Learning to Count Targets from Dual-Window: A CNN Approach for OFDM ISAC
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915642418921472 |
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| author | Khansa, Ali Al |
| author_facet | Khansa, Ali Al |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22473 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Count Targets from Dual-Window: A CNN Approach for OFDM ISAC Khansa, Ali Al Signal Processing 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. |
| title | Learning to Count Targets from Dual-Window: A CNN Approach for OFDM ISAC |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2511.22473 |