Super-Resolution Doppler Velocity Estimation by Kernel-Based Range–$\tau$ Point Conversions for UWB Short-Range Radars

Abstract

Lower band ultrawideband (UWB) Doppler radar is promising for through-wall imaging, e.g., human body detection in rescue scenarios. The inherent problem with pulse-Doppler radar is the tradeoff between the Doppler velocity resolution and the resulting temporal resolution that makes it difficult to conduct real-time target tracking, because the separation of micro-Doppler velocities of the human body requires a higher Doppler velocity resolution. This problem is particularly severe for lower band UWB radar systems, which are required to attain a sufficient penetration depth in concrete material in the through-the-wall imaging scenario. Because UWB signals generally have large fractional bandwidths, the reflected pulse is located over a range gate along the slow-time direction; this is well known as the range walk problem. As a promising solution to this problem, this article newly introduces a technique for a super-resolution Doppler velocity estimation algorithm based on Gaussian kernel density estimation, which converts observed range– $\tau $ points to Doppler-associated ranges. In addition, this approach makes an important contribution for super-resolution range extraction with a compressed sensing (CS) filter, which is combined with the range-point migration (RPM) method for human body imaging associated with micro-Doppler components. 2-D or 3-D numerical simulations, including human body imaging scenario, demonstrate that the proposed method allows both accurate Doppler velocity estimation and human body imaging, which can be updated at the pulse-repetition interval.