A Barometer-Enhanced State Adaptive Indirect Kalman Filter for 3-D Indoor Navigation

In order to achieve accurate 3-D indoor positioning, a state adaptive indirect Kalman filter (SA-IKF) framework is proposed, which integrates the state recognition algorithm and the barometric height constraint algorithm. The state recognition algorithm can enhance the horizontal positioning by identifying three states: level, stairs, and lift through dual threshold judgment and continuous wavelet transform (CWT), and adaptively adjust the indirect Kalman filter (IKF) gain to adapt to multiple states. The barometric height constraint algorithm can obtain accurate height by adaptively constraining the height based on the detected state and then suppress vertical errors. Compared with other state-of-the-art methods, the experimental results show that the proposed SA-IKF framework can effectively reduce horizontal and vertical errors in multiple motion states (horizontally about 0.04%–0.74% total distance and vertically about 0.21%–0.48% total height).