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Generalized pencil-of-function method (GPOF), also known as matrix pencil method, is a signal processing technique for estimating a signal or extracting information with complex exponentials. Being similar to Prony and original pencil-of-function methods, it is generally preferred to those for its robustness and computational efficiency.
A long list of noise measures have been defined to measure noise in signal processing: in absolute terms, relative to some standard noise level, or relative to the desired signal level. They include: Dynamic range, often defined by inherent noise level; Signal-to-noise ratio (SNR), ratio of noise power to signal power
The zero-forcing equalizer applies the inverse of the channel frequency response to the received signal, to restore the signal after the channel. [1] It has many useful applications. For example, it is studied heavily for IEEE 802.11n (MIMO) where knowing the channel allows recovery of the two or more streams which will be received on top of ...
Here, / is the inverse of the original system, = / is the signal-to-noise ratio, and | | is the ratio of the pure filtered signal to noise spectral density. When there is zero noise (i.e. infinite signal-to-noise), the term inside the square brackets equals 1, which means that the Wiener filter is simply the inverse of the system, as we might ...
The regularization parameter plays a critical role in the denoising process. When =, there is no smoothing and the result is the same as minimizing the sum of squares.As , however, the total variation term plays an increasingly strong role, which forces the result to have smaller total variation, at the expense of being less like the input (noisy) signal.
Since any signal vector that resides in the signal subspace must be orthogonal to the noise subspace, , it must be that for all the eigenvectors {} = + that spans the noise subspace. In order to measure the degree of orthogonality of e {\displaystyle \mathbf {e} } with respect to all the v i ∈ U N {\displaystyle \mathbf {v} _{i}\in {\mathcal ...
In pulsed radar and sonar signal processing, an ambiguity function is a two-dimensional function of propagation delay and Doppler frequency, (,).It represents the distortion of a returned pulse due to the receiver matched filter [1] (commonly, but not exclusively, used in pulse compression radar) of the return from a moving target.
EEMD adds finite amplitude white noise to the original signal. After that, decompose the signal into IMFs using EMD. The processing steps of EEMD are developed as follows: Add finite amplitude white noise to the original signal. Decompose the noisy signal into IMFs using EMD. Repeat steps 1 and 2 multiple times to create an ensemble of IMFs.