Multitarget tracking technology is the core topic in the field of intelligent driving. Multi-target complex manoeuvring, measurement outliers and unknown environmental prior parameters strongly affect the tracking accuracy of the target state. To address the accurate tracking of multitarget under the above complex working conditions, we propose a new multitarget tracking algorithm, named the Multiconstrained Generalized Probabilistic Data Association Filtering (MCGPDAF) algorithm. In this algorithm, we use the target position and heading information to construct constraint parameters to calculate the association probability between each effective measurement combination and the target track. This algorithm can effectively suppress the measurement association anomalies and aprior information errors, as well as enable the robust association of single-sensor multitarget measurements and accurate tracking of target states under complex working conditions. On this basis, a multitarget tracking method based on composite perception fusion is further constructed, and the correlation sequential track association algorithm and covariance cross fusion algorithm are used to enable the track association and the estimation and fusion of target states among multiple sensors, which further enhances the tracking accuracy of the multitarget state. The simulation and real vehicle experiment results reveal that, compared to current advanced algorithms, the RMSE and MAPE of the MCGPDAF algorithm for multitarget tracking are enhanced by an average of 23.97% and 24.35%, respectively. Additionally, the MOTA and MOTP of the MCGPDAF algorithm improve by an average of 14.68% and 15.71%. Moreover, compared to single-sensor multitarget tracking, the RMSE and MAPE of composite perception fusion results based on the MCGPDAF algorithm are further enhanced by 26.43% and 27.15% on average, which reflects the practicality of the tracking method showcased in this research.