Kurtosis-Based Threshold Selection for UWB TOA Estimation

Kurtosis-Based Threshold Selection Method for UWB TOA Estimation With continuous technological advancements and expanding application domains, UWB TOA estimation has become increasingly critical in wireless communication systems. During UWB TOA estimation, threshold selection serves as a pivotal step that directly impacts estimation accuracy and reliability. This paper proposes a kurtosis-based threshold selection method to enhance UWB TOA estimation performance. By analyzing the kurtosis characteristics of UWB signals—which measure the "tailedness" of signal amplitude distribution—we can determine an optimal threshold for precise time-of-arrival detection. The implementation typically involves calculating the fourth standardized moment of signal samples using mathematical operations like: kurtosis = mean((x - μ)^4) / (mean((x - μ)^2)^2) - 3 where x represents signal samples and μ is the mean value. This statistical approach helps distinguish signal peaks from noise more effectively than traditional fixed-threshold methods. Experimental results demonstrate that the proposed kurtosis-based threshold selection method significantly improves both accuracy and reliability in UWB TOA estimation. This research holds substantial importance for advancing performance in wireless communication systems. Thus, the kurtosis-based threshold selection method provides a viable solution for UWB TOA estimation, offering novel perspectives and methodologies for enhancing wireless communication system performance through intelligent signal processing techniques.