Grouped Pattern and Multi-Periodogram Algorithm for Range Estimation in ISAC Systems

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.