Target detection, classification, localization, and tracking (DCLT) using horizontal arrays and passive sonar is a well-studied problem. Good detection results can be obtained using narrow beams that maximize signal to noise ratio. Beamforming also provides azimuth angle, but is generally not reliable for range and depth estimation. Matched field processing (MFP) makes use of environmental knowledge to predict the received signal at the array, and correlates predicted and received signals to estimate target range and depth. However, poor or inaccurate environmental knowledge degrades MFP performance, and in general, MFP suffers from high side lobes or ambiguity in the range/depth probability surface. Received signal amplitude statistics have been used to estimate source depth, but the method has not been studied extensively to date. Recently several methods have been developed involving received signal amplitude statistics predicted using environmental parameter statistics to construct statistically valid descriptions of the environment, which were then fed to an ocean acoustic propagation model (i.e., Monte Carlo simulation). Performance of the algorithms has been evaluated using data from the Swellex-96 experiment. [Work sponsored by ONR Undersea Signal Processing.]