This paper presents a robust train localisation system by fusing a Global Navigation Satellite System (GNSS) with an Inertial Navigation System (INS) in a tightly-coupled (TC) strategy. To improve navigation performance in GNSS partly blocked areas, an advanced map-matching (MM) measurement-augmented TC GNSS/INS method is proposed via an error-state unscented Kalman filter (UKF). The advanced MM generates a matched position using a one-step predicted position from a UKF time update step with binary search algorithm and a point–line projection algorithm. The matched position inputs as an additional measurement to fuse with the INS position to augment the degraded GNSS pseudorange measurement to optimise the state estimation in the UKF measurement update step. Both the real train test on the Qinghai–Tibet railway and the simulation were carried out and the results confirm that the proposed advanced MM measurement-augmented TC GNSS/INS with error-state UKF provides the best horizontal positioning accuracy of 0 ⋅ 67 m, which performs an improvement of about 71% and 90% with respect to TC GNSS/INS with only error-state UKF and only error-state Extended Kalman filter in GNSS partly blocked areas.

Vehicle state estimation and path prediction, which usually involve Kalman filter and motion model, are critical tasks for intelligent driving. In vehicle state estimation, the comparative performance assessment, regarding accuracy and efficiency, of the unscented Kalman filter (UKF) and the extended Kalman filter (EKF) is rarely discussed. This paper is devoted to empirically evaluating the performance of UKF and EKF incorporating different motion models and investigating the models’ properties and the affecting factors in path prediction. Extensive real world experiments have been carried out and the results show that EKF and UKF have roughly identical accuracy in state estimation; however, EKF is faster than UKF generally; the fastest filter is about 2⋅6 times faster than the slowest. The path prediction experiments reveal that the velocity estimate and the used motion model affect path prediction; the more realistically the model reflects the vehicle's driving status, the more reliable its predictions.

In this paper, we consider the joint estimation of the position of a spacecraft and debris in Earth orbit to achieve spacecraft localisation based on angular measurements and precise measurements of the debris relative to the spacecraft. The dynamic model of the spacecraft caters for several perturbing effects, such as Earth and Moon gravitational field asymmetry and the Earth's oblateness effect. The Moon's position is assumed to be accurately known for the purposes of simulation from published Jet Propulsion Laboratory (JPL) ephemerides. The measurement process is based on the elevation and azimuth of the Moon and the Sun with respect to the spacecraft reference system. Range measurements are not assumed to be available. Position and velocity of the spacecraft are estimated by using the Unscented Kalman Filter (UKF). The performance of the filters are evaluated on an example of an Earth-orbiting satellite at an altitude over 1200 km with measurements of the directions of the Moon and the Sun only. It is shown that the estimates of position and velocity components track the corresponding simulated position and velocity components.

A robust state estimation technique based on the Huber-based Cubature Kalman Filter (HCKF) is proposed for Global Positioning System (GPS) navigation processing. The Cubature Kalman Filter (CKF) employs a third-degree spherical-radial cubature rule to compute the Gaussian weighted integration, such that the numerical instability induced by round-off errors can be avoided. In GPS navigation, the filter-based estimation of the position and velocity states can be severely degraded due to contaminated measurements caused by outliers or deviation from a Gaussian distribution assumption. For the signals contaminated with non-Gaussian noise or outliers, a robust scheme combining the Huber M-estimation methodology and the CKF framework is beneficial where the Huber M-estimation methodology is used to reformulate the measurement information of the CKF. GPS navigation processing using the HCKF algorithm has been carried out and the performance has been compared to those based on the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and CKF approaches. Simulation and experimental results presented in this paper confirm the effectiveness of the method.

The Unscented Kalman Filter (UKF) is a well-known nonlinear state estimation method. It shows superior performance at nonlinear estimation compared to the Extended Kalman Filter (EKF). This paper is devoted to an investigation between UKF and EKF with different feedback control modes in vehicle navigation. Theoretical formulation, simulation and field tests have been carried out to compare the performance of UKF and EKF. The simulation and test results demonstrate that the estimated state of a UKF relies on the measurements and is less sensitive to historical model information. The results also indicate that UKF has benefits for prototype model design due to avoidance of calculation of a Jacobian matrix. EKF, however, is more computationally efficient and more stable.

In this paper, we propose an enhanced Monte Carlo localization (EMCL) algorithm for mobile robots, which deals with the premature convergence problem in global localization as well as the estimation error existing in pose tracking. By incorporating a mechanism for preventing premature convergence (MPPC), which uses a “reference relative vector” to modify the weight of each sample, exploration of a highly symmetrical environment can be improved. As a consequence, the proposed method has the ability to converge particles toward the global optimum, resulting in successful global localization. Furthermore, by applying the unscented Kalman Filter (UKF) to the prediction state and the previous state of particles in Monte Carlo Localization (MCL), an EMCL can be established for pose tracking, where the prediction state is modified by the Kalman gain derived from the modified prior error covariance. Hence, a better approximation that reduces the discrepancy between the state of the robot and the estimation can be obtained. Simulations and practical experiments confirmed that the proposed approach can improve the localization performance in both global localization and pose tracking.

A novel relative spacecraft attitude and position estimation approach based on an Unscented Kalman Filter (UKF) is derived. The integrated sensor suite comprises the gyro sensors on each spacecraft and a vision-based navigation system on the slave spacecraft. In the traditional algorithm, an assumption that the master's body frame coincides with its Local Vertical Local Horizontal (LVLH) frame is made to construct the line-of-sight observations for convenience. To solve this problem, two relative quaternions that map the master's LVLH frame to the slave and master body frames are involved. The general relative equations of motion for eccentric orbits are used to describe the positional dynamics. The implementation equations for the UKF are derived. A modified version of the UKF is presented based on the averaging-quaternion algorithm. Simulation results indicate that the proposed filters provide more accurate estimates of relative attitude and position than the Extended Kalman Filter (EKF).

This article proposes a novel method to autonomously determine the relative orbits of a small spacecraft formation flying with a slowly rotating near-Earth asteroid for the asteroid mitigation mission. A combined model of spacecraft-to-asteroid measurements with inter-spacecraft communication is built up. The dynamic model accounting for the perturbations is derived to describe the state variables of the Unscented Kalman Filter (UKF). The Lyapunov function approach is used to design a control law to maintain the orbits of the spacecraft formation. Four navigation scenarios are presented based on the different combinations of multiple measurements: two of them are non-collaborative scenarios which only include the spacecraft-to-asteroid measurements in the measurement models, while the other two are collaborative scenarios with the inter-spacecraft communication added into the measurement models. Simulation results are analysed and compared with each other to show the improvements of the filter performance in the aspects of the convergence speed and the accuracy of the estimator.

This paper presents a module for pedestrian detection from a moving vehicle in low-light conditions. The algorithm make use of a single far infrared camera based on a microbolometer. Images of the area ahead of the vehicle are analyzed to determine if any pedestrian might be in its trajectory. Detection is achieved by searching for distributions of temperatures in the scene similar to that of the human body. Those areas with an appropriate temperature, size, and position in the image are classified, by means of a correlation between them and some probabilistic models, which represents the average temperature of the different parts of the human body. Finally, those pedestrians found are tracked in a subsequent step, using an unscented Kalman filter. This final stage of the algorithm enables the algorithm to predict the trajectory of the pedestrian, in a way that does not depend on the movement of the camera. The aim of this system is to warn the vehicle's driver and reduce the reaction time in case an emergency break is necessary.

In this paper we assume that we have measurements of a first difference of a typical satellite navigation carrier phase differential, which is a homogeneous quadratic function of the components of the attitude quaternion. We illustrate the determination of large or sustained attitudes using a dedicated unscented Kalman filter. The unscented Kalman filter structure was chosen for the dedicated filter because of its derivative free nature and other advantages. When a Gaussian distributed vector random variable is transformed to an equivalent quaternion it does not continue to be Gaussian distributed. For this reason, a new predictor-corrector form of the unscented Kalman filter is proposed to maintain the normalization of the unscented mean quaternion estimate in the presence of additive disturbances. The results from our realistic simulations indicate that the large attitude of a spacecraft can be estimated to within 0·1% accuracy over large time frames. The filter is particularly useful for autonomous operations of spacecraft as well as in other applications where the process model is bilinear or second order in the states.

Efficient and precise geolocation can be achieved by integrating a ranging system, such as GPS, with inertial sensors in order to bridge short outages, enhance accuracy degradation, and increase the temporal resolution in the ranging system. Optimal integration depends on appropriate filter methods that can accommodate the particular short-term dynamics experienced by platforms, such as UXO ground-based detection systems. The traditional extended Kalman filter was designed to integrate data from a linearized system excited by Gaussian noise. We compared this filter to modern filters that obviate these prerequisites, including the unscented Kalman filter, the particle filter, and adaptive variations thereof, using simulated IMU/ranging systems that follow a typical trajectory with both straight and curved segments. The unscented filter performed significantly better than the extended Kalman filter, particularly over the curved segments, yielding up to 50% improvement in the position accuracy using medium-grade inertial measurement units. Similar improvement was obtained for the unscented particle filter, and its adaptive variant, over the unscented Kalman filter (which performed comparably to the extended Kalman filter) when the statistical distribution of the IMU noise was non-symmetric (i.e., essentially non-Gaussian). While the few-centimetre geolocation accuracy goal for highly dynamic UXO characterization applications remains a challenge if tactical grade IMUs are integrated with a significantly degraded ranging system, using filters appropriate to the inherent nonlinear dynamics and potential non-Gaussian nature of the sensor noise tend to reduce overall errors compared to the traditional filter.

A navigation integration processing scheme, called the strong tracking unscented Kalman filter (STUKF), is based on the combination of an unscented Kalman filter (UKF) and a strong tracking filter (STF). The UKF employs a set of sigma points by deterministic sampling, such that the linearization process is not necessary, and therefore the error caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. As a type of adaptive filter, the STF is essentially a nonlinear smoother algorithm that employs suboptimal multiple fading factors, in which the softening factors are involved. In order to resolve the shortcoming in traditional approach for selecting the softening factor through personal experience or computer simulation, a novel scheme called the fuzzy strong tracking unscented Kalman filter (FSTUKF) is presented where the Fuzzy Logic Adaptive System (FLAS) is incorporated for determining the softening factor. The proposed FSTUKF algorithm shows promising results in estimation accuracy when applied to the integrated navigation system design, as compared to the EKF, UKF and STUKF approaches.