Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model

TL;DR

This paper introduces a deep spectrogram network for human motion recognition using wireless sensing and a novel hybrid channel model for efficient data generation, resulting in improved accuracy over traditional CNN methods.

Contribution

It proposes a deep spectrogram network with residual mapping and a primitive based autoregressive hybrid channel model for enhanced wireless sensing performance.

Findings

PBAH channel model closely matches real data.

DSN outperforms CNN in recognition accuracy.

Efficient virtual dataset generation is feasible.

Abstract

Human motion recognition (HMR) based on wireless sensing is a low-cost technique for scene understanding. Current HMR systems adopt support vector machines (SVMs) and convolutional neural networks (CNNs) to classify radar signals. However, whether a deeper learning model could improve the system performance is currently not known. On the other hand, training a machine learning model requires a large dataset, but data gathering from experiment is cost-expensive and time-consuming. Although wireless channel models can be adopted for dataset generation, current channel models are mostly designed for communication rather than sensing. To address the above problems, this paper proposes a deep spectrogram network (DSN) by leveraging the residual mapping technique to enhance the HMR performance. Furthermore, a primitive based autoregressive hybrid (PBAH) channel model is developed, which facilitates efficient training and testing dataset generation for HMR in a virtual environment. Experimental results demonstrate that the proposed PBAH channel model matches the actual experimental data very well and the proposed DSN achieves significantly smaller recognition error than that of CNN.