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For the novelty Mode-Converted ROV(MC-ROV), a set of MEMS-based integrated inertial navigation system has been developed, including gyroscope, accelerometer, magnetic compass, depth sensor and a micro-controller. Utilizing complementary filter to restrain the drifts of gyroscope while Kalman filter is designed to estimate attitude angles. This paper applies an improved adaptive Kalman filter (AKF), stressing the effects of new data with adding weights to gradually leave old data behind to avoid divergence, to further improve the navigation effects. The pool experimental results present that complementary filter and Kalman filter can get stable and high-precision effects. Meanwhile, algorithm simulations based on the real measured data demonstrate that the improvement of fading memory AKF based on exponential weighting can better the navigation results to some degree.