Abstract: ‍ ‍The paper proposes a distributed multi-target joint localization and tracking method based on the matched field localization measurement model to address the issues of limited detection range and insufficient target localization and tracking performance of underwater single hydrophone arrays. The proposed method aims to address the limitation of conventional matched field localization method， which relies solely on selecting coordinates that match the number of targets of interest. To mitigate the adverse impact of mismatched acoustic field parameters and the actual environment on localization accuracy， the proposed method selects coordinates corresponding to peaks in the ambiguous functions generated after matched field localization at each array node that exceed a set threshold as measurements. The measurements are then subjected to the cardinality balanced multi-Bernoulli filtering algorithm to filter out noise interference. Under a distributed network architecture， to fully utilize the received information of different array nodes in the distributed fusion structure and thus improve the tracking accuracy of multiple underwater targets， the multi-target posterior probability density obtained from each array node is sequentially fused with its neighboring array nodes through the Generalized Covariance Intersection fusion law. Due to fusing the posterior probability density of multiple targets instead of the measurement set itself， the method improves the high communication burden problem of centralized fusion processing. The simulation results demonstrate that distributed fusion leads to a significant decrease in the average Optimal Subpattern Assignment （OSPA） distance， and a clear improvement in the tracking accuracy of both the state and cardinality of multiple targets compared to the single hydrophone array multi-target tracking algorithm from multiple Monte Carlo experiments. Under the condition of significantly reduced communication and computation burdens on the system and individual nodes， it is possible to achieve tracking accuracy comparable to centralized fusion processing.