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Although automatic and robust cluster identiflcation is crucial for ultra-wideband propagation modeling, the existing schemes may either require interactions with analyst, or fail to produce reasonable clustering results in more universal propagation environments. In this article, we suggest a novel cluster identiflcation algorithm. Rather than assuming the limited exponential power decay characteristics on UWB channels, from a novel perspective cluster identiflcation is formulated as the local discontinuity detection based on wavelet analysis. Firstly, in order to comprehensively re∞ect the prevailing amplitude changes induced by new clusters, the moving averaging ratio is extracted from the measured UWB channel impulse responses. Subsequently, the appealing local-transient analysis ability of wavelet transform is properly exploited, and a computationally e-cient cluster extraction method is developed. Distinguished from the subjective visual inspection and excluding any analyst interaction, the presented scheme can automatically discover multiple clusters. Our algorithm is premised on the general amplitude discontinuity and hence is applicable to various complicated operation environments. Moreover, the produced clustering results, essentially depicting realistic physical propagations, are basically independent of parameter conflgurations. Experiments on both simulated channels and the measured data in typical vehicle cabin validate the proposed method.

EFFICIENT CLUSTER IDENTIFICATION FOR MEASURED ULTRA-WIDEBAND CHANNEL IMPULSE RESPONSE IN VEHICLE CABIN