Abstract: In the process of radar tracking to target, the measurement noise always shows non-Gaussian characteristics representedby flicker noise under the influence of echo interference at different positions, which seriously destroys the Gaussian hypothesis ofKalman filter algorithm and makes the estimation accuracy decreased greatly. At the same time, particle filter which doesn′t depend on Gaussian hypothesis has particle degradation problem and can′t achieve good tracking performance. To solve this problem,a genetic resampling particle filter (GRPF) is proposed based on the idea of biological genetics. In the resampling process, the selection, combination and mutation of particles are respectively used to simulate the selection, crossover and mutation of naturalgenes, which ensures the completeness and representativeness of particle swarm, avoids particle degradation in the iterativeprocess, and solves the filtering problem under the condition of non-Gaussian measurement noise. In addition, the proposed algorithm is applied to free-flight tracking and the mathematical simulation is carried out. Simulation results indicate better adaptabilityand estimation accuracy of GRPF over the extended Kalman filter and Huber filter under the condition of non-Gaussian noise.