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In order to solve the problem of low precision caused by sample dilution in wireless sensor target tracking based on particle filter (PF), a WSN target tracking method based on improved cuckoo particle filter is proposed. The filter algorithm of the improved cuckoo algorithm replaces the particle filter resampling process, mainly through the adaptive adjustment of the search step value and the probability of discovering exotic bird eggs in the cuckoo algorithm, and meanwhile, the change trend of function value is introduced into the step update equation in real time. It can guide particles to move to a higher random region on the whole, effectively adjust the adaptability of global exploration and local exploration, improve particle dilution and local extreme value problems, and increase the diversity of particle swarm to improve tracking performance. The experimental results show that the improved resampling method of cuckoo 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. Compared with CS-PF algorithm and PF algorithm, The ICS-PF algorithm has the shortest calculation time, the ICS-PF algorithm has the smallest mean square root error of position and velocity (position 0.0306, 0.0213, speed 0.0253, 0.0102), and the PF algorithm has the lowest tracking accuracy. The Tracking accuracy of ICS-PF is higher, and the Algorithm has been proved to have good tracking performance.