Ambient Light Subtraction for Active Sensor Accuracy
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Solution Overview
Problem
Ambient light interferes with the sensitivity and efficacy of active sensors like proximity sensors, causing saturation current and changing reflectivity, which reduces their detection accuracy.
Innovation Solution
A method involving a sensor with an emitter and a receiver that performs three sampling processes: one to collect ambient light before and after emitting a detecting signal, calculating an average value from the ambient light data sets and subtracting it from the data set containing both ambient and detecting signals to eliminate ambient light interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ambient light detection and subtraction is performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary ambient light detection before the actual object detection. The first sampling process measures ambient light when the emitter is off, and this preliminary measurement is stored for later subtraction from the second sampling process results, thereby eliminating ambient light interference and improving detection precision
Solution Approach 2:
The patent applies different sampling strategies to different time periods: the first and third sampling processes (when emitter is off) use one measurement approach to capture ambient light, while the second sampling process (when emitter is on) uses another approach to capture both ambient light and object reflection. This localized differentiation allows precise subtraction of ambient light components
2Measurement precision
If multiple sampling processes are performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent employs periodic sampling with the emitter alternately switching on and off between the first and second sampling processes. This periodic action allows the system to efficiently capture ambient light measurements at specific intervals and subtract them from the total measurements, achieving precise ambient light elimination without requiring continuous measurement
Solution Approach 2:
The first sampling process is performed preliminarily before the second sampling process to capture ambient light baseline data. This preliminary action allows the system to prepare ambient light reference values in advance, which are then subtracted from the second sampling results, reducing the need for extended measurement periods
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 reduces 90% of flicker noise interference, enhancing the accuracy of active sensors by minimizing ambient light impact.
Implementation Method 1
an emitter in the sensor radiates an electromagnetic field or electromagnetic waves
Implementation Method 2
a detector in the sensor detects the variation in electromagnetic energy or receives the electromagnetic waves reflected by an object
Implementation Method 3
the receiver receives a first ambient light signal, which is converted into a first data set by the processor
Data Source
AI summary
A method for ambient light subtraction providing a sensor having an emitter, a receiver, and a processor; performing a first sampling process, during which the emitter does not emit signals, the receiver receives a first ambient light signal converted into a first data set by the processor; performing a second sampling process, during which the emitter emits a detecting signal, the receiver receives a second ambient light signal and the detecting signal converted into a second data set by the processor; performing a third sampling process, during which the emitter does not emit signals, the receiver receives a third ambient light signal converted into a third data set by the processor; deriving an average value by calculating the average of the first data set and the third data set; and deriving a difference value by calculating the difference between the second data set and the average value.