Energy-Based Timing Estimation and Artificial Neural Network Based Ranging Error Mitigation in mm-Wave Ranging Systems Using Statistics Fingerprint Analysis

摘要: Non-line-of-sight (NLOS) and dense multipath problems are the major challenges for millimeter wave (mm-wave) indoor ranging systems. To acquire time of arrival (TOA) estimate accurately in such a poor environment, an improved statistics fingerprint analysis algorithm energy-based timing estimation artificial neural network (ANN) based error mitigation is presented this paper. The developed can obtain TOA by measuring kurtosis, skewness, standard deviation, minimum slope, gradient received mm-wave pulses. ANN employed to mitigate on obtained nonlinear regression between thresholds analyzed characteristics numerical simulation results indicate proposed achieve significant performance improvements both line sight NLOS channels IEEE 802.15.3c standard, as compared conventional algorithms.