Ricardo (Ric) Losada is a software development manager in the Signal Processing & Communications group at MathWorks. Ric leads the DSP & Audio team that develops the DSP System Toolbox and the Audio Toolbox. Ric’s expertise includes single- and multi-rate filtering, spectral analysis and statistical signal processing/adaptive filtering. Ric has over 20 years of experience in developing high performance simulation and analysis software for signal processing applications.
Ric is a contributing author to two chapters of the book “Streamlining Digital Signal Processing: A Tricks of the Trade Guidebook, Second Edition” edited by Richard G. Lyons, published By Wiley & IEEE Press (2012).
High Resolution Spectral Analysis Using Channelizers
Modern spectral estimation is most often done using FFTs. Spectral windows are used to tradeoff spectral leakage for resolution (the ability to distinguish between spectral lines that are close to each other). Rectangular, flat-top, Hann, Kaiser windows (among others) are all commonly used, and each have their strengths and weaknesses. The window + FFT approach is called a (modified) periodograms and, when used in conjunction with overlapping and averaging, Welch’s method.
We discuss here an alternative to this approach using efficient polyphase-FFT filter banks (a.k.a. channelizers). Channelizers find wide application in wireless communications systems. The analysis filter bank can also be used for high resolution spectral analysis. The tradeoff between resolution and spectral leakage is much less of an issue with the channelizer approach. You’ll also get more stable and accurate peaks and a more consistent noise floor. Channelizers are the modern/digital equivalent of sweeping a narrow bandpass filter across the spectrum and computing the average power of its output. However, there is no sweeping required and no spectral window either.