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Threshold Selection for TOA Estimation based on Skewness and Slope in Ultra-wideband Sensor Networks

Accurate localization has gained significant interest within sensor networks but because of the high sampling rate, Matched Filter (MF) based coherent Time of Arrival (TOA) estimation algorithms are not practical for low cost, low complexity Ultra-wideband (UWB) ranging, positioning and tracking systems. In this paper, an Energy Detection (ED) based non-coherent TOA estimation algorithm is presented where the TOA is estimated via Threshold Crossing (TC). The expected values of skewness, maximum slope, kurtosis and standard deviation with respect to the Signal to Noise Ratio (SNR) are investigated using the IEEE 802.15.4a Channel Models (CM). It is shown that the skewness and maximum slope are more sensitive to the SNR and thus more suitable for TOA estimation. To improve the precision of ED based TOA estimation, especially in low SNR environments, a new threshold selection algorithm is proposed which is based on a joint metric of the skewness and maximum slope. The best threshold values for different SNRs are investigated and the effects of integration period and channel models are examined. Comparisons with other ED based algorithms show that in both the CM1 and CM2 channels, the joint metric provides higher precision and robustness in both high and low SNR environments.