Angular Sector-Based Sparse Array Design for Adaptive Beamforming Using Deep Learning

Efficient sparse array reconfigurability is essential for cognitive sensing in dynamic radio frequency environments, where rapid interference variations require both adaptability and stability. This work presents a framework for designing sparse arrays optimized over broad angular sectors, enabling near-optimal beamforming that maximizes the signal-to-interference-plus-noise ratio (SINR) across a range of interferer angles. Full data correlation matrices are computed for candidate configurations, and an angular-sector-based class reduction strategy is applied to merge adjacent sectors dominated by the same configuration, resulting in 56 representative classes. Controlled up- and down-sampling produce four dataset variants involving, high and low sample count, balanced and unbalanced datasets, to systematically evaluate the effects of dataset size and class distribution on neural network performance. A lightweight convolutional neural network (CNN) and a deeper ResNet 50 architecture are trained and evaluated using these datasets. Results demonstrate high classification accuracy, with ResNet 50 achieving up to 97.3%, while SINR deviations remain below 1% for most classes and below 5% even for challenging interference angles near broadside. The proposed approach enables robust sparse array selection, maintains strong SINR performance, reduces unnecessary reconfigurations, and provides an effective framework for real-time cognitive sensing and adaptive interference mitigation.

Continuous Human Activity Recognition using a MIMO Radar for Transitional Motion Analysis

The prompt and accurate recognition of Continuous Human Activity (CHAR) is critical in identifying and responding to health events, particularly fall risk assessment. In this paper, we examine a multi-antenna radar system that can process radar data returns for multiple individuals in an indoor setting, enabling CHAR for multiple subjects. This requires combining spatial and temporal signal processing techniques through micro-Doppler (MD) analysis and high-resolution receive beamforming. We employ delay and sum beamforming to capture MD signatures at three different directions of observation. As MD images may contain multiple activities, we segment the three MD signatures using an STA/LTA algorithm. MD segmentation ensures that each MD segment represents a single human motion activity. Finally, the segmented MD image is resized and processed through a convolutional neural network (CNN) to classify motion against each MD segment.