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In this paper, we study the clustered propagation characteristics of ultrawideband sensors in vehicle cabin. Inspired by the amplitude discontinuity of channel impulse responses, we first develop an automatic cluster identification algorithm. A moving averaging ratio is extracted from measured responses, which thoroughly reflects prevailing amplitude ruptures induced by different clusters. Based on a novel wavelet scales multiplication, an efficient cluster extraction scheme is presented. Our scheme can automatically discover multiple clusters, which shows great promise in realistic measurement analysis. Subsequently, from a ray-optical and statistical point of view, a parametric model of inter-cluster shape is suggested, which interprets the observed inter-cluster power as a kind of large-scale frequency selectivity. Parameter fitting to real data validates this power delay profile (PDP) model. Finally, we apply the identified clusters and fitted PDP to the practical design of low-complexity ultra-wideband sensors, which is of particular interest to the emerging wireless sensor networks. By utilizing the cluster PDP as a roughly matched template for a received signal, detection performance can be noticeably reinforced. This method simultaneously provides a practical criterion to evaluate the clustered propagation modeling, and the remarkable receiving gain verifies the effectiveness of our suggested cluster identification scheme and PDP model.