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1

The best fit time domain solution can be found by constructing two two basis vectors with your known frequency and calculate the coefficients directly. The magnitude and phase can then be directly determined from these values. Let C be a vector of cosine values over your frame and S be a vector of sine values. You then want to find $(a,b)$ so that $aC + ... 0 Assume that the input to a simple up-sampler (say by$L$) is$x[n], hence $$y[n] = \begin{cases} { x[n/L] ,\qquad n=kL \\ 0 , \qquad \qquad \ \text{ else } }\end{cases}$$ If you compute the auto-correlation sequence, defined as $$\phi_{xx}(i,j) = \mathbb{E}(x[i]x[i+j])$$ for an iid white Gaussian process, we have that \begin{align} \phi_{xx}(i,j) &= ... 2 Assuming that the sensors share the same characteristics, have the same timing (acceleration signals are aligned), the model withy_1= x + n_1$and$y_2= x + n_2$,$n_1$and$n_2$being uncorrelated noises of the same power, averaging them is a way to reduce the noise. The theory that asymptotically, averaging$N$sensors reduce the variance by a factor of$...

1

It may be useful to have a deeper understanding of you satellite's dynamics. What I mean is that by observing your attitude quaternion error variation DSP ( or using the motion mathematical model), you may be able to choose a convenient filtering type/tuning to get rid of most possible white noise.

0

I'm not comfortable with either of your two equations, in part because you don't define any of the three variables involved, but also because I think there's an easier way to understand $E_b/N_0$ and simulate it. I think the most important thing to know about $E_b/N_0$ is that it is measured at the matched filter's output. The first consequence of this is ...

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You can use cv2.PSNR like this example: import cv2 img1 = cv2.imread('img1.bmp') img2 = cv2.imread('img2.bmp') psnr = cv2.PSNR(img1, img2)

2

I'd do some small adjustments to your idea (You really nailed them). Assumptions The Signal Model - Signal + Additive White Gaussian Noise (AWGN) Probably we could generalize it more but this is beyond the scope of this question. The DFT of the signal contains Peaks with relatively small roll off This is important as we're almost saying the Signal is a ...

2

There are indeed many peak detection algorithms, and no clear consensus on which ones are "good" or "bad". But for what it's worth, your approach makes sense. Using median or other quantiles to detect sparse signals is common, e.g. the "median clipping" stage in Lasseck (2014), Large-scale identification of birds in audio recordings. In effect, you're ...

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turn to float first!!!!!!!! turn to float first!!!!!!!! turn to float first!!!!!!!! def compute_psnr(img1, img2): img1 = img1.astype(np.float64) / 255. img2 = img2.astype(np.float64) / 255. mse = np.mean((img1 - img2) ** 2) if mse == 0: return "Same Image" return 10 * math.log10(1. / mse)

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Yes, you can apply deep learning to peak detection. A 1D CNN would be appropriate for this task. Here is an example for such application: Risum, Anne Bech, and Rasmus Bro. "Using deep learning to evaluate peaks in chromatographic data." Talanta 204 (2019): 255-260. You would need to have annotated data. If you decide to stick with the classical ...

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This is a beautiful way of noise/interference cancellation technique, please go through the link that I am sharing here. Certainly, you may find a way to implement the concept. https://drive.google.com/file/d/0B2bUtLEhrWp8Wi1JZzdub0U2Wm9JWlZEX290cHByZi1ES3FZ/view?usp=sharing

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