Terrain matching can provide position information to calibrate the inertial navigation system ( INS). However, underwater vehicles are often limited in their operational time within areas where the depth geographic information feature map can be matched. The position information provided by terrain matching is inherently uncertain. These factors lead to several challenges, including unknown segments of usable information and error characteristics that exhibit a variance-changing Gaussian distribution. To address these issues, this article proposes an adaptive Kalman filter based on performance monitoring of the measurement information (MMAKF) algorithm. First, a forward and backward adaptive Kalman filter utilizing data backtracking technology is designed. Then, the filtering results are employed to calibrate the INS. The results of sea tests show that the proposed MMAKF algorithm is effective for the comprehensive calibration of the INS during short-duration position information. This calibration method effectively corrects both position and attitude to values close to the reference, thereby achieving rapid and comprehensive calibration of the underwater INS.