The single hydrophone localization problem is considered. Single hydrophone localization is a special case of matched field localization where measurements from only one hydrophone are available. The time series of the pressure at the hydrophone is compared with predicted times series calculated using an ocean acoustic propagation model for many different source locations. The source location that gives the best match between the predicted time series and the measurement is assumed to be the correct source location. Single hydrophone localization algorithms from the literature are reviewed and a new algorithm is introduced. The new algorithm does not require knowledge of the source signal and does not assume the use of a particular ocean acoustic model, unlike some algorithms in the literature. Source location estimates calculated from the new algorithm are compared with ground truth using simulated ocean acoustic measurements and experimental measurements. Source location estimates calculated using other algorithms from the literature are shown for comparison. The simulated measurements use three source signals with bandwidths of 10 Hz, 100 Hz, and 200 Hz and the ocean is modeled as a Pekeris waveguide. The new algorithm estimates the source location accurately for all three source signals when several of the localization algorithms from the literature give inaccurate estimates. Gaussian white noise signals are added to the measured signals to test the impact of signal-to-noise ratio (SNR) on the algorithm. Four signal-to-noise ratios of 60 dB, 40 dB, 20 dB, and 0 dB are used. The new algorithm gives accurate source location estimates down to an SNR of 20 dB for two of the source signal bandwidths. Source location estimates using other algorithms from the literature break down at either 20 dB or 0 dB. Source location estimates are calculated using two hydrophone measurements taken at different depths in an experiment conducted near the Bahamas. The new algorithm accurately estimates the source location in both cases. In one case, only two other localization algorithms from the literature locate the source accurately. In the other case, only one other localization algorithm succeeds.