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As indoor applications grow in diversity, wireless sensing, vital in areaslike localization and activity recognition, is attracting renewed interest.Indoor wireless sensing relies on signal processing, particularly channel stateinformation (CSI) based signal parameter estimation. Nonetheless, regardingreflected signals induced by dynamic human targets, no satisfactory algorithmyet exists for estimating the acceleration of dynamic path length change(DPLC), which is crucial for various sensing tasks in this context. Hence, thispaper proposes DP-AcE, a CSI-based DPLC acceleration estimation algorithm. Wefirst model the relationship between the phase difference of adjacent CSImeasurements and the DPLC's acceleration. Unlike existing works assumingconstant velocity, DP-AcE considers both velocity and acceleration, yielding amore accurate and objective representation. Using this relationship, analgorithm combining scaling with Fourier transform is proposed to realizeacceleration estimation. We evaluate DP-AcE via the acceleration estimation andacceleration-based fall detection with the collected CSI. Experimental resultsreveal that, using distance as the metric, DP-AcE achieves a medianacceleration estimation percentage error of 4.38%. Furthermore, in multi-targetscenarios, the fall detection achieves an average true positive rate of 89.56%and a false positive rate of 11.78%, demonstrating its importance in enhancingindoor wireless sensing capabilities.

Acceleration Estimation of Signal Propagation Path Length Changes for Wireless Sensing