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I will focus on the reason of the factor $1/2$ and leave aside the estimation things. The exact understanding should be : if a scalar Gaussian random variable (rv) is circular symmetric, its real and imaginary parts must be uncorrelated (this is equivalent to independence if they are assumed jointly Gaussian) and identically distributed with zero mean. Thus,...

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One approach would be to cast the problem as least-squares smoothing. The idea is to locally fit a polynomial with a moving window, then evaluate the derivative of the polynomial. This answer about Savitzky-Golay filtering has some theoretical background on how it works for nonuniform sampling. In this case, code is probably more illuminating as to the ...

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Read the original paper: Schmidt, R. O. "Multiple Emitter Location and Signal Parameter Estimation." IEEE Transactions on Antennas and Propagation. Vol. AP-34, March, 1986, pp. 276–280 You may also want to look up "Pisarenko's Method", "Prony's Method" and read about related problems such as ESPRIT (Roy, R.; Kailath, T. (1989). "Esprit - Estimation Of ...

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I've never seen the word "Formula" with "AMDF". My understanding of the definition of AMDF is $$Q_x[k,n_0] \triangleq \frac{1}{N} \sum\limits_{n=0}^{N-1} \Big| x[n+n_0] - x[n+n_0+k] \Big|$$ $n_0$ is the neighborhood of interest in $x[n]$. Note that you are summing up only non-negative terms. So $Q_x[k,n_0] \ge 0$. We call "$k$" the "lag". clearly if ...

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Common Approaches for Commercial Denoisers Commercial denoisers are different than what you'd see on most papers. While on papers the results are mostly using objective metrics (PSNR / SSIM) and are evaluated vs. Additive White Gaussian Noise (AWGN) with high level of noise real world images are mostly with moderate level of noise with Mixed Poisson Gaussian ...

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Are you sure you shouldn't be using numpy.correlate instead of numpy.convolve? To estimate delay, you want to cross-correlate your signals, not convolve them. You'll possibly end up with a much larger delay by convolving. Trying something simple: x = [1, 0, 0, 0, 0 ]; y = [0, 0, 0, 0, 1 ]; conv = numpy.convolve(x,y); conv array([0, 0, 0, 0, 1, 0, 0, 0, ...

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Slope from all samples obtained To summarize the question's problem, you want to calculate the slope based on all samples obtained thus far, and as new samples are obtained, update the slope without going through all the samples again. On the page you cite is the equation for calculation of the slope $m_n$ that together with $b_n$ minimizes the sum of ...

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Maximum likelihood (ML) estimator Here will be derived a maximum-likelihood estimator of the power of the clean signal, but it doesn't seem to be improving things in terms of root mean square error, for any SNR, compared to spectral power subtraction. Introduction Let's introduce the normalized clean amplitude $a$ and normalized noisy magnitude $m$ ...

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Well, because in a lot of real world problems this If I have a deterministic signal with a fixed number of samples, shouldn't I be able to directly determine its spectral information? is just not the case. Very often, measured signals are more of a random process. A simple and common case would be to have the desired signal and some additive noise, very ...

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Given measurements \begin{align} Z_1 &= x + N_1\\ Z_2 &= x + N_2 \end{align} where $N_1$ and $N_2$ are independent zero-mean Gaussian random variables with variances $\sigma_1^2$ and $\sigma_2^2$ respectively, it can be shown that the minimum-mean-square-error (MMSE) estimate of $x$ in terms of $Z_1$ and $Z_2$ is a linear function of $Z_1$ and ... 6 Samples of colored noise (taken at different times) generally are correlated random variables because the autocorrelation function of the noise process is not a delta function as it is in the case of white noise. Thus, if we assume a zero-mean process (noise is generally assumed to be regardless of its color), then the covariance of two signals separated in ... 6 You want a method that removes noise while preserving edges. This cannot be achieved well by linear filtering, as you noticed yourself. I know of two approaches that might work well for your problem. The first is median filtering, where samples inside a window are replaced by their median. The following plot shows the result of median filtering with a window ... 6 So this is just the start of an answer. I'll have to keep updating it as I go. The first attempt is to say that the quantities you are interested in are the location of the center of the four LEDs, and the roll, pitch, and yaw (rotation angles) of the LEDs. That means your Kalman FIlter state will be: \mathbf{x}_k = \left[x_k\ y_k\ \alpha_k\ \beta_k\ \... 6 Well, in continuous time, a sinusoid with a bias can be seen as the output of the linear system \begin{align*} \begin{bmatrix}\dot x_1\\\dot x_2\\\dot x_3\end{bmatrix} &= \begin{bmatrix}0 & 1 & 0\\-\omega^2&0&0\\0 &0 &0\end{bmatrix}\begin{bmatrix}x_1\\x_2\\x_3\end{bmatrix}\\ y &= \begin{bmatrix}1&0&1\end{bmatrix} \... 6 After signal detection, how to estimate the clean signal s(t)? Matched filtering is used to detect the presence of a known signal in noise. There is no estimation part when you are talking about a matched filter. The estimate part comes after you have done the matched filter and need to estimate the symbols. It looks like you are talking about a ... 5 The first algorithm that springs to mind is the Goertzel Algorithm. That algorithm usually assumes that the frequency of interest is an integer multiple of the fundamental frequency. However, this paper applies the (generalized) algorithm to the case you are interested in. Another problem is that the signal model is incorrect. It uses 2*%pi*(1:siglen)*(Fc/... 5 I'm unsure if this is the answer you are looking for, but why not save and share |H_i|^2 in addition to the least squares estimates? If you have w_1^*=\frac 1 {|H_1|^2} H_1^t d_1, w_2^*=\frac 1 {|H_2|^2} H_2^t d_2, and also know |H_1|^2 and |H_2|^2, you get the total least squares estimate with: w^*=\frac 1 {|H_1|^2+|H_2|^2} (H_1^t d_1 + H_2^t ... 5 I assume that you have a wide-sense stationary discrete-time input noise signal x(n), a linear time-invariant filter with impulse response h(n), and an output noise process y(n). If we assume that x(n) is zero mean (which is not necessary, but makes it easier) and if we model x(n) as perfectly band-limited, we get for its variance\sigma_x^2=\... 5 Complex A and B are a bit messy. What about using the signal model $$f(t) = \sum_{k=1}^{2} A_k\, e^{j(w_k t + \phi_k)}$$ wherew$is the frequency and$\phi$is phase. Since sinusoids are orthogonal, probably the easiest thing to do is to Fourier transform the signal and pick out the two largest peaks in the positive part of the spectrum. These will ... 5 The usual way to estimate the amplitude of a particular frequency is to use the Goertzel algorithm. There is a good write-up by Rick Lyons here. Even though Rick's writeup is about single tone detection, it can be applied when multiple tones are present, too. 5 1) It depends on the application, but in general, both. In some cases we can whiten the noise, say in the following situation: $$Y = x+ \eta\text{ where }\eta \sim N(0, \Sigma)$$ where we want to estimate$x \in \mathcal{A}$for some set$\mathcal{A}$from$Y$. In this case, we multiply both sides by the inverse of the square root of$\Sigma$, and we have ... 5 To answer your final question: Why is it that the estimation community ignores the use of a discrete lowpass filter for estimation? As far as I can tell it's the best approach to estimate the signal above. That's because you're feeding them the wrong signal model. TL;DR: Use the right tool for the job! Gory Details Any time you start in with the ... 5 Hi: I'll try to answer as briefly as possible and only with respect to statistics. not dsp. In statistics, if you have a nice pdf such as the normal distribution, then maximizing the likelihood is equivalent to minimizing the sum of squares of the residuals ( often called errors ). In other cases, where you either have a complicated distribution ( maybe ... 5 You have a set of message set$m_i$,$0 \le i \le N-1$. (For example, QPSK will be$N=4$). For the transmitted message$m_i$, the corresponding symbol vector is$\textbf{x}_i$, and the received symbol vector is$\textbf{y} = \textbf{x} + \textbf{w}$, where$\textbf{w}$is the AWGN at the receiver. The above is a simplified baseband model assuming a simple ... 4 In ideal world you'd have the correct model and use it. In your case, the model isn't perfect. Yet the steps you're suggesting are based on a knowledge you have about the process - which you should incorporate into your process equation using your dynamic model matrix: The classic and correct way given F matrix is built correctly according to your knowledge.... 4 The Wiener Filter can also be derived by another (Easier) way. Let's assume the following model: $$y = h \ast x + n$$ Namely the data is a result of a linear combination (Convolution) of$ x $with Additive Noise. If we assume the noise model is Gaussian and our data is also formed by a Gaussian distribution then we should try to minimize (MAP ... 4 Generally, the frequency estimation problem decouples from the amplitude + phase estimation problem. As I said in the comments, you could just do: $$\hat{A} = \left|\sum_{n=0}^{N-1} x_n e^{-j 2\pi\hat{f} n} \right|$$ where$\hat{f}\$ is your frequency estimate. Regarding your issues: because the number of points isn't high enough to hit the right ...

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If you are willing to use multiple neighboring FFT bins, not just 2, then windowed Sinc interpolation between the complex bin results can produce a very accurate estimate, depending on the width of the window. Windowed Sinc interpolation is commonly found in high quality audio upsamplers, so papers on that subject will have suitable interpolation formulas ...

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If you use Flanagan [1] it is computed from the phase difference of successive phase spectra Δϕ (Instantaneous Frequency) and if you reconstruct the magnitude using a correct factor (Instantaneous Magnitude) [2] use a normalized sinc function: $$\frac{\sin( \pi x ) }{ (\pi x)}$$ And at the end use Parabolic interpolation around the peak magnitude you can ...

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I doubt that there is such a rule because your assumption- the negative of computational work more than offsets the benefits of lower error probabilities- assumes that computations are expensive and errors are not. The "costs" of computational work and errors depends entirely on the application.

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