# CRB-Driven Beamforming and Trajectory Optimization for UAV-assisted ISAC System

arXiv: https://arxiv.org/abs/2607.19609 · HTML: https://mlsanity.com/paper/2607.19609/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 21 Jul 2026.

## Builds on (most cited first, 10 of 10)

- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications](https://mlsanity.com/paper/2405.01816/) (2024)
- [On the Fisher information matrix for multivariate elliptically contoured distributions](https://mlsanity.com/paper/1306.6415/) (2013)
- [Sensing-Assisted Eavesdropper Estimation: An ISAC Breakthrough in Physical Layer Security](https://mlsanity.com/paper/2210.08286/) (2022)
- [Sensing as a Service in 6G Perceptive Mobile Networks: Architecture, Advances, and the Road Ahead](https://mlsanity.com/paper/2308.08185/) (2023)
- [Securing the Sensing Functionality in ISAC Networks: An Artificial Noise Design](https://mlsanity.com/paper/2312.00981/) (2023)
- [Improving Physical-Layer Security in ISAC-UAV System: Beamforming and Trajectory Optimization](https://mlsanity.com/paper/2409.14431/) (2024)
- [Secure Enhancement for RIS-Aided UAV with ISAC: Robust Design and Resource Allocation](https://mlsanity.com/paper/2409.16917/) (2024)
- [Secure Integrated Sensing and Communication Exploiting Target Location Distribution](https://mlsanity.com/paper/2306.04543/) (2023)

## Abstract (arXiv)

In this paper, we study an unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) system, where a UAV enhances the sensing capability of a base station (BS) towards a target while ensuring reliable communication towards a downlink user. This architecture is practically attractive for future wireless networks due to the UAV's controllable mobility and adaptive sensing coverage in wireless environments. The sensing performance is characterized by the average Cram'er-Rao bound (CRB), which quantifies the minimum variance of the unbiased angle-of-arrival estimation. To enhance the sensing performance, the UAV trajectory and beamforming parameters are jointly optimized under power and mobility constraints, while satisfying communication requirements to the downlink user. To address the resulting non-convex problem, we employ null-space projection for beamforming design and adopt deep reinforcement learning for the trajectory optimization over a discrete-time scale. In each time slot, beamforming is optimized based on the channel state information to improve CRB performance while mitigating interference between the BS and the communication user. Simulation results demonstrate that the proposed method significantly reduces the time-averaged CRB by over 10%, compared with the ISAC system without UAV assistance, and also achieves a higher sensing accuracy than both the fixed-UAV-trajectory and the maximum-ratio-transmission-based beamforming benchmarks.