This paper proposes a novel zero velocity update (ZUPT) method for a foot-mounted pedestrian navigation system (PNS). First, the error model of the PNS is developed and a Kalman filter is built based on the error model. Second, a novel zero velocity detection algorithm based on the variations in speed over a gait cycle is proposed. A finite state machine including three states is employed to model a gait cycle. The state transition conditions are determined based on speed using a sliding window. Third, the ZUPT software flow is illustrated and described. Finally, the performances of the proposed method and other methods are examined and compared experimentally. The experimental results show that the mean relative accuracy of the proposed method is 0.89% under various motion modes.

Multi-task remote sensing sensors have become attractive development directions of aerial remote sensing system, which rely on distributed position and orientation system (DPOS) to provide multi-node motion parameters to achieve superior performance. DPOS depends on transfer alignment from its master system to slave inertial measurement units to obtain multi-node motion information. However, for DPOS, there are many factors like carrier maneuver mode and external disturbance which will result in time-varying measurement noise and further degrade transfer alignment performance of DPOS obviously. In this work, a transfer alignment method based on variational Bayesian cubature RTS smoothing is developed to improve the accuracy of DPOS, which is implemented by combining the cubature RTS smoothing algorithm and variational Bayesian estimation method to deal with the time-varying measurement noise. A semi-physical simulation based on real flight experiment has been conducted, the results show that the motion parameter accuracy has achieved noticeable enhancement than the existing cubature RTS smoothing algorithm.

Position and Orientation system (POS), a loosely integrated inertial navigation system (INS) and global positioning system (GPS), can provide high-accuracy motion information for the airborne remote sensing loads, which plays a crucial role in airborne remote sensing imaging. However, the airborne POS often suffers from the harsh environment, such as aircraft maneuver mode and other external disturbance, which will lead to measurement noise unknown and further affects the accuracy of motion parameters. In this paper, an adaptive central difference Kalman filter method based on expectation maximization algorithm is proposed, which can estimate measurement noise adaptively and further improve the performance of POS. A flight experiment is conducted and the results show that the proposed method achieves higher-accuracy motion information by compared with the traditional CDKF method and covariance matching.

Distributed position and orientation system (POS) plays an important role in the fields of aerial remote sensing, which serves the sensors by precise motion information. For distributed POS, the slave systems consist of low accuracy inertial sensors, which must depend on the high accuracy motion information of the master to proceed transfer alignment (TA) to improve accuracy. Generally, the TA filtering algorithms perform superior performance when the noise statistical characteristic is known and accurate, however like gust, engine vibration and other external disturbances both will cause the inertial sensors output with unknown, varying noises and performance decline. Aiming at this, a variational Bayesian central difference Kalman filtering algorithm for distributed POS real-time TA is developed to suppress the effect of external noise, incorporating the central difference Kalman filtering (CDKF) algorithm and variational Bayesian (VB) adaption theory. In detail, the algorithm is to apply the noise estimation by VB adaption to the CDKF algorithm to achieve real-time TA accuracy enhancement. Distributed POS flight test is devoted for the algorithm validation, by the comparison, evident progress in TA accuracy has been presented.

Airborne position and orientation system (POS) can provide high accuracy motion parameters for multiple aerial mission loads and has became a vital equipment in aerial remote sensing system. However, the airborne POS often suffers from the systematic kinematic model and observation model inaccuracy, which further affects the accuracy of motion parameters, and the conventional innovation-based adaptive method only deals with observation model inaccuracy and results in limited performance improvement. In this work, a dual adaptive factors filtering algorithm is adopted to deal with the kinematic model and observation model inaccuracy together, which has been validated by the real flight experiment and the results show that performance improvement has achieved.

For distributed Position and Orientation System (POS), multi-node motion information are measured mainly by transfer alignment technology. The KF-based non-linear filtering algorithm provides optimal integrated solution for transfer alignment. However, on account of uncertain external environment and flexible lever arm compensation error, the noises of transfer alignment exhibit non-Gaussian property and further cause lower accuracy of distributed POS. In this paper, Correntropy-based CDKF is proposed by combining central difference Kalman filter (CDKF) and maximum correntropy criterion (MCC), which aims to decrease the effect of non-Gaussian noises. We rebuild a linear regression model and define a cost function under MCC to modify measurement information, and finally accurate state estimation is achieved. Meanwhile, the error model of nonlinear transfer alignment is formulated where flexible deformation and lever arm effect are considered, and the attitude and velocity information of main POS are transferred to slave IMU to improve its performance. Based on a semi-physical simulation, the performance of proposed method is proved to outperform the standard CDKF in term of estimation accuracy.