In the evolving landscape of high-speed communication, the shift from traditional pilot-based methods to a Sensing-Oriented Approach (SOA) is anticipated to gain momentum. This paper delves into the development of an innovative Integrated Sensing and Communication (ISAC) framework, specifically tailored for beamforming and trajectory prediction processes. Central to this research is the exploration of an Unmanned Aerial Vehicle (UAV)-enabled communication system, which seamlessly integrates ISAC technology. This integration underscores the synergistic interplay between sensing and communication capabilities. The proposed system initially deploys omnidirectional beams for the sensing-focused phase, subsequently transitioning to directional beams for precise object tracking. This process incorporates an Extended Kalman Filtering (EKF) methodology for the accurate estimation and prediction of object states. A novel frame structure is introduced, employing historical sensing data to optimize beamforming in real-time for subsequent time slots, a strategy we refer to as 'temporal-assisted' beamforming. To refine the temporal-assisted beamforming technique, we employ Successive Convex Approximation (SCA) in tandem with Iterative Rank Minimization (IRM), yielding high-quality suboptimal solutions. Comparative analysis with conventional pilot-based systems reveals that our approach yields a substantial improvement of 156\% in multi-object scenarios and 136\% in single-object scenarios.

翻译：在高速通信不断演进的背景下，从传统的基于导频的方法向感知导向方法（SOA）的转变预计将获得发展势头。本文深入探讨了一种创新的集成感知与通信（ISAC）框架的开发，该框架专为波束成形和轨迹预测过程定制。本研究的核心在于探索一种无人机（UAV）使能的通信系统，该系统无缝集成了ISAC技术。这种集成强调了感知与通信能力之间的协同作用。所提出的系统最初在面向感知的阶段部署全向波束，随后转换为定向波束以实现精确的目标跟踪。此过程结合了扩展卡尔曼滤波（EKF）方法，用于准确估计和预测目标状态。本文引入了一种新颖的帧结构，利用历史感知数据为后续时隙实时优化波束成形，这一策略我们称之为"时间辅助"波束成形。为了优化时间辅助波束成形技术，我们采用逐次凸逼近（SCA）与迭代秩最小化（IRM）相结合的方法，从而产生高质量的次优解。与传统的基于导频的系统进行比较分析表明，我们的方法在多目标场景中带来了156%的显著提升，在单目标场景中提升了136%。