In this study, the sequential quadratic programming (SQP) in nonlinear optimization theory is used to solve the filtered sine wave fitting (FSWF). Based on the speed azimuth display (VAD) algorithm, high-precision inversion of the vector wind field is achieved at low signal-to-noise ratio (SNR). In the simulation experiment, the root mean square errors of the inversion results are used as the evaluation index, and the direct sine wave fitting (DSWF) algorithm and the SQP-FSWF algorithm are compared. In the FSWF calculation, based on the spatial-temporal continuity of the wind field inversion results, the SQP algorithm and the quasi-Newton method in the unconstrained optimization algorithm are compared. The comparison results show that the inversion effect of SQP-FSWF is better than those of DSWF and the quasi-Newton method at low SNR. To further evaluate the reliability of the proposed algorithm, we perform the wind field measurement contrast experiments based on lidar and synchronous sounding balloon, in which we obtain the real echo signal of lidar and the wind field data of synchronous sounding balloon. The wind speed inversion results simulated by the SQP-FSWF algorithm and the results measured by synchronous sounding balloon as the comparison object are compared. It can be seen that for horizontal wind speed, the correlation coefficient, the average error, the root mean square error are 0.993, 0.2 m/s, 0.28 m/s; for horizontal wind direction, the correlation coefficient, the average error, the root mean square error are 0.988, 3.28°, 4.62°, respectively. Based on the comparison between the spatial-temporal continuity of the wind retrieval results, the proposed method at low SNR is advantageous, which is consistent with the results of the simulated data.