To address the low pose estimation accuracy and unreliable information output in small unmanned surface vessels caused by complex water surface environments and low-frequency vibration interference, this paper proposes a pose estimation algorithm based on smoothed iterated error-state Kalman filtering. Under low-speed operating conditions, the algorithm employs an accelerometer to compensate and correct pitch and roll angles. In the data fusion process of micro-electro-mechanical system （MEMS） sensors, an improved fixed-interval smoothing algorithm is adopted, which utilizes the innovation from the next time step to perform backward smoothing corrections on the error state variables while conducting time-reversed inverse corrections, thereby reducing the interference of low-frequency linear vibrations on effective signals. The smoothed estimates are used to predict and correct the measurement values, with each time step′s innovation iteratively refining both the estimated and measured values to enhance overall pose estimation accuracy.Experimental results demonstrate that compared to the standard error-state Kalman filter，the proposed SIESKF algorithm reduces the root mean square errors (RMSEs) of roll, pitch, and yaw angles by 0.762 1°, 1.818 8° and 0.340 5°, respectively. Under normal water surface navigation conditions, the RMSEs of eastward, northward, and upward velocities decrease by 0.402 3, 0.239 4 and 0.116 5 m/s, respectively. Similarly, the RMSEs of eastward, northward, and upward positions are reduced by 0.148 4, 0.258 9 and 0.083 2 m. This algorithm can provide more precise pose information for USVs.