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EnglishWideband 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.
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| Titolo: | Wideband Localization via Range Likelihood based on Reduced Dataset | |
| Autori: | ||
| Data di pubblicazione: | 2015 | |
| Abstract: | 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. | |
| Handle: | http://hdl.handle.net/11392/2339369 | |
| ISBN: | 9781479965601 9781479965601 | |
| Appare nelle tipologie: | 04.2 Contributi in atti di convegno (in Volume) |
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http://hdl.handle.net/11392/2339369
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