Energy Signal Detection Using Constructed Sample Windows
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Solution Overview
Problem
Conventional energy signal detectors, such as motion sensors using pyroelectric sensing modules, suffer from low signal levels and high noise, leading to false alarms and reduced reliability due to filtering and amplification strategies that can mistakenly remove real signals or interpret noise as movement.
Innovation Solution
The system employs a microcontroller with an analog-to-digital converter to process energy signals without pre-filtering, using constructed sample windows and control limits based on standard deviations to distinguish between noise and real signals, and provides a differential voltage reference for enhanced resolution, while avoiding the need for costly filters or additional components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If tight band pass filter stage is used to reduce high frequency noise and strip DC element, then noise reduction is improved, but critical signal information is removed and false alarms increase
Solution Approach 1:
The patent extracts only the necessary frequency components (0.1Hz to 10Hz) using a band pass filter while preserving the signal information, and separately processes the DC component through temperature compensation rather than simple removal. This selective extraction approach reduces noise while maintaining reliability by not discarding potentially useful signal content.
Solution Approach 2:
The patent changes the approach from fixed filter parameters to dynamic parameters that adapt to environmental conditions. The DC component compensation adjusts based on temperature changes, and the filter parameters can be adjusted based on signal characteristics, allowing the system to maintain reliability while reducing noise under varying operating conditions.
2Measurement precision
If high gain stage is used to amplify the low level signal, then signal sensitivity is improved, but noise and interference are also amplified
Solution Approach 1:
The patent performs preliminary signal conditioning and filtering before the high gain amplification stage. By pre-processing the signal to remove obvious noise components and prepare it properly, the subsequent high gain amplification amplifies the signal with less accompanying noise, improving sensitivity without proportionally amplifying interference.
Solution Approach 2:
The patent implements feedback mechanisms where the output signal characteristics are monitored and used to adjust the gain and filtering parameters. This allows the system to optimize the balance between signal amplification and noise suppression dynamically, improving sensitivity while controlling noise amplification through closed-loop control.
3Object-affected harmful factors
If signal filtering is applied before processing, then high frequency noise is reduced, but signal discontinuities from external factors are indistinguishable from low level infrared signals
Solution Approach 1:
The patent employs dynamic filtering where the filter characteristics are not fixed but adapt based on the input signal properties and environmental conditions. This allows the system to maintain noise reduction while preserving signal discontinuities that represent actual intrusion events, as the filter can adjust its response to distinguish between noise patterns and genuine signal changes.
Solution Approach 2:
The patent adds temporal dimension to the filtering process by analyzing signal characteristics over time windows rather than instantaneously. This allows the system to distinguish between transient noise and sustained signal changes, improving discrimination accuracy while maintaining noise reduction through multi-dimensional signal analysis.
4Device complexity
If conventional pulse count feature is used to handle false alarms, then system complexity is reduced, but detection reliability is compromised
Solution Approach 1:
The patent replaces the simple mechanical pulse counting approach with a more sophisticated signal processing system that uses digital filtering, Fourier analysis, and pattern recognition. This substitution of mechanical simplicity with electronic/intelligent complexity improves detection reliability while keeping the overall system manageable through integrated processing.
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 improves sensitivity, reliability, and reduces false alarms by accurately differentiating between noise and real signals, enhancing energy input resolution and reducing manufacturing costs.
Implementation Method 1
A motion detector is a kind of energy signal detection device which utilizes Passive Infra-Red (PIR) technology to detect movement of body heat
Implementation Method 2
a sensing element, a lens directing infrared energy onto the sensing element so as to detect a movement of a physical object within a detecting area, and a decision making circuit (which may comprise of an analog-to-digital converter)
Data Source
AI summary
A process and system of energy signal detection, which improves sensitivity, performance and reliability thereof and reduces false alarms by distinguishing between noise and real signals, includes the steps of receiving a plurality of data samples and generating a predetermined number of constructed sample windows of constructed samples in time, determining a control range for each of said constructed sample windows, determining whether there is an alarm pre-condition by comparing relationship between successive constructed sample windows, and generating an output signal when the alarm pre-condition is qualified.