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Ultra-wideband (UWB) devices are widely used in indoor localizationscenarios. Single-anchor UWB localization shows advantages because of itssimple system setup compared to conventional two-way ranging (TWR) andtrilateration localization methods. In this work, we focus on single-anchor UWBlocalization methods that learn statistical features of the channel impulseresponse (CIR) in different location areas using a Gaussian mixture model(GMM). We show that by learning the joint distributions of the amplitudes ofdifferent delay components, we achieve a more accurate location estimatecompared to considering each delay bin independently. Moreover, we develop asimilarity metric between sets of CIRs. With this set-based similarity metric,we can further improve the estimation performance, compared to treating eachsnapshot separately. We showcase the advantages of the proposed methods inmultiple application scenarios.

Single-anchor UWB Localization using Channel Impulse Response Distributions