A multi-sensor data rate management and task assignment fusion optimization algorithm under multi-target tracking scenario is proposed to solve the problem of multi-radar sensor resource allocation in co-detection of ship-borne aircrafts. Based on the multi-sensor distribution model controlled by covariance, a fusion optimization model of multi-sensor data rate management and task assignment is established by constriction of target priority and sensor performance conditions. By introducing dwell time improvement factors into the sequential Kalman filtering algorithm, the fusion covariance of sensor combination state estimation under different sampling intervals is calculated, and the optimal sampling interval and the sensor combination are solved. Simulation results show that the proposed fusion optimization algorithm can adaptively optimize the data rate and radar allocation combination, improve the multi-sensor tracking capability, effectively save radar resources, and has faster convergence speed and steady-state accuracy compared with other methods.