This paper addresses the issue of maintaining trajectories of nearby targets in passive detection scenarios. Traditional association methods based on DBSCAN are prone to parameter mismatches during target proximity, leading to decreased accuracy in trajectory association and resulting in trajectory misassociation or termination. Building upon this, the paper draws inspiration from parameter search concepts and utilizes the OSPA distance formula as a clustering scoring function. An improved trajectory maintenance strategy is proposed. Simulation results demonstrate that the proposed algorithm exhibits higher accuracy in target association during periods of target proximity.