The integrated sensing and communication (ISAC) technology, which performs vehicle state sensing and communications simultaneously, has been widely regarded as a key to enable future intelligent vehicular networks. However, the communications information is in general vehicle-specific, which requires the RSU to distinguish vehicle identities (IDs). Conventionally, the vehicles feed back their IDs to the RSU, since they are not contained in the sensing echoes. This letter develops a novel approach for multi-vehicle tracking and ID association using the ISAC signals. In particular, the ID association is done by comparing the similarity of the distributions of estimated and predicted locations based on the Kullback-Leibler divergence (KLD). With the aid of the proposed approach, frequent uplink ID feedback is avoided, leading to a reduced communication overhead as well as latency. We further design a high-efficiency predictive beamforming scheme, which predicts the angular parameters in the following time instant relying on the estimated states. Simulation results show that our proposed scheme can effectively associate different vehicle IDs and improve the communications performance.

中文翻译：

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基于集成传感和通信信令的多车跟踪与身份关联

同时进行车辆状态感知和通信的集成传感和通信（ISAC）技术被广泛认为是实现未来智能车辆网络的关键。然而，通信信息通常是特定于车辆的，这需要 RSU 区分车辆身份 (ID)。传统上，车辆将其 ID 反馈给 RSU，因为它们不包含在感应回波中。这封信开发了一种使用 ISAC 信号进行多车辆跟踪和 ID 关联的新方法。具体来说，ID 关联是通过比较基于 Kullback-Leibler 散度 (KLD) 的估计位置和预测位置的分布的相似性来完成的。借助所提出的方法，避免了频繁的上行链路 ID 反馈，从而减少通信开销和延迟。我们进一步设计了一种高效的预测波束成形方案，该方案根据估计的状态预测下一时刻的角度参数。仿真结果表明，我们提出的方案可以有效地关联不同的车辆 ID，提高通信性能。