FejerKernel - crowlogic/arb4j GitHub Wiki
The Fejér kernel is a type of kernel function used in analysis and signal processing. It is named after the Hungarian mathematician Lipót Fejér.
The Fejér kernel is defined as the arithmetic mean of the first $n$ Dirichlet kernels, where $n$ is a positive integer. The Dirichlet kernel is another type of kernel function used in signal processing and Fourier analysis.
A kernel function is a non-negative function that satisfies certain properties, such as symmetry and integrability. Kernel functions are used in a variety of applications, such as smoothing data, estimating probability densities, and performing Fourier analysis. The Fejér kernel is a commonly used kernel function in these types of applications because of its simplicity and computational efficiency.
The Fejér kernel is defined as follows:
$$K_n(t) = \frac{1}{n}\left(D_0(t) + D_1(t) + \ldots + D_{n-1}(t)\right)$$
where $K_n(t)$ is the Fejér kernel of order $n$, $D_k(t)$ is the $k$-th Dirichlet kernel, and the notation $\frac{1}{n}$ denotes the arithmetic mean of the enclosed terms.
The Dirichlet kernel $D_k(t)$ is defined as:
$$D_k(t) = \frac{1}{2\pi}\sum_{m=-k}^{k} e^{imt}$$
where $i$ is the imaginary unit and the sum is taken over integer values of $m$.
In signal processing and Fourier analysis, the Fejér kernel is often used as a window function, which is a function that is multiplied with a signal in the time domain to reduce the effects of spectral leakage in the frequency domain. The Fejér kernel has a smooth, symmetric shape that tapers off towards the edges, making it a good choice for this purpose.
The Fejér kernel has several important properties, including:
- It is a non-negative function that is bounded between 0 and 1.
- It is symmetric around the origin.
- It is periodic with period $2\pi$.
- As the order $n$ increases, the Fejér kernel converges uniformly to the Dirac delta function, which has important implications for Fourier analysis.
In summary, the Fejér kernel is a type of kernel function that is commonly used in mathematical analysis and signal processing. It is defined as the arithmetic mean of the first $n$ Dirichlet kernels and has several important properties that make it a useful tool for analyzing and manipulating signals.