In this paper, the well-known multiplicative extended Kalman filter (MEKF) is re-investigated for attitude estimation using vector observations. From the Lie group theory, it is shown that the attitude estimation model is group-affine and its error state model should be trajectory-independent. Moreover, with such a trajectory-independent error state model, the linear Kalman filter is still effective for large initialisation errors. However, the measurement model of the traditional MEKF is dependent on the attitude prediction, which is therefore trajectory-dependent. This is also the main reason why the performance of traditional MEKF is degraded for large initialisation errors. Through substitution of the attitude prediction related term with vector observations in the body frame, a trajectory-independent measurement model is derived for MEKF. Meanwhile, MEKFs with reference attitude error definition and with global state formulating on special Euclidean group have also been studied, with the main focus on derivation of the trajectory-independent measurement models. Extensive Monte Carlo simulations of spacecraft attitude estimation implementations demonstrate that the performance of MEKFs can be much improved with trajectory-independent measurement models.

Transverse arrangement is one of the main methods used in the polar inertial navigation system (INS). In the traditional algorithm, the calculation of using the earth ellipsoid model is complex, while using the earth sphere model cannot satisfy a high-accuracy application. Therefore, an approach based on the virtual sphere model is proposed, which has been proved in simulation experiments to reduce the computational complexity and maintain the same accuracy as the ellipsoid algorithm, but its accuracy has not yet been proved in theory. Starting from the basic principles of the ellipsoid and virtual sphere model algorithm, this paper compares the key formulations of the two. Finally, it is proved that the two arrangements are actually the same.

In traditional satellite navigation receivers, the parameters of tracking loop such as loop bandwidth and integration time are usually set in the design of the receivers according to different scenarios. The signal tracking performance is limited in traditional receivers. In addition, when the tracking ability of weak signals is improved by extending the integration time, negative effect of residual frequency error becomes more and more serious with extension of the integration time. To solve these problems, this paper presents out research on receiver tracking algorithms and proposes an optimised tracking algorithm with inertial information. The receiver loop filter is designed based on Kalman filter, reducing the phase jitter caused by thermal noise in the weak signal environment and improving the signal tracking sensitivity. To confirm the feasibility of the proposed algorithm, simulation tests are conducted.

In this paper, adaptive noise estimation is used along with a previously proposed Huber-based robust algorithm for cooperative localisation of Autonomous Underwater Vehicles (AUVs). The Huber-based robust cooperative localisation algorithm named Huber-based Iterative Divided Difference Filtering (HIDDF), proposed in our previous work, effectively achieved a robust result against abnormal measurement noise, enhanced the stability of the filtering algorithm and improved the performance of cooperative localisation state estimation. However, its performance could be significantly further improved if it could estimate the system's noise statistical properties online in real time and then adaptively adjust the filtering gain matrix accordingly. In this paper, a novel adaptive noise estimation algorithm is proposed based on a covariance matching method. The proposed algorithm is suitable for adaptively estimating Gaussian and non-Gaussian measurement as well as process noise. The efficacy of the proposed algorithm has been verified through simulation results. In order to further verify the effectiveness of the proposed algorithm in practical systems, lake tests were conducted. Then, based on offline test data, the performance of the cooperative positioning algorithm under dual-pilot and single-pilot schemes was simulated. The advantages and feasibility of the algorithm are analysed and compared through performance comparison. Cooperative localisation accuracy of the previously proposed Huber-based robust algorithm has been enhanced significantly when used with the proposed adaptive noise estimation algorithm.

This paper offers an algorithm for enhancement of positioning accuracy of a quad-rotor flying robot, based on jerk and jounce of motion. The suggested method utilises the first and second numerical derivatives of the vehicle's acceleration and augments the mathematical model in the estimation process. For this purpose, the Kalman Filter (KF) is implemented for integration of a Strapdown Inertial Navigation System (SINS) and Global Navigation Satellite System (GNSS). The required data are collected from a low-cost/quality Micro Electromechanical Sensors (MEMS) during an assisted flight. For increasing the precision and accuracy of the collected data, all instruments including accelerometers, gyroscopes and magnetometers are calibrated before the experiments. Moreover, to reduce and limit the measurement noises of the MEMS sensor, a low-pass filter is applied; this is while sensors in the autopilot are affected by high levels of noise and drift, which makes them inappropriate for accurate positioning. The experimental results exhibit an improvement in positioning and altitude sensing through augmentation of the loosely coupled SINS/GNSS navigation method.

Zero velocity update (ZUPT) is widely discussed for error restriction in land vehicle Inertial Navigation Systems (INSs) and wearable pedestrian INSs to overcome the problems of Global Positioning System (GPS) unavailability in urban canyons or indoor scenarios. In this paper, an online smoothing method for ZUPT-aided INSs is presented. By introducing the Rauch–Tung–Striebel (RTS) smoothing method into the ZUPT-aided INS, position errors can be effectively restrained not only at stop points but during the whole trajectory. By integrating reverse navigation with a ZUPT smoother, the method realises forward and real-time processing. Compared with existing approaches, it can improve the position accuracy in real time without any other sensors, which is well suited for applications on high-accuracy navigation in GPS-challenging environments. Accuracy test results with different Inertial Measurement Units (IMUs) show that the developed method can significantly decrease position errors from hundreds or thousands of metres to below ten metres. During the whole trajectory, the online smoothing method ensures the maximum position errors at non-stop points can reach the same level of accuracy at stop points. A delay test result proves that the delay of the reverse online smoothing method proposed in this paper is much shorter than existing online smoothing methods.

The transverse navigation system has been designed and developed to solve the challenges of navigation in polar regions. However, considerable theoretical errors are introduced into the system when the spherical Earth model is adopted. To tackle this problem, a transverse navigation mechanism under the ellipsoidal Earth model has been proposed in this research and the application regions of the proposed algorithm are specified and evaluated through error analysis. The analysis shows the presented transverse navigation system works in both polar and part of the non-polar regions. Field tests were conducted to evaluate the navigation performance in Nanjing, a non-polar region. A novel experimental method, where the field test data in mid-latitude areas was used to simulate the real Inertial Measurement Unit (IMU) data and the reference information in polar regions, was adopted to investigate the performance of the proposed algorithm in polar areas. The results show: that in the mid-latitude areas, the presented transverse navigation system achieves the same accuracy as the traditional inertial navigation system and that in polar regions, the proposed transverse mechanism outperforms the traditional method with a much lower error in longitude and yaw.

A dual-axis rotational Inertial Navigation System (INS) has received wide attention in recent years because of high performance and low cost. However, some errors of inertial sensors such as stochastic errors are not averaged out automatically during navigation. Therefore a Twice Position-fix Reset (TPR) method is provided to enhance accuracy of a dual-axis rotational INS by compensating stochastic errors. According to characteristics of an azimuth error introduced by stochastic errors of an inertial sensor in the dual-axis rotational INS, both an azimuth error and a radial-position error are much better corrected by the TPR method based on an optimised error propagation equation. As a result, accuracy of the dual-axis rotational INS is prominently enhanced by the TPR method, as is verified by simulations and field tests.

Precision inertial navigation depends not only on the quality of the inertial sensors (accelerometers and gyros), but also on the accuracy of the gravity compensation. With a view toward the next-generation inertial navigation systems, based on sensors whose errors contribute as little as a few metres per hour to the navigation error budget, we have analyzed the required quality of gravity compensation to the navigation solution. The investigation considered a standard compensation method using ground data to predict the gravity vector at altitude for aircraft free-inertial navigation. The navigation effects of the compensation errors were examined using gravity data in two gravimetrically distinct areas and a navigation simulator with parameters such as data noise and resolution, supplemental global gravity model noise, and on-track interpolation method. For a typical flight trajectory at 5 km altitude and 300 km/hr aircraft speed, the error in gravity compensation contributes less than 5 m to the position error after one hour of free-inertial navigation if the ground data are gridded with 2 arcmin resolution and are accurate to better than 5 mGal.

A fuzzy logic controller for an omni-directional, autonomous guided vehicle (AGV) implementing Mecanum wheels to achieve directional control of its motion is presented. Omni-directional robotic platforms advantage over conventional designs is that they are capable of performing tasks in congested environments. The AGV's behaviour during navigation is controlled using a fuzzy logic controller, with slip detection.

We propose in this paper a method to enhance the performance of a coupled global positioning/inertial navigation system (GPS/INS) for land navigation applications during GPS signal loss. Our method is based on the use of an artificial neural network (ANN) to intelligently aid the GPS/INS coupled navigation system in the absence of GPS signals. The proposed enhanced GPS/INS is tested in the dynamic environment of a land vehicle navigating around a closed path on the METU campus and we provide the results. Our GPS/INS+ANN system performance is thus demonstrated with a land trial.