Abstract: Particle swarm optimization (PSO) square root strong tracking cubature Kalman filter was proposed for underwater transponder positioning/dead reckon (UTP/DR) integrated navigation system. Firstly, the square root strong tracking cubature Kalman filter was designed, which views strong tracking filter (STF) as the basic theory framework. Secondly, a novel particle swarm optimization algorithm has been introduced, the swarm split into a pair of particles, and comparing the performance of two particles of each pair, the better particle search direction focuses on the swarm historical experience, and the other particle search direction focuses on self historical experience. Finally, the novel particle swarm optimization algorithm was used to calculate the strong tracking filter fading factor. The result of simulation showed that under the condition of the system model being not accurate, the proposed algorithm can effectively track the change of state, and shows better filtering accuracy and stability than cubature Kalman filter.