An Automated Window Selection Procedure For DFT Based Detection Schemes To Reduce Windowing SNR Loss

TL;DR

This paper introduces an automated method to select optimal window functions for each spectral bin in DFT-based detection, aiming to minimize SNR loss and improve low SNR target detection.

Contribution

It proposes a single snapshot, adaptive window selection procedure that dynamically chooses the best window function per spectral bin based on interference levels.

Findings

Reduces windowing SNR loss significantly.

Improves detection of low SNR targets.

Adapts window choice to interference conditions.

Abstract

The classical spectrum analysis methods utilize window functions to reduce the masking effect of a strong spectral component over weaker components. The main cost of side-lobe reduction is the reduction of signal-to-noise ratio (SNR) level of the output spectrum. We present a single snapshot method which optimizes the selection of most suitable window function among a finite set of candidate windows, say rectangle, Hamming, Blackman windows, for each spectral bin. The main goal is to utilize different window functions at each spectral output depending on the interference level encountered at that spectral bin so as to reduce the SNR loss associated with the windowing operation. Stated differently, the windows with strong interference suppression capabilities are utilized only when a sufficiently powerful interferer is corrupting the spectral bin of interest is present, i.e. only when this window is needed. The achieved reduction in the windowing SNR loss can be important for the detection of low SNR targets.