Learning to Count Targets from Dual-Window: A CNN Approach for OFDM ISAC

TL;DR

This paper introduces a CNN-based method for OFDM ISAC target counting that learns to fuse dual-windowed range-Doppler maps, improving accuracy in dense scenes and under noise.

Contribution

It proposes a novel CNN approach that learns to combine resolution-optimized and sidelobe-suppressed windowed maps, addressing the resolution-sidelobe trade-off in target counting.

Findings

Improved accuracy over single-window CNN baselines.

Better scalability with target density.

Enhanced robustness across noise levels.

Abstract

Integrated Sensing and Communication (ISAC) with Orthogonal Frequency Division Multiplexing (OFDM) waveforms is a key enabler for next-generation wireless systems. Recent studies show that Convolutional Neural Networks (CNNs) can estimate the number of targets from two-dimensional (2D) range-Doppler periodogram maps, yet accuracy often degrades as scenes become denser. One significant factor is the classical resolution-sidelobe attenuation trade-off, which limits performance when targets are weak or closely spaced. While windowing is routinely applied to shape this trade-off, the choice is typically static. This paper proposes a new CNN method that uses two windowed range-Doppler periodograms and learns to fuse complementary views: one window optimized for resolution and one window optimized for sidelobe suppression. The design explicitly targets the resolution-sidelobe attenuation trade-off by exposing the model to complementary windowed maps and letting it learn when each is most informative. Numerical experiments show consistent gains over single-window CNN baselines, with better scaling in target density and greater robustness across different noise levels.