This paper reports on an experimental comparison of three synchronous clock, acoustic, distributed navigation algorithms commonly found in the underwater robotics community and literature: a naively distributed extended Kalman filter (NEKF), the interleaved update (IU) algorithm, and a decentralized extended information filter (DEIF). Traditional deadreckoned underwater navigation methods result in unbounded error growth as subsea vehicles do not typically have access to an absolute position reference. Synchronous-clock acoustic navigation systems can provide one-way travel time (OWTT) range constraints to nearby vehicle nodes thereby bounding error. Several distributed estimation algorithms for such scenarios have been proposed by the community; however, each makes fundamentally different trade offs in various specifications such as scalability, complexity, directionality, and consistency. We report an experimental comparison between the performance of each algorithm as compared to the benchmark solution of a centralized extended Kalman filter (CEKF) applied to a variety of 2-node and 3-node vehicle network topologies using data collected from two Ocean-Server autonomous underwater vehicles (AUVs) and a surface craft.