Signal processing APIs use dsps prefix. The following modules are available:

Basic math - the example shows how to use basic vector math operations

Dot-product - the example demonstrates how to use dot product functions

FFT - the example demonstrates how to use FFT functionality

FFT Window - the example demonstrates how to use Window and FFT functionality

FFT 4 Real - the example demonstrates how to use FFT functionality for real input signals

IIR - the example demonstrates how to use IIR filters functionality

FIR - the example demonstrates how to use FIR filter functionality

Kalman Filter - Extended Kalman Filter (EKF) example

Matrix - example demonstrates how to use Mat class functionality

This example demonstrates how to use basic math functions from esp-dsp library. The example does the following steps:

Initialize the library

Initialize input signals with 1024 samples

Apply window to input signal by standard C loop.

Calculate FFT for 1024 complex samples and show the result

Show results on the plots

Apply window to input signal by basic math functions dsps_mul_f32 and dsps_mulc_f32.

Calculate FFT for 1024 complex samples

Show results on the plots

For more details please look to the examples/basic_math/README.md

The example demonstrates how to use dotprod dsps_dotprod_f32 from esp-dsp library. Example does the following steps:

Initialize the input arrays

Calculate dot product of two arrays

Compare results and calculate execution time in cycles.

For more details please look to the examples/dotprod/README.md

This example demonstrates how to use FFT functionality from esp-dsp library. Example does the following steps:

Initialize the library

Initialize input signals with 1024 samples: one 0 dB, second with -20 dB

Combine two signals as one complex input signal and apply window to input signals paar.

Calculate FFT for 1024 complex samples

Apply bit reverse operation for output complex vector

Split one complex FFT output spectrum to two real signal spectrums

Show results on the plots

Show execution time of FFT

For more details please look to the examples/fft/README.md

This example demonstrates how to use Window and FFT functionality from esp-dsp library. Example does the following steps:

Initialize the library

Initialize input signals with 1024 samples

Apply window to input signal.

Calculate FFT for 1024 complex samples

Apply bit reverse operation for output complex vector

Split one complex FFT output spectrum to two real signal spectrums

Show results on the plots

For more details please look to the examples/fft_window/README.md

This example demonstrates how to use FFT functionality from esp-dsp library. Example does the following steps:

Initialize the library

Initialize input signals with 1024 samples: one 0 dB, second with -20 dB

Calculate FFT Radix-2 for 1024 complex samples

Calculate FFT Radix-4 for 1024 complex samples

Apply bit reverse operation for output complex vectors

Show results on the plots

Show execution time of FFTs

For more details please look to the examples/fft4real/README.md

This example demonstrates how to use IIR filters functionality from esp-dsp library. Example does the following steps:

Initialize the library

Initialize input signal

Calculate iir filter coefficients

Filter the input test signal (delta function)

Shows impulse response on the plot

Shows frequency response on the plot

Calculate execution performance

The same for LPF filter with Q factor 10

For more details please look to the examples/fir/README.md

This example demonstrates how to use FIR filter functionality from esp-dsp library. Example does the following steps:

Initialize the FFT library

Initialize input signal

Show input signal

Show filtered signal

For more details please look to the examples/fir/README.md

This example emulate system with IMU sensors and show how to use Extended Kalman Filter (EKF), with 13 values states vector, to estimate gyroscope errors and calculate system attitude. Also, this example show how to use esp-dsp library to operate with matrices and vectors.

In real system, the emulated sensors values should be replace by the real sensors values. Then, in real system, a calibration phase should be implemented and after the calibration phase the state vector X and covariance matrix P should be saved and restored next time, when filter called. It will save time for initial phase.

For more details please look to the examples/kalman/README.md

This example demonstrates how to use Mat class functionality from esp-dsp library. Example does the following steps:

Initialize a matrix A and matirx x

Calculate matrix b: b = A*x

Find roots x1: A*x1 = b, with different methods

Print result