Learnable Template Matching Approach for Micro-Deformation Monitoring based on Integrated Sensing and Communication Platform

Existing integrated sensing and communication (ISAC) platforms fail to fully utilize the shared spectrum and aperture resources for sensing, resulting in poor sensing performance. Specifically, weak target sensing on the ISAC platform, such as micro-deformation monitoring (mDM), suffers from inaccurate measurements due to poor sensing quality. In this paper, we propose an AI-assisted approach to alleviate the effect of poor sensing quality in the ISAC system by effectively removing the clutter. We begin by modeling the environment clutter model as a combination of the deterministic and stochastic signals to represent urban coverage scenarios around the base station (BS). A clutter suppression optimization problem is formulated to extract the micro-deformation displacement (mDD) from the original ISAC signals. We then propose a learnable template-matching (LTM) approach to mitigate the influences of clutters, thereby enhancing sensing quality. In particular, the electromagnetic (EM) signal feature of the mDD is embedded into the network to strengthen the mDM signal, and clutter filters are incorporated to suppress environmental clutter. Numerical results illustrate the superiority of our proposed approach concerning convergence speed and accuracy in mDD prediction. By deploying our approach to the BS measurement, the simulation-only trained LTM exhibits impressive performance in environment clutter separation and mDD estimation.

Aerial Vision-Language Navigation with a Unified Framework for Spatial, Temporal and Embodied Reasoning

Aerial Vision-and-Language Navigation (VLN) aims to enable unmanned aerial vehicles (UAVs) to interpret natural language instructions and navigate complex urban environments using onboard visual observation. This task holds promise for real-world applications such as low-altitude inspection, search-and-rescue, and autonomous aerial delivery. Existing methods often rely on panoramic images, depth inputs, or odometry to support spatial reasoning and action planning. These requirements increase system cost and integration complexity, thus hindering practical deployment for lightweight UAVs. We present a unified aerial VLN framework that operates solely on egocentric monocular RGB observations and natural language instructions. The model formulates navigation as a next-token prediction problem, jointly optimizing spatial perception, trajectory reasoning, and action prediction through prompt-guided multi-task learning. Moreover, we propose a keyframe selection strategy to reduce visual redundancy by retaining semantically informative frames, along with an action merging and label reweighting mechanism that mitigates long-tailed supervision imbalance and facilitates stable multi-task co-training. Extensive experiments on the Aerial VLN benchmark validate the effectiveness of our method. Under the challenging monocular RGB-only setting, our model achieves strong results across both seen and unseen environments. It significantly outperforms existing RGB-only baselines and narrows the performance gap with state-of-the-art panoramic RGB-D counterparts. Comprehensive ablation studies further demonstrate the contribution of our task design and architectural choices.

Learning Surface Scattering Parameters From SAR Images Using Differentiable Ray Tracing

Simulating high-resolution Synthetic Aperture Radar (SAR) images in complex scenes has consistently presented a significant research challenge. The development of a microwave-domain surface scattering model and its reversibility are poised to play a pivotal role in enhancing the authenticity of SAR image simulations and facilitating the reconstruction of target parameters. Drawing inspiration from the field of computer graphics, this paper proposes a surface microwave rendering model that comprehensively considers both Specular and Diffuse contributions. The model is analytically represented by the coherent spatially varying bidirectional scattering distribution function (CSVBSDF) based on the Kirchhoff approximation (KA) and the perturbation method (SPM). And SAR imaging is achieved through the synergistic combination of ray tracing and fast mapping projection techniques. Furthermore, a differentiable ray tracing (DRT) engine based on SAR images was constructed for CSVBSDF surface scattering parameter learning. Within this SAR image simulation engine, the use of differentiable reverse ray tracing enables the rapid estimation of parameter gradients from SAR images. The effectiveness of this approach has been validated through simulations and comparisons with real SAR images. By learning the surface scattering parameters, substantial enhancements in SAR image simulation performance under various observation conditions have been demonstrated.