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.

翻译：本文提出了一种用于感知信号的分组模式（GP）及相应的多周期图算法，应用于集成感知与通信（ISAC）系统中的距离估计。GP将子载波划分为多个组，每组内部配置相同且跨组重复，从而生成具有周期性峰值和结构化多峰特征的距离剖面，改善了低信噪比下的目标检测性能。通过跨模式峰值验证进行目标识别，所提方法在减少专用感知资源需求的同时，降低了漏检和虚警率。大量仿真结果表明，与传统方法相比，检测距离延长了16.5%，虚警率降低了61%。