Human Behavior Recognition Method Based on CEEMD-ES Radar Selection

TL;DR

This paper introduces a novel radar signal processing and selection method using CEEMD-ES to improve human behavior recognition accuracy, achieving over 98% recognition rate with reduced computational effort.

Contribution

The paper proposes a CEEMD-ES based multistatic radar selection method combined with entropy features and ELM classification for enhanced human behavior recognition.

Findings

Recognition rate of 98.53% for three human actions

Effective radar selection reduces computational cost

Method improves recognition accuracy in multi-radar systems

Abstract

In recent years, the millimeter-wave radar to identify human behavior has been widely used in medical,security, and other fields. When multiple radars are performing detection tasks, the validity of the features contained in each radar is difficult to guarantee. In addition, processing multiple radar data also requires a lot of time and computational cost. The Complementary Ensemble Empirical Mode Decomposition-Energy Slice (CEEMD-ES) multistatic radar selection method is proposed to solve these problems. First, this method decomposes and reconstructs the radar signal according to the difference in the reflected echo frequency between the limbs and the trunk of the human body. Then, the radar is selected according to the difference between the ratio of echo energy of limbs and trunk and the theoretical value. The time domain, frequency domain and various entropy features of the selected radar are extracted. Finally, the Extreme Learning Machine (ELM) recognition model of the ReLu core is established. Experiments show that this method can effectively select the radar, and the recognition rate of three kinds of human actions is 98.53%.