论文标题
基于CEEMD-ES雷达选择的人类行为识别方法
Human Behavior Recognition Method Based on CEEMD-ES Radar Selection
论文作者
论文摘要
近年来，识别人类行为的毫米波雷达已被广泛用于医疗，安全和其他领域。当多个雷达执行检测任务时，很难保证每个雷达中包含的特征的有效性。此外，处理多个雷达数据还需要大量时间和计算成本。提出了互补的集合经验模式分解 - 能量切片（CEEMD-ES）多静脉雷达选择方法来解决这些问题。首先，该方法根据四肢和人体躯干之间反射频率的差异分解和重建雷达信号。然后，根据四肢和躯干的回声能量与理论值之间的差异选择雷达。提取了所选雷达的时域，频域和各种熵特征。最后，建立了Relu Core的极端学习机（ELM）识别模型。实验表明，该方法可以有效地选择雷达，而三种人类作用的识别率为98.53％。
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%.
