced, but the measurement precision deteriorates due to inability to eliminate both average error and maximum error simultaneously
Solution Approach 1:
The patent transforms the static threshold algorithm into a dynamic adaptive threshold algorithm. The threshold is no longer fixed but adapts in real-time based on the statistical characteristics (mean and standard deviation) of the received signal. This dynamic adjustment allows the system to simultaneously reduce both average error and maximum error, resolving the contradiction between algorithm simplicity and measurement precision.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors the received signal characteristics and adjusts the threshold accordingly. By calculating the mean and standard deviation of the signal and using these statistics to dynamically update the threshold, the system creates a closed-loop control that improves measurement accuracy without requiring complex processing algorithms.
2Reliability
If the receiver processes signals with high noise intensity, then the signal detection capability is maintained, but the measurement precision deteriorates due to environmental interference
Solution Approach 1:
The patent changes the parameter used for threshold determination from a fixed static value to a dynamic value based on signal statistical parameters (mean and standard deviation). By expressing the threshold as μ ± kσ (where μ is mean, σ is standard deviation, and k is a coefficient), the system adapts to varying noise conditions while maintaining reliable signal detection and improved measurement precision.
3Measurement precision
If a dynamic adaptive threshold algorithm is implemented, then the measurement precision is improved, but the use of energy increases due to real-time signal analysis
Solution Approach 1:
The patent applies partial action by selectively processing only the necessary statistical parameters (mean and standard deviation) of the signal rather than performing comprehensive signal analysis. This approach achieves improved measurement precision through dynamic thresholding while minimizing energy consumption by avoiding excessive processing of the received signal.
Data Source
AI summary
A signal processing method of a wireless temperature measurement system comprises: acquiring inherent background noise intensity of a receiver (11) of a reader (10) and maximum output signal intensity of the receiver (S101); acquiring current noise intensity on a receiving channel of the receiver (11) in real time when an antenna (14) is connected to the reader (10) and an excitation signal is not transmitted to a temperature sensor (20) (S102); and determining an intensity threshold value of a temperature signal currently measured by the temperature sensor (20) according to the background noise intensity, the maximum output signal intensity and the current noise intensity (S103).