How to Store ADC Data in a Float Buffer for STM32L4

Learn how to effectively store ADC data in a float buffer for the STM32L4 microcontroller, utilizing DMA and FFT techniques.
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How to Store ADC Data in a Float Buffer for STM32L4

When working with embedded systems, efficient data storage and processing are key challenges. A common scenario encountered by developers is how to handle Analog-to-Digital Converter (ADC) data, particularly when integrating it with a floating point buffer for tasks like Fast Fourier Transform (FFT) calculations. In this post, we will explore the specific issue of storing ADC data in a float buffer using STM32L4 with Floating Point Unit (FPU).

Understanding the Problem

You may find yourself in a situation where, after configuring your ADC correctly and reading data, the float buffer contains unexpected values, such as 1.51059974e-042. Despite reading integer values correctly from the ADC (using HAL_ADC_GetValue(&hadc1)), converting these directly to float does not yield usable results when processed in FFT calculations, leading to outputs that are essentially zero.

Key Points:

Correct ADC Setup: The ADC is reading values correctly, indicating there's no direct hardware failure.

Data Type Handling: The issue lies primarily in the handling of data types and the conversion from integer ADC values to float.

Conversion Expectations: Simply casting an integer value to a float might not suffice, especially for FFT algorithms that expect specific value ranges.

Solution Steps

1. Verify ADC Configuration

Before proceeding, it's essential to confirm that the ADC is set up correctly. The ADC initialization should ensure it samples the intended output, which you have already done in your MX_ADC1_Init function.

2. DMA Transfer and Callback Use

The data transfer from ADC to memory is handled by DMA. The suggestion is to convert the ADC data into float format upon the occurrence of the DMA transfer interrupt instead of casting it directly after reading the value. This avoids the problems you have encountered.

Code Snippet Example:

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3. Normalization of Data

It's important to ensure the data is normalized to the expected range of the FFT. This means adjusting the integer values according to the magnitude and scaling them appropriately. For instance, FFT typically expects floating-point values to lie within a range of -1.0 to 1.0 (for signed samples) or 0 to 1.0 (for unsigned samples).

Important Considerations:

DC Offset: If your signal has a DC offset, it must be subtracted from your signal to center it around zero.

High-Pass Filtering: Implementing a DC-blocking High-Pass filter can help eliminate offsets that are not at the midpoint of the ADC range.

4. Transforming Data for FFT Use

For floating-point FFT implementations, make sure you're passing the correctly transformed buffer. If your FFT library is designed for integer data, you may opt for it, but keep in mind that the effectiveness of the FFT may be affected by scaling and normalization issues.

Conclusion

In summary, effectively storing ADC data into a float buffer on the STM32L4 platform involves more than a simple type cast. By consolidating the conversion within the DMA transfer callback and ensuring correct normalization, you can prepare your data for accurate processing in FFT calculations. This approach will lead to meaningful results and enhance your overall project development experience.

Now, with this understanding, you can efficiently manage and process ADC data for your embedded applications!