Yanmo Hu

Junyuan Gao, Weifeng Zhu, Yanmo Hu et al.

This paper considers multi-view imaging in a sixth-generation (6G) integrated sensing and communication network, which consists of a transmit base-station (BS), multiple receive BSs connected to a central processing unit (CPU), and multiple extended targets. Our goal is to devise an effective multi-view imaging technique that can jointly leverage the targets' echo signals at all the receive BSs to precisely construct the image of these targets. To achieve this goal, we propose a two-phase approach. In Phase I, each receive BS recovers an individual image based on the sample covariance matrix of its received signals. Specifically, we propose a novel covariance-based imaging framework to jointly estimate effective scattering intensity and grid positions, which reduces the number of estimated parameters leveraging channel statistical properties and allows grid adjustment to conform to target geometry. In Phase II, the CPU fuses the individual images of all the receivers to construct a high-quality image of all the targets. Specifically, we design edge-preserving natural neighbor interpolation (EP-NNI) to map individual heterogeneous images onto common and finer grids, and then propose a joint optimization framework to estimate fused scattering intensity and BS fields of view. Extensive numerical results show that the proposed scheme significantly enhances imaging performance, facilitating high-quality environment reconstruction for future 6G networks.

Yanmo Hu, Shuowen Zhang, Ross Murch et al.

Imaging is a crucial sensing function that finds wide applications in environmental reconstruction, autonomous driving, etc. However, the signal processing methods for existing radio imaging techniques, such as millimeter wave (mmWave) imaging, require high-resolution range estimation enabled by Gigahertz-level or even Terahertz-level bandwidth, and cannot be applied in 6G integrated sensing and communication (ISAC) network with Megahertz-level bandwidth. This paper proposes two novel high-resolution radio imaging schemes that can work on the 6G signals with limited bandwidth - bandwidth-independent synthetic aperture radar (BI-SAR), where the movable base station (BS) revolves along the static targets by 360 degrees; as well as bandwidth-independent inverse synthetic aperture radar (BI-ISAR), where the BS is static and the targets revolve along an axis by 360 degrees. Different from conventional SAR and ISAR counterparts that rely on range estimation, our proposed imaging schemes solely utilize Doppler information to perform imaging without any range information. The main technical challenge of our schemes lies in the anisotropic scattering functions over different directions, which hinder the coherent synthesis of the backscattered signals from all directions. We design an iterative adaptive approach-based Doppler association (IAA-DA) algorithm to tackle the above issue. Moreover, we also derive the imaging resolution to characterize the reconstruction quality. Real-world experiments are provided to show the feasibility and the effectiveness of our proposed 6G imaging schemes.

Yanmo Hu, Weifeng Zhu, Chenshu Wu et al.

This paper considers a networked tracking architecture in 6G integrated sensing and communication (ISAC) systems, where multiple base stations (BSs) cooperatively transmit radio signals and process received echo signals to track multiple moving targets. Compared to the single-BS counterpart, networked tracking allows the moving targets to be associated with different BSs over time such that the wireless resources can be dynamically allocated among BSs based on target locations. However, networked tracking imposes new challenges for algorithm design and resource allocation. In this paper, we first design the networked Kalman Filter (NKF) that is suitable for multi-BS based tracking, then characterize the posterior Cramer-Rao bound (PCRB) under this NKF, and last design the beamforming vectors of all the BSs to minimize the tracking PCRB. Numerical results show that our dynamic beamforming design can properly associate the targets to the suitable BSs at various sensing blocks and reduce the tracking mean-squared error (MSE).