Time of Flight Estimation in the Presence of Outliers: A Biosonar- Inspired Machine Learning Approach.

摘要

When the Signal-to-Noise Ratio (SNR) falls below a certain level, the error of the Time-of-Flight (ToF) Maximum Likelihood Estimator (MLE) increases abruptly due to the well known threshold effect. Nevertheless, operating near and below the threshold SNR value might be necessary for many remote sensing applications due to power-related constraints. These constrains may include a limit on the maximum power of a single source pulse or a limit on the total power used by multiple signals transmitted during a single measurement. For narrowband signals, the threshold effect emerges mostly due to outliers induced by local maxima of the autocorrelation function of a source signal. Following the previously explored path of biosonar-inspired echo processing, in this research we introduce new methods for ToF estimation in the presence of outliers. The proposed methods employ a bank of phase-shifted unmatched filters for generating multiple biased but only partially correlated estimators (multiple experts). Using machine-learning techniques, the information from the multiple experts is combined together for improving the near-the-threshold ToF estimation from a single echo. We describe methods for ToF estimation from single and multiple pulses as well as the method for improving the energy efficiency of the estimation.