Wideband localization via range likelihood based on reduced dataset

Wideband localization commonly relies on accurate range information extracted from received waveforms, which can be obtained via hard-decision or soft-decision ranging. While hard-decision ranging based on energy samples has received much attention because of its low-complexity, soft-decision ranging based on waveform samples can significantly improve the localization accuracy at the cost of higher complexity. This paper proposes new soft-decision ranging techniques with low complexity for wideband localization. The proposed techniques adopt range likelihood functions obtained from a reduced dataset of the received waveform samples. Results show that the proposed soft-decision techniques enable localization with higher accuracy compared to hard-decision ranging.