Inverting and Up-channelizing Critically-Sampled Polyphase Filter Banks

Abstract

Polyphase filter banks (PFBs) are widely used to channelize digitized time-domain data in real time. However, applications such as radio astronomy often require higher spectral resolution than real-time systems can provide. We introduce a circulant inverse-PFB formalism and present a new, scalable, fast Fourier transform (FFT)-based algorithm for inverting critically sampled PFBs. This formalism explicitly identifies the origin of quantization noise amplification and enables two complementary mitigation strategies: (1) a fast, practical Wiener filter and (2) a rigorous maximum-likelihood reconstruction with time-domain priors. We evaluate both approaches using simulated 4-bit quantized PFB data. For autospectra, Wiener filtering confines reconstruction errors to less than 10\% of the channel width, with a peak error below 10\%. The maximum-likelihood approach further reduces reconstruction errors as additional time-domain prior information is incorporated, with priors spanning 10\% of the time-domain samples reducing worst-case errors to below 2\%. This framework enables high-resolution spectral analysis of archival PFB data, facilitating applications including long-baseline interferometry, pulsar and fast radio burst searches, and ultra-narrow-band physics experiments.