Sensor fusion using recurrent neural network: obtaining a smoothed output
To remove the noise I'd suggest you utilize Fourier Transformation to get the frequency spectrum of the signal. Then you can apply a threshold to remove high-frequency signal components which are mainly noises.

As I understood you are using python. The code below creates an artificial noisy signal and removes it using FFT (Fast Fourier Transformation) algorithm.

import numpy as np
import matplotlib.pyplot as plt

x = np.arange(0, 10, 0.01)

y = np.sin(x)
y_noisy = y + np.random.uniform(low=0, high=0.5, size=len(y))

fft_values = np.fft.fft(y_noisy)

mean_value = np.mean(np.abs(fft_values))

threshold = 10 * mean_value  # Fine-tune this

fft_values[np.abs(fft_values) &lt; threshold] = 0

filtered_samples = np.fft.ifft(fft_values)

plt.plot(x, filtered_samples)
plt.show()


Good Luck!