Although the onboard microvibration has a very small amplitude, it is one of the major factors contributing to platform jitter and a decline in pointing stability and precision. The frequency range of a microbiration is very wide (1 Hz-1000 Hz), among which the most prominent disturbance is in the range of 10-200 Hz. It goes far beyond AOCS's range. To prevent its impact on the spacecraft, it is essential to research and implement practical countermeasures. One of the most significant sources of disturbance, RWA has drawn the attention of many academics. Examples include the empirical and analytical models of RWA disturbances, in references (2016, 2017, 2018, 2019), parameter estimation, identification of RWA, in references (2018, 2019), and nonlinear electromechanical coupling dynamics of RWA, in reference (2019). Evaluating the influence of the disturbance on the pointing accuracy of a spacecraft is an arduous task because it involves multiple disciplines, such as structural dynamics, control systems, mechanisms, and system engineering (2019). The integrated modeling is an efficient method, as demonstrated by the development of DOCS (Dynamics-Optics-Controls-Structures) by SSL (Space System Laboratory, Cambridge, MA, USA) at MIT (2019, 2019), IMOS (Integrated Modeling of Optical Systems) by Jet Propulsion Laboratory (JPL) (Pasadena, CA, USA) (2019), Integrated Modeling Environment (IME) by Goddard Space Flight Center (Greenbelt, MD, USA), Constellation Software Engineering (CSE) (Annapolis, MD, USA) (2019), and Integrated Framework by Korea Advanced Institute of Science and Technology (Daejeon, Republic of Korea) (2020). Acquisition of the frequency response function (FRF) from the disturbance source to the sensitive payload is one of the most crucial stages in integrated modeling, and many scholars have worked hard on this (2019, 2019, 2019). It is challenging to measure the FRF directly due to the restricted room for RWA installation in real spacecraft, particularly in small satellites. The standard procedure is to use a shaker to measure the FRF from the location of the RWA installation to the sensitive payload. The response of the sensitive payload derived by multiplying the FRF and the disturbance force and tongue measured in a hard-mounted boundary condition is quite different from the test result because of the coupling between the RWA and the support structure. Therefore, additional complex tests are needed to compensate for the disturbance force of the RWA, such as the static dynamic mass measurement techniques, for when the RWA is at 0 rpm (2019, 2019), the dynamic mass measurement method, which includes the gyroscopic effects in its accelerants (2019, 2019), a combination of the experiments in a static condition, and analysis that includes the gyroscopic effect through the RWA finite element (FE) model (2019). There are many various FRF estimation techniques [28; 29; 30], yet the most widely used ones are the \(\mathbf{H}_{1}\), \(\mathbf{H}_{2}\), and \(\mathbf{H}_{\mathrm{v}}\) algorithms. The \(\mathbf{H}_{1}\) method assumes that there is no error in the references, while the \(\mathbf{H}_{2}\) method assumes that there is no error in the responses. The \(\mathbf{H}_{\mathrm{v}}\) method provides the most precise estimate by accounting for the input and output noises. This paper presents a general approach based on the \(\mathbf{H}_{\mathrm{v}}\) algorithm to acquire the FRF from the disturbance source to the sensitive payload, which is excited by the RWA, itself, and does not need any additional excitation source. The FRF obtained by this method accounts for the coupling between the RWA and the spacecraft, so the response of the sensitive load can be obtained by multiplying the RWA disturbance measured at a fixed interface using the FRF. The paper is organized as follows: in Section 2, an integrated model of a high-precision optical remote sensing satellite is developed that consists of the disturbance model, structural dynamic model, and the optical model.