Grouped Pattern and Multi-Periodogram Algorithm for Range Estimation in ISAC Systems
Yi Geng, Pan Cao

TL;DR
This paper introduces a grouped pattern and multi-periodogram algorithm for enhanced range estimation in ISAC systems, improving detection range and reducing false alarms through structured signal processing.
Contribution
It presents a novel grouped pattern and multi-periodogram method that enhances target detection accuracy and resource efficiency in ISAC systems.
Findings
Achieved 16.5% extended detection range.
Reduced false alarm rate by 61%.
Improved low-SNR target detection.
Abstract
This paper proposes a grouped pattern (GP) for sensing signals and a corresponding multi-periodogram algorithm for range estimation in integrated sensing and communications (ISAC) systems. GP partitions subcarriers into groups with an identical intra-group configuration replicated across groups, producing range profiles with periodic peaks and a structured multi-peak signature that improves low-SNR target detection. By identifying targets via cross-pattern peak validation, the proposed approach reduces missed detections and false alarms while requiring fewer dedicated sensing resources. Extensive simulations demonstrate a 16.5% extended detection range and a 61% reduced false alarm rate compared to conventional methods.
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