An Improved Statistics Fingerprint Analysis Method for Time Delay Estimation in Multipath NLOS Environment

This paper focuses on time of arrival (TOA) measurement for wireless personal area network (WPAN) under the circumstances that dense multipath and non-line of sight (NLOS), which exploits a non-coherent receiver for millimeter wave (mm-Wave) pulses. For the localization enhancement, an improved statistics fingerprint analysis (SFA) method is presented for energy-assisted TOA estimate and the ranging error mitigation using artificial neural network (ANN) by fourth order cumulants (FOCs) technique. The corresponding mean absolute error (MAE) is utilized as a measurement to access the performance of the developed method. Simulation results indicate SFA can outperform TOA estimation scheme significantly proposed in WPAN previously, show that SFA can effectively enhance the accuracy of TOA estimates evidently. The advantages of SFA such as low complexity implementation, fast convergence, high accuracy, etc. make it an attractive positioning method in WPAN compared with a coherent receiver.