To achieve multi-target tracking tasks in complex water environment, a multi-target tracking method based on slope constraint and backtracking search strategy is proposed. Firstly, the threshold detection method is used to extract targets based on the bearing-time recording (BTR) data and the underwater kinematics analysis. Then, a novel hypothesis generation rule is proposed under the frame of traditional multi-hypothesis tracking (MHT) algorithm, which could be termed as slope constraint and measurements sharing. When the trajectory is interrupted, the retrospective search method is used to determine the interruption starting track point of targets. In addition, the cubature Kalman filter (CKF) is utilized to predict and compensate the interrupted trajectory. The hypothesis generation result is reduced to optimize the space complexity of algorithm. Experimental results show that this strategy can complete tasks such as multitarget automatic association tracking, interrupted track automatic prediction, and automatic track termination. The the average root mean square error of target tracking is 0. 594 4°, and the average running time of the algorithm is 0. 826 5 s.