Papercraft Doppler Radar Measurements Based on Covariance Eigenvalue Spectrum-Assisted Empirical Mode Decomposition

Doppler radar systems encounter challenges due to their high costs, cumbersome designs, and heavy weights, especially in resource-limited environments. As a promising alternative, papercraft Doppler radar has emerged, offering a lightweight, easily deployable and cost-effective solution. However, despite many advantages, papercraft-based radar faces inherent challenges due to the material used, which leads to vulnerability to external stimuli. In this paper, a novel method is proposed demonstrating that papercraft Doppler radar can achieve high performance comparable to its aluminum counterparts and perform multi-target detection even in noisy environment with multiple stimuli. For the first time, we integrate a papercraft Doppler radar with the proposed Covariance Eigenvalue Spectrum (CES)-assisted Empirical Mode Decomposition (EMD) method, significantly improving the performance of the papercraft radar system. Single and multi-target detection, exploiting proper intrinsic mode function (IMF) selection, is achieved through the CES algorithm, which distinguishes between the target and unwanted components via proper windowing and weighting of the decomposed radar signal. According to the results, the proposed method significantly enhances multi-target movement detection and outperforms existing methods.