Abstract: Most of traditional tracking algorithms are based on ideal models and achieve continuous position estimates and predictions by using filtering methods. However, in real-world, moving targets often have no patterns, termed as the random walk model. Hence, this paper proposes a lower-complexity solving scheme for this kind of disorder movement model. By changing positon tracking problem into a data sequence matching problem, the proposed algorithm provides a heuristic tracking scheme for random moving targets. Numerical simulation experiments show that this scheme can effectively reduce the complexity while ensuring tracking accuracy. Meanwhile, it has greater flexibility and universality.