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.

3D认知传感，通过可重构稀疏阵列（RSA）促进，通过有效地最大程度地减少来自各种源方向的干扰，同时保持相同数量的昂贵无线电频率（RF）前端组件，从而优于固定阵列配置。 RSA通过在通过快速RF开关选择的活动天线位置之间共享昂贵的天线组件来减少它们的降低。使用RSA认知传感模仿了认知过程的感知效果周期（PAC）。 RSA收集来自不同空间天线位置的实时数据，感知周围环境，并动态适应天线位置和随后的阵列处理，称为波束形成。鉴于主动天线位置可以根据所需的源和干扰方向以及其他参数发生变化，因此该工作解决了阵列重新配置特定的三个主要挑战：频繁且复杂的天线切换，计算最佳的活动阵列结构，并在快速PAC中实现光束。克服这些挑战需要快速的迭代优化算法，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执行简化的天线切换标准。探索了针对两个关键应用的RSA设计：认知无线电（CR）中的光谱传感（SS）以及认知雷达传感中的定位和跟踪。由SS功能启用，CR检测未充分利用的频带，以供机会使用，并自主对RF信号进行了分类。将RSA设计集成在CR中可以增强干扰缓解，提高服务质量，带宽可用性和频谱利用率。这些优势通过提高情境意识，监视能力，频谱管理和共存措施来使监管机构和政府实体受益。尽管CR通常依赖于被动或仅接收感测，但启用RSA的认知雷达系统通过在发射器和接收器处优化天线位置，尽管通过不同的方法来增强性能。这一进步将推动最先进的天气监测，军事雷达，用于自动驾驶汽车的雷达，室内人类活动分类，跌落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技术在内的快速迭代算法，以迅速和智能地抑制AntennAs的动态抑制Antennas的动态抑制。当前的算法仅在严重有限的方案下才有效：假定的操作环境的先验知识通常是未知的，而实时重新配置性是一个巨大的挑战，这是由于优化阵列拓扑的高运行时间。拟议的研究旨在通过两个新颖的想法来改变当前的SS和信号调节分类范例：（i）采用CR中的多频段RSA作为一种机制，以增强各种频段跨频段的源ID分类，同时考虑现实的频道损害，以及（II）通过包括机器学习和CONVEX在内的多型频段感知的RSA的优化。 DL模型的设计有望在统一框架中处理多个感兴趣的频带。该建议将与数据相关的技术和先前的环境知识集成到DL模型中，从而使新的体系结构具有更高的精度，从而通过克服特定于数据依赖性实现的瓶颈来提高自适应感测。拟议的研究将通过（i）有效求解高分辨率的RSA多输入/多输出/多输出（MIMO）雷达公式来提高雷达传感的性能，以生成异常准确的接收光束板，这些光束在未知的环境和混乱环境中都可以固定，并通过求解型号，以实现良好的训练DL模型，以实现型号，以实现型号，以实现Trans Models，以实现Transine，并实现transine，并实现Trans Models，并实现Transine的最终工程，并实现最初的设备。传输波形以最大化目标位置的功率。由于雷达传感能力由于各种环境因素（例如噪声，混乱和干扰信号）而受到限制，因此该提案提供了一种有希望的解决方案，因为它涉及将RSA集成到MIMO雷达跟踪中，以增强干扰拒绝能力。该奖项反映了NSF的法定任务，并通过评估了CR CRCRITAIL IFFICTIAL IFFICTIAL和FRODICAIL的支持，并通过评估了基金会的范围。