Noise Filtering in Sensor Data

In robotics and embedded systems, sensor data often contains noise that can lead to inaccurate readings and poor decision-making. This lesson explores various noise filtering techniques to improve data quality.

• **Sensor Noise**: Unwanted disturbances that affect sensor readings.
• **Signal Processing**: Techniques used to analyze and manipulate signals.
• **Filtering**: The process of removing unwanted components from a signal.

1. **White Noise**: Random noise with equal intensity across all frequencies.
2. **Gaussian Noise**: Noise that follows a Gaussian distribution.
3. **Impulse Noise**: Sudden spikes in data due to interference.

Common filtering techniques include:

• **Low-pass Filter**: Allows signals below a certain frequency to pass while attenuating higher frequencies.
• **Kalman Filter**: An algorithm that uses a series of measurements observed over time to estimate unknown variables.
• **Median Filter**: A non-linear filter that replaces each element with the median of its neighbors.

Low-pass Filter Example

float lowPassFilter(float input, float prevOutput, float alpha) {
    return alpha * input + (1 - alpha) * prevOutput;
}
                

Median Filter Example

#include 

void medianFilter(int *data, int size) {
    // Sort the data array
    sort(data, data + size);
    // Return the median value
    return data[size / 2];
}
                

• Collect data at a higher frequency to improve filtering accuracy.
• Choose the right filter based on the type of noise and application requirements.
• Regularly calibrate sensors to maintain accuracy.