The main function of a microcontroller unit (MCU) is to manage peripheral devices and handle communication and data processing. However, in certain scenarios, mathematical operations are necessary, even though MCUs aren't typically optimized for complex computations. This article focuses on how to implement digital filtering using a microcontroller.
When an MCU performs data acquisition, it often encounters random errors caused by external interference. These errors vary unpredictably in both magnitude and direction under the same conditions, but they follow statistical patterns over multiple measurements. To reduce these errors, hardware filters can be used, or software-based digital filtering algorithms can be implemented. Digital filtering plays a crucial role in measurement and control systems, requiring high real-time performance.
Digital filtering offers several advantages:
1. It doesn’t require additional hardware, only a computational process, which makes it reliable and eliminates impedance matching issues. It can also filter very low-frequency signals that analog filters cannot.
2. Implemented through software, digital filtering allows multiple input channels to share the same filter program, reducing system overhead.
3. The filter characteristics can be easily adjusted by modifying the algorithm, making it highly effective for removing low-frequency noise and random signals.
4. Common digital filtering techniques include limit filtering, median filtering, arithmetic average filtering, weighted average filtering, and moving average filtering.
**Limit Filtering Algorithm**
This method compares the difference between two consecutive samples with a predefined threshold A. If the difference is within the allowed range, the new sample is accepted; otherwise, the previous value is used. It's ideal for slowly changing signals like temperature or position.
**Median Filtering Algorithm**
It involves taking N samples (usually an odd number), sorting them, and selecting the middle value as the current output. This method is effective for eliminating pulse noise but not suitable for fast-changing data.
**Arithmetic Average Filtering Algorithm**
This approach calculates the average of N consecutive samples. The accuracy depends on the value of N—larger N improves smoothness but reduces sensitivity. For efficiency, N is often chosen as a power of two.
**Weighted Average Filtering Algorithm**
To balance smoothness and sensitivity, this method assigns different weights to each sample. The sum of all weights equals one, emphasizing recent samples more. It’s useful for detecting trends in signal changes.
**Moving Average Filtering Algorithm**
Instead of collecting multiple samples at once, this method uses a sliding window of N samples. Each new sample replaces the oldest, and the average is calculated continuously. It’s efficient for real-time applications and can be implemented using a ring buffer.
**Low-Pass Filtering Algorithm**
This mimics the behavior of an RC low-pass filter using a recursive formula:
Yn = a * Xn + (1 - a) * Yn-1
Where a is a small coefficient that determines the filter’s response time. It’s ideal for slow-changing signals and can be implemented efficiently in software.
In summary, digital filtering is a powerful tool for improving data quality in MCU-based systems. While many methods exist, such as first-order lag filtering or fault-tolerant redundancy, this article covers the most commonly used techniques. As technology evolves, more advanced methods will continue to emerge, and further research is needed to refine these approaches.
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