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To enhance the navigation performance and robustness of navigation system combining ultrawideband (UWB) and inertial navigation systems (INS) under complex indoor environments, an improved navigation method—Allan variance (AV) to assist a modified adaptive extended Kalman Filter based on the dynamic weight function (DWF-MAEFF)—is proposed. Firstly, AV is used to improved INS error dynamics by modeling the stochastic noise of an inertial sensor; which can compensate for inertial sensor error caused by stochastic noise during integrated navigation. Secondly, the MAEKF is developed by designing the weight function to adjust the weight of measurement noise reasonably and dynamically, which can further improve the robustness of the AEKF algorithm. Field tests were conducted to verify the effectiveness of the proposed navigation method. The result indicated that an improvement of up to 60% over the existing integrated navigation method based on EKF and AEKF can be obtained by the proposed method. [ABSTRACT FROM AUTHOR]

Pedestrian dead reckoning (PDR), used in state-of-the-art smartphones, calculates pedestrian positions by using built-in inertial sensors. However, the complex and changeable usage modes of smartphones have been obstructing the development of PDR in the field of gait detection. Since the measurement of the sensor is affected by noise, position errors will emerge, needing to be corrected periodically via external measurements. To this end, an optimization-based PDR (OBPDR) method for smartphones is proposed in this study. First, an improved finite state machine (IFSM) gait detection method is designed, which can improve the gait recognition rate and stability compared with the traditional peak detection method. Second, the step detection algorithm proposed in this paper is combined with a heading estimation to obtain the PDR dynamic model. Finally, the measurements of GNSS are fused to the PDR model, based on an adaptive extended Kalman filter (AEKF) algorithm, which can enhance the adaptability of the system to the environment in order to reduce the cumulative errors of PDR. Experiments are carried out to evaluate the performance of the proposed method. The results indicate that compared with the gait detection method, based on peak detection, and the integrated positioning method, based on an extended Kalman filter, the proposed method boasts favorable robustness and a high gait recognition rate, the recognition accuracy being kept between 97.5% and 98.5%; the average position error decreased by more than 67.25%. [ABSTRACT FROM AUTHOR]

The performance of low-cost Micro Electro Mechanical systems (MEMS) gyroscope severely deteriorates under unknown disturbances, such as temperature, vibration and shock. In this paper, gyroscope array based on deep learning is proposed to improve the accuracy of single gyroscope during unknown disturbances. First, the gyroscope raw data are subjected to modeling and estimation within long short-term memory (LSTM) neural network. By training the model within the LSTM network, the intricate patterns and dynamic variations present in the gyroscope data could be captured, resulting in the generation of high-quality state estimates. Furthermore, the dropout technique is introduced to finely adjust the LSTM model, with the aim of reducing overfitting risks and effectively diminishing the computational complexity of the network. Lastly, state estimates generated by the LSTM are ingeniously combined with the raw gyroscope observations, furnishing the Kalman filter (KF) with input. The simulation and experiment results demonstrate that compared with the gyroscope array based on KF and H ∞ , the proposed method exhibits better performance and robustness. [Display omitted] • Innovative deep learning-based gyroscope array to boost low-cost MEMS gyroscope performance under disturbances. • LSTM neural networks for intricate pattern capture in gyroscope data, resulting in high-quality state estimates. • Dropout technique optimizes LSTM, reducing overfitting and computational complexity. • State estimates from LSTM enrich the Kalman filter's input, significantly improving performance compared to traditional method. [ABSTRACT FROM AUTHOR]