Generalization of Channel Micro-Doppler Capacity Evaluation for Improved Finer-Grained Human Activity Classification Using MIMO UWB Radar

In this article, a channel micro-Doppler (
$\mu \text{D}$
) signatures’ quantitative evaluation method for multiple-input–multiple-output (MIMO) radar system, named relative signal–noise ratio in the time–frequency domain and denoted as RSNRtf, is introduced. The impact on RSNR
_tf
by some key dominant factors in real practical and diverse detection environments was theoretically analyzed first. Then, this proposed method is exploited to conduct effective and superior channel selection for improved human activity classification at random human target positions. Based on numerous MIMO stepped-frequency continuous wave (SFCW) radar data from eight subjects with seven activities at nine positions in a through-wall within a view angle of ±20° and up to 6-m distance away, the influence of various primary factors on the proposed method is investigated. Investigative experiments showed that the proposed method has a wide range of applicability and a high interference tolerance even for different detection distances, angles, activities, and subjects. Classification experimental results based on typical classifiers also showed that the proposed method could effectively improve human activity classification. Moreover, we can claim that the proposed method can achieve a higher outstanding performance in the channel
$\mu \text{D}$
capacity evaluation and enhanced classification for the MIMO-radar-based human activity classification in free space or with a wider detection field with large angle range.