Научный семинар «A Low Complexity, Accurate Adaptive Threshold Predictor for Energy Detection-Based UWB Ranging»
Направление
Наука
Организатор
Институт компьютерных наук и телекоммуникаций
Формат мероприятия
Семинар
Контактное лицо
Мутханна Аммар Салех Али
О мероприятии
Nouali Ibrahim Yassine, PhD, a researcher at the Laboratory of Information and Communication Systems and Technologies (STIC) at Abou Bekr Belkaïd University of Tlemcen, Algeria.
Energy detection (ED)-based receivers are widely adopted in ultra-wideband (UWB) ranging systems due to their simplicity and ease of implementation. Thresholding-based methods are commonly used in such receivers for first path (FP) detection and time-of-arrival (TOA) estimation, but accuracy depends on how well the threshold adapts to noise and multipath effects. This paper proposes the peak-to-average ratio (PAR) of the energy samples as an adaptive threshold predictor, modelling its relationship with the optimal threshold using a sigmoid function fitted by nonlinear least squares. Monte Carlo simulations using the CM1 to CM4 IEEE 802.15.4a channel models show up to 59% lower mean absolute error (MAE) than the Kurtosis-based approach. A new ED resolution utilization (ED-RU) metric further shows that PAR recovers between 24% and 45% of the theoretical resolution floor, compared with 6% to 20% for Kurtosis. Moreover, a single PAR model pooled across all four channels retains most of this accuracy gain without per-channel calibration, while also being computationally efficient: PAR is 14 times faster than Kurtosis because it requires only a maximum and a mean over the energy samples. These findings establish PAR as a simpler, more accurate, and better generalized adaptive threshold predictor for ED-based UWB ranging.
Ссылка на подключение: https://telemost.yandex.ru/j/77800784482961
Energy detection (ED)-based receivers are widely adopted in ultra-wideband (UWB) ranging systems due to their simplicity and ease of implementation. Thresholding-based methods are commonly used in such receivers for first path (FP) detection and time-of-arrival (TOA) estimation, but accuracy depends on how well the threshold adapts to noise and multipath effects. This paper proposes the peak-to-average ratio (PAR) of the energy samples as an adaptive threshold predictor, modelling its relationship with the optimal threshold using a sigmoid function fitted by nonlinear least squares. Monte Carlo simulations using the CM1 to CM4 IEEE 802.15.4a channel models show up to 59% lower mean absolute error (MAE) than the Kurtosis-based approach. A new ED resolution utilization (ED-RU) metric further shows that PAR recovers between 24% and 45% of the theoretical resolution floor, compared with 6% to 20% for Kurtosis. Moreover, a single PAR model pooled across all four channels retains most of this accuracy gain without per-channel calibration, while also being computationally efficient: PAR is 14 times faster than Kurtosis because it requires only a maximum and a mean over the energy samples. These findings establish PAR as a simpler, more accurate, and better generalized adaptive threshold predictor for ED-based UWB ranging.
Ссылка на подключение: https://telemost.yandex.ru/j/77800784482961