Aiming at the lack of effective methods for testing abnormal signals during the operation of unmanned vehicles, the paper focuses on signal anomalies caused by environmental disturbances in reliability driving tests. By using the correlation of signals from multiple sensors in both the time domain and spatial domain, a crossmathematical model is established based on the multisensor data. The signals collected from sensors are assigned as the row elements and the sensors as the column elements within the signal matrix. This numerical method transforms the original multisensor signals into a parameterized signal matrix model. A method combining matrix completion and deep matrix decomposition fusion (MC+DMF) is proposed to recover certain abnormal signals resulting from environmental disturbances. According to the forward propagation characteristics of the neural network, dimensionality reduction is applied to the row vectors (data collected by individual sensors at time i ) and column vectors (sensor arrays) in the original matrix. This process reduces the computational load during feature extraction from the distorted signal matrix. Additionally, the Hadamard product is used to regularize the MC+DMF loss function after feature extraction to avoid overfitting. The proposed method is applied on the SODA10M and KITTI public datasets, and comparing with traditional approaches, such as the single matrix factorization (MF), probability matrix factorization (PMF) and BiasSVD, the experiments using root mean square error (RMSE) show that the method can effectively detect abnormal sensor signals caused by vibration interference during driving. The results show that the MC+DMF method can greatly reduce the data recovery error and time. Compared with the probability matrix decomposition method, it achieves a 1% lower error rate and approximately 20.65% less recovery time.