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

Widener University, Chester PA

Investigators

Abstract

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.