Discrete Fourier Transform (DFT) Archives | Page 2 of 4

Signal frequency and analysis frequency are not the same, resulting in DFT leakage and spectrum sampling away from the peak. The spectrum shown here is oversampled by 3 through 3x zero-padded FFT

October 16, 2026 DSP

Peak Interpolation for Sub-Bin Accuracy in Radar and Communications

In signal processing applications, one frequently encounters the problem of estimating the frequency of a complex sinusoid embedded in noise. For example, a DSP engineer will encounter this problem in the delayed echo of an FMCW radar that directly translates into target range, or in carrier frequency estimation of a wireless communication system after timing synchronization has already been achieved (see Chapter 6 for details of how modulation removal exposes the underlying complex sinusoid). Beyond communications and radar, this frequency estimation is also heavily relied upon in digital pitch tracking, speech synthesis, active and passive sonar detection, Doppler ultrasound and

A bank of N filters each centered at discrete frequency k/N

March 19, 2020 DSP

Discrete Fourier Transform (DFT) as a Filter Bank

We have discussed before what a Discrete Fourier Transform (DFT) is and how to find the DFT of some commonly used signals. Here, we will see how a DFT acts as a (crude) bank of filters that can pass the signal contents around a desired frequency while blocking the rest. Let us start with the definition of the DFT. \begin{equation*} \begin{aligned} S_I[k]\: &= \sum \limits _{n=0} ^{N-1}\left[ s_I[n] \cos 2\pi\frac{k}{N}n + s_Q[n] \sin 2\pi\frac{k}{N}n\right] \\ S_Q[k] &= \sum \limits _{n=0} ^{N-1}\left[ s_Q[n] \cos 2\pi\frac{ k}{N}n – s_I[n] \sin 2\pi\frac{k}{N}n\right] \end{aligned} \end{equation*} for each $k$. In complex notation, this DFT is

Symbolic representation of linear phase rotation that changes with frequency index

May 14, 2019 DSP

Effect of Time Shift in Frequency Domain

Children usually ask questions like “How many hours have passed?” And they have no idea about the start time to be taken as a reference. Just like the zero of a measuring tape, a zero reference for time plays a crucial role in analyzing the signal behaviour in time and frequency domains. Until now, we assumed that reference time $0$ coincides with the start of a sine and a cosine wave to understand the frequency domain. Later, we will deal with symbol timing synchronization problem in single-carrier systems and carrier frequency synchronization problem in multicarrier systems, both of which address

OFDM slices the spectrum just like a bread

May 1, 2017 Wireless and SDR

A Beginner’s Guide to OFDM

In the recent past, high data rate wireless communications is often considered synonymous to an Orthogonal Frequency Division Multiplexing (OFDM) system. OFDM is a special case of multi-carrier communication as opposed to a conventional single-carrier system. The concepts on which OFDM is based are so simple that almost everyone in the wireless community is a technical expert in this subject. However, I have always felt an absence of a really simple guide on how OFDM works which can prove useful for technical persons not wanting to deal with too much technicalities, such as DSP experts outside communications, computer programmers, ham

Beat frequency sinusoid and its spectrum

October 31, 2023 DSP

Spectrum of a Sinusoid

In this article, we derive the spectrum of a complex sinusoid that acts as the basis for all spectra. In fact, the very definition of the Fourier Transform, whether continuous or discrete, comes from the perspective of a complex sinusoid. Therefore, exploring this derivation will be useful in everything else we learn about DSP. For an in-depth understanding of complex signals and I/Q processing, you can read the following two articles (the option of downloading them as PDF is available). The origin of complex numbers and signals I/Q signal processing from the ground up Let us start with the continuous-time