Sparse Micro-Doppler Analysis for Robust Indoor Human Sensing Amidst Moving Clutter Using FMCW Radar

In this paper, we present a model integrating joint sparsity and sinusoidal fitting to address the challenge of static human detection in indoor environments with interference. The motivation behind this model lies in leveraging the frequency domain sparsity induced by the micro-Doppler effect caused by human breathing. The tasks of interference suppression and human detection are formulated as an optimization problem, fitting the signals using sinusoidal functions with sparse coefficients. To effectively address this challenge, we introduce an iterative algorithm based on Improved Sparse Matching Pursuit (ISMP) within the compressed sensing framework, aiming to capture the micro-Doppler information of the target, fit the low-dimensional structure of human respiration in the phase domain, and simultaneously suppress interference micro-motion features. Compared to conventional methods using max average power and standard deviation , our model effectively suppresses interference and enhances target signals in real single-channel radar experiments, even amidst mixed interference with human targets.