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In order to solve the problem of low precision caused by sample dilution in WSN target tracking based on particle filter (PF), a WSN target tracking method based on adaptive bat particle filter was proposed. The re-sampling process of particle filtering was optimized by the improved bat algorithm, and the fitness function of the particle was defined based on the latest observations to guide the particle to move to a higher random region on the whole. At the same time, the dynamic adaptive inertia weight is used to explore the new particle position update as the design mechanism, and the dynamic adaptive inertia weight value is introduced to effectively adjust the global exploration and local exploration adaptability, improve the particle dilution and local extreme value problems, and increase the particle swarm diversification to improve the tracking performance. The experimental results show that the resampling method of the adaptive bat particle filter algorithm can prevent the degradation of particles, increase the diversity of particles, reduce the tracking error, reduce the running time of the algorithm, and greatly improve the real-time tracking performance. IBAPF algorithm has the shortest computation time. Compared with BAPF algorithm and PF algorithm, IBAPF algorithm has the smallest mean square root error of position and velocity (position 0.0313, 0.0270, speed 0.02021, 0.0102), PF algorithm has the lowest tracking accuracy, and IBAPF algorithm has the highest tracking accuracy. The IBAPF algorithm is proved to have good tracking performance.