ERI: AI-Enhanced Dynamic Interference Suppression in Cognitive Sensing with Reconfigurable Sparse Arrays

ERI：利用可重构稀疏阵列在认知传感中进行人工智能增强型动态干扰抑制

• 批准号: 2347220
• 负责人: Syed Ali Hamza
• 金额: $ 19.99万
• 依托单位: Widener University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2347220&HistoricalAwards=false
• 关键词: ERI; AI; Enhanced; Dynamic; Interference

3D cognitive sensing, facilitated by a reconfigurable sparse array (RSA), outperforms fixed array configurations by effectively minimizing interference from various source directions while maintaining the same number of expensive radio frequency (RF) front-end components. The RSA achieves this reduction in costly antenna components by sharing them between active antenna locations selected through fast RF switching. Cognitive sensing using RSA emulates the perception-action cycle (PAC) of cognitive processes. RSA gathers real-time data from different spatial antenna locations, perceives surroundings, and dynamically adapts antenna locations and subsequent array processing, known as beamforming. Given that active antenna locations can change depending on desired source and interference directions and other parameters, this work addresses three main challenges specific to array reconfigurability: frequent and complex antenna switching, computing optimum active array structures, and implementing beamforming within the fast PAC. Overcoming these challenges requires fast iterative optimization algorithms, artificial intelligence (AI) techniques such as offline deep learning (DL)-based neural network training, and enforcement of simplified antenna switching criteria. The RSA design is explored for two key applications: spectral sensing (SS) in cognitive radio (CR) and localization and tracking in cognitive radar sensing. Enabled by SS functionality, a CR detects underutilized frequency bands for opportunistic use and autonomously classifies RF signals. Integrating RSA design within CR enhances interference mitigation, improving service quality, bandwidth availability, and spectrum utilization. These advantages benefit regulatory authorities and government entities by enhancing situational awareness, surveillance capabilities, spectrum management, and coexistence measures. While CR typically relies on passive or receive-only sensing, an RSA-enabled cognitive radar system enhances performance by optimizing antenna locations at both the transmitter and receiver, albeit through distinct approaches. This advancement would propel state-of-the-art weather monitoring, military radar, radar for self-driving cars, indoor human activity classification, fall detection, and remote vital sign estimation.The proposed research is predicated on developing fast iterative algorithms, including DL techniques, to enable dynamic interference suppression by swiftly and intelligently selecting subsets of antennas from a uniform grid of antenna locations. Current algorithms are effective only under severely limited scenarios: the assumed prior knowledge of the operating environment is often unknown, and real-time reconfigurability is a considerable challenge due to the high run times of optimizing the array topology. The proposed research aims to transform the current paradigms for SS and signal modulation classification via two novel ideas: (i) the adoption of multi-band RSA in CR as a mechanism to enhance source ID classification across a range of frequency bands while considering realistic channel impairments, and (ii) the optimization of RSA for multi-band sensing by including advances in machine learning and convex optimization. The design of DL models is expected to process multiple frequency bands of interest in a unifying framework. The proposal integrates data-dependent techniques and prior environment knowledge into DL models, resulting in novel architectures with greater accuracy that will advance adaptive sensing by overcoming bottlenecks specific to data-dependent implementation. The proposed research would advance the performance of radar sensing by (i) efficiently solving high-resolution RSA multi-input/multi-output (MIMO) radar formulation for generating exceptionally accurate receive beampatterns that are robust to unknown jamming and clutter environments, and (ii) training DL models through solving complex optimization problems, to realize an end-to-end transmitter design to predict transmit antenna locations, as well as transmit waveforms for maximizing the power towards target locations. Since radar sensing capability is limited due to various environmental factors such as noise, clutter, and jamming signals, this proposal offers a promising solution as it involves integrating RSA into MIMO radar tracking to bolster interference rejection capabilities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由可重构稀疏阵列 (RSA) 推动的 3D 认知感测可有效最小化来自各个源方向的干扰，同时保持相同数量的昂贵射频 (RF) 前端组件，从而优于固定阵列配置。 RSA 通过在通过快速射频切换选择的有源天线位置之间共享天线组件，实现了昂贵天线组件的减少。使用 RSA 的认知感知模拟认知过程的感知-行动周期 (PAC)。 RSA 从不同的空间天线位置收集实时数据，感知周围环境，并动态调整天线位置和后续阵列处理（称为波束成形）。鉴于有源天线位置可能会根据所需的源和干扰方向以及其他参数而变化，这项工作解决了阵列可重构性特有的三个主要挑战：频繁且复杂的天线切换、计算最佳有源阵列结构以及在快速 PAC 中实现波束成形。克服这些挑战需要快速迭代优化算法、人工智能 (AI) 技术（例如基于离线深度学习 (DL) 的神经网络训练）以及简化天线切换标准的执行。 RSA 设计针对两个关键应用进行了探索：认知无线电 (CR) 中的频谱传感 (SS) 以及认知雷达传感中的定位和跟踪。在 SS 功能的支持下，CR 可以检测未充分利用的频段以供机会使用，并自动对 RF 信号进行分类。将 RSA 设计集成到 CR 中可以增强干扰抑制，提高服务质量、带宽可用性和频谱利用率。这些优势通过增强态势感知、监视能力、频谱管理和共存措施，使监管机构和政府实体受益。虽然 CR 通常依赖于无源或仅接收传感，但支持 RSA 的认知雷达系统通过优化发射器和接收器的天线位置来增强性能，尽管方法不同。这一进步将推动最先进的天气监测、军用雷达、自动驾驶汽车雷达、室内人类活动分类、跌倒检测和远程生命体征估计。拟议的研究基于开发快速迭代算法，包括DL 技术，通过从统一的天线位置网格中快速、智能地选择天线子集来实现动态干扰抑制。当前的算法仅在严格限制的情况下有效：假设的操作环境的先验知识通常是未知的，并且由于优化阵列拓扑的运行时间较长，实时可重构性是一个相当大的挑战。拟议的研究旨在通过两个新颖的想法来改变当前的 SS 和信号调制分类范式：(i) 在 CR 中采用多频段 RSA 作为一种机制，在考虑实际信道的同时，在一系列频段上增强源 ID 分类(ii) 通过机器学习和凸优化的进步来优化 RSA 的多频段传感。深度学习模型的设计预计能够在统一框架中处理多个感兴趣的频段。该提案将数据相关技术和先验环境知识集成到深度学习模型中，从而产生具有更高准确度的新颖架构，通过克服数据相关实施特有的瓶颈来推进自适应传感。拟议的研究将通过以下方式提高雷达传感的性能：（i）有效解决高分辨率 RSA 多输入/多输出（MIMO）雷达公式，以生成对未知干扰和杂波环境具有鲁棒性的异常精确的接收波束方向图，以及（ ii) 通过解决复杂的优化问题来训练深度学习模型，以实现端到端发射机设计来预测发射天线位置以及发射波形以最大化目标位置的功率。由于雷达传感能力因噪声、杂波和干扰信号等各种环境因素而受到限制，因此该提案提供了一个有前途的解决方案，因为它涉及将 RSA 集成到 MIMO 雷达跟踪中，以增强抗干扰能力。该奖项反映了 NSF 的法定使命，并具有通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。