The integration of Inertial Navigation System (INS) and Global Positioning System (GPS) architectures can be achieved through the use of many filters such as, an extended Kalman filter, an unscented Kalman filter, divided difference filter, and particle filter. The main objective of all the above filters is to provide accurate fusion of the data from GPS and INS to predict INS-only navigation solution during GPS outages. The prediction mode performance of all these filters is very poor with significant drift in the INS-only solution. Time domain approaches are traditionally used to improve the INS-only solution such as, neural network models. In this paper, a new frequency domain method is proposed in which the frequency band of interest can easily be selected and used in the modeling. To develop a frequency domain method for INS/GPS bridging model for GPS outages, the frequency response of the INS/GPS system must be investigated. The Least Squares Spectral Analysis (LSSA) and a parametric transfer function in the complex Z-plane are employed to develop the frequency response. The input to this system is the INS-only solution and the output is the INS/GPS integration solution. Then, the discrete inverse Z-transform of the parametric transfer function is applied to estimate the impulse response of the INS/GPS system. To examine the performance of the proposed approach, a kinematic dataset is collected in Hamilton Harbour onboard a hydrographic surveying vessel owned by the Canadian Hydrographic Service, Canada. The loosely coupled INS/GPS integration with unscented Kalman filter is developed to obtain an INS/GPS integrated navigation solution and an INS-only solution. Then, the INS/GPS and INS-only navigation solution are used to develop the impulse response of the INS/GPS system. It is shown that the developed impulse response can be used to detect and recover the long-term motion dynamics during GPS outages with about 80% dynamic recovery for longitude solution and 50% dynamic recovery for east velocity solution when compared with the INS-only solution (prediction mode of the INS/GPS filter).