In this study we investigated Kalman filter-based attitude estimation algorithms, considering external acceleration and bias effects in several different scenarios. Towards these goals, gyro biases were first estimated, or calibrated, in all three applied algorithms. Whereas external acceleration effects were not considered in the first algorithm, external acceleration effects were compensated for in the second and third algorithms, using the Kalman filter’s residual and acceleration model. Low, intermediate, and high external acceleration scenarios were then implemented in our test-bed. Three different rotational frequencies (0.3, 3, and 6 ㎐) for roll and pitch rotations were applied. Performance of each estimation algorithm was analyzed using slopes, y-intercepts, and standard deviations obtained from the linear regression. Our results confirm that attitude estimation errors are linearly proportional to the magnitude of the applied external acceleration. Perhaps most importantly, our results show the second algorithm may be used to provide relatively uniform and accurate estimation performance for low- and high-frequency motions.