Abstract
In wireless sensor networks, ranging or positioning via ultra-wideband (UWB) has caused widespread research interests where the non-coherent energy detection (ED) method with low sampling rate and low complexity is widely studied. However, the traditional energy detection methods only analyze the signal energy in the time domain, so their error is relatively large. In this paper, the simulation results show that most of the signal energy concentrates in the low-frequency band, so a novel threshold selection method for time of arrival (TOA) estimation is proposed that analyzes the signals in both time domain and frequency domain. In this method, the received signal is decomposed by “db6” wavelet and the kurtosis of energy blocks of the low-frequency wavelet coefficients (Kc) is analyzed. At last, the mapping relationship between Kc and the normalized threshold for TOA estimation is created using polynomial fitting with degree 3. The simulation results show that the TOA estimation error of the proposed method is significantly less than the method without wavelet decomposition.
Highlights
In recent years, with the development of wireless communication technologies [1, 2], the applications of wireless sensor networks are more and more widely used
In order to improve the capacity of anti-interference, pulse position modulation (PPM) and time hopping spread spectrum (TH-SS) [7] are used which can be expressed as Eq (2)
6 Conclusions In the UWB ranging system, the energy detection method based on non-coherent receiver is widely used
Summary
With the development of wireless communication technologies [1, 2], the applications of wireless sensor networks are more and more widely used. The first approach is matched filter (MF) based on coherent algorithm with high sampling rate [20]. The third is energy detection (ED) algorithm based on non-coherent receiver with low sampling rate and low complexity [8, 9, 21]. In this paper, after the wavelet transform used in the received signal, the high-frequency coefficients are discarded, and only the low-frequency coefficients are used as the received signal energy to improve the accuracy of ranging In this way, the white Gauss noise interference in the signal can be reduced effectively.
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