Feature optimization based on multi-order fusion and adaptive recursive elimination for motion classification in doppler radar

Radar-based human motion recognition (HMR) technology has gained substantial importance across diverse domains such as security surveillance, post-disaster search and rescue operations, and the development of smart home environments. The intricate nature of human movements generates radar echo signals with pronounced non-stationary attributes, which encapsulate a wealth of target feature data. However, striking a balance between the precision of motion recognition and the requirement for real-time processing, especially in the context of extracting meaningful features from radar signals, remains a formidable challenge. This research paper introduces a novel approach to tackle this challenge. Firstly,we apply the multi-order fractional Fourier transform (m-FRFT) to radar echo signals, facilitating the extraction of micro-Doppler (m-D) frequency information. Secondly, we have developed an optimized feature selection model named MPG, which stands for m-D parameter screening based on genetic algorithm (GA) and adaptive weight particle swarm optimization (AWPSO). Thirdly, we apply the MPG model to the recursive feature elimination (RFE) algorithm to refine the representation of m-D frequency information, allowing for adaptive parameter adjustment and effective feature dimensionality reduction. The proposed method has been tested using human motion echo data collected from a Doppler radar prototype. The experimental outcomes demonstrate that our approach outperforms traditional feature extraction methods in terms of reducing feature dimensionality, computational efficiency, and classification accuracy.