This paper studies multi-active intelligent-reflecting-surface (IRS) cooperative sensing, in which multiple active IRSs are deployed in a distributed manner to help the base station (BS) provide multi-view sensing. We focus on the scenario where the sensing target is located in the non-line-of-sight (NLoS) area of the BS. Based on the received echo signal, the BS aims to estimate the target's direction-of-arrival (DoA) with respect to each IRS. In addition, we leverage active IRSs to overcome the severe path loss induced by multi-hop reflections. Under this setup, we minimize the maximum Cram\'{e}r-Rao bound (CRB) among all IRSs by jointly optimizing the transmit beamforming at the BS and the reflective beamforming at the multiple IRSs, subject to the constraints on the maximum transmit power at the BS, as well as the maximum transmit power and the maximum power amplification gain at individual IRSs. To tackle the resulting highly non-convex max-CRB minimization problem, we propose an efficient algorithm based on alternating optimization, successive convex approximation, and semi-definite relaxation, to obtain a high-quality solution. Finally, numerical results are provided to verify the effectiveness of our proposed design and the benefits of active IRS-assisted sensing compared to the counterpart with passive IRSs.

翻译：本文研究了多主动智能反射面(IRS)协作感知问题，其中多个主动IRS以分布式方式部署，以辅助基站(BS)实现多视角感知。我们重点考虑感知目标位于基站非视距(NLoS)区域的情形。基于接收到的回波信号，基站旨在估计目标相对于每个IRS的到达角(DoA)。此外，我们利用主动IRS来克服多跳反射导致的严重路径损耗。在此框架下，我们在基站最大发射功率、各IRS最大发射功率以及最大功率放大增益的约束下，通过联合优化基站的发射波束赋形与多个IRS的反射波束赋形，最小化所有IRS中的最大克拉美罗界(CRB)。针对由此产生的极度非凸的最大CRB最小化问题，我们提出了一种基于交替优化、逐次凸逼近和半正定松弛的高效算法以获得高质量解。最后，通过数值结果验证了所提设计方案的有效性，以及与被动IRS相比，主动IRS辅助感知的优势。