基于数据增强算法和CNN-LSTM的高精确度手势识别

近年来，基于雷达的手势识别技术在工业和生活中得到了广泛应用，但愈加复杂的应用场景对手势识别算法的准确率和鲁棒性提出了更高要求。对此，设计了一种基于毫米波雷达的高精确度手势识别算法。通过对已有分类算法的研究对比，构建了一种用于手势识别的卷积神经网络-长短期记忆网络(CNN-LSTM)深度学习算法模型；同时，运用布莱克曼窗抑制手势信号处理中的频谱泄露问题，并联合运用小波阈值和动态补零算法实现高效杂波抑制和数据增强。实测结果表明，设计的手势识别算法正确分类率达到97.29%，在不同的距离和角度情况下也可以保持较好的识别准确率，具有良好的鲁棒性。

High-precision gesture recognition based on data enhancement algorithm and CNN-LSTM

In recent years, radar-based gesture recognition technology has been widely used in industry and life, and more complex application scenarios also put forward higher requirements on the accuracy and robustness of gesture recognition algorithms. A high-precision gesture recognition algorithm based on millimeter-wave radar is desgined. By comparing the existing classification algorithms, a Convolutional Neural Network-Long Short Term Memory(CNN-LSTM) deep learning algorithm model is constructed for gesture recognition. At the same time, the Blackman window is employed to suppress the problem of spectrum leakage in gesture signal processing, and efficient clutter suppression and data enhancement is achieved through the combining of wavelet threshold and dynamic zero-padding algorithm. The actual measurement results show that the proposed gesture recognition algorithm achieves a correct classification rate of 97.29%, and can maintain a good recognition accuracy rate under different distances and angles with very good robustness.