Multisensor unscented filtering for GPS-based navigation systems

Accurate localization in land vehicle navigation systems is highly dependent on the global navigation satellite system (GNSS). However, GNSS signal outages are common in urban areas due to obstacles such as tall buildings and tunnels. To mitigate these issues, digital road maps and dead reckoning sensors, like odometers, are often integrated to provide continuous vehicle localization. This paper presents a robust estimation method to solve the fusion problem of GNSS, odometer, and digital road map measurements in the presence of GNSS out- ages. The proposed solution utilizes a marginalized particle filter (MPF), which combines the robustness of particle filtering with the efficiency of a Kalman filter to handle the linear and non-linear parts of the state and/or measurement equations, respectively. When GNSS signals are unavailable, the MPF fuses all available pseudo-range data with odometric and map information to enhance vehicle positioning. The effectiveness of the proposed method is demonstrated using real-world data in an urban transportation scenario, highlighting signifi- cant performance improvements and real-time application potential.

The Japan Digital Road Map Association (JDRMA) was founded to establish the standards for digital road map (DRM) and to produce a DRM database in Japan. The JDRMA is now in the process of refining the database using 1/25,000 scale topographical maps. The JDRMA supplies users with an updated version of the database every year. Car navigation is currently the largest application of DRM. Police departments, fire departments, ambulance services, and taxi companies use location systems that use DRM. Significant application of DRM is expected to come in the future in the planning and research fields. Road sign checking system, traffic accident analysis system, road census reference system and logistics system are now being studied. As the applications expand, more advanced DRMs must be made available, To meet this requirement, development of more detailed and precise DRMs and linkage to dynamic information systems is being pursued.

PurposeAutonomous orbit determination using geomagnetic measurements is an important backup technique for safe spacecraft navigation with a mere magnetometer. The geomagnetic model is used for the state estimation of orbit elements, but this model is highly nonlinear. Therefore, many efforts have been paid to developing nonlinear filters based on extended Kalman filter (EKF) and unscented Kalman filter (UKF). This paper aims to analyze whether to use UKF or EKF in solving the geomagnetic orbit determination problem and try to give a general conclusion.Design/methodology/approachThis paper revisits the problem and from both the theoretical and engineering results, the authors show that the EKF and UKF show identical estimation performances in the presence of nonlinearity in the geomagnetic model.FindingsWhile EKF consumes less computational time, the UKF is computationally inefficient but owns better accuracy for most nonlinear models. It is also noted that some other navigation techniques are also very similar with the geomagnetic orbit determination.Practical implicationsThe intrinsic reason of such equivalence is because of the orthogonality of the spherical harmonics which has not been discovered in previous studies. Thus, the applicability of the presented findings are not limited only to the major problem in this paper but can be extended to all those schemes with spherical harmonic models.Originality/valueThe results of this paper provide a fact that there is no need to choose UKF as a preferred candidate in orbit determination. As UKF achieves almost the same accuracy as that of EKF, its loss in computational efficiency will be a significant obstacle in real-time implementation.

In underwater navigation, the global positioning system is unavailable; hence, other solutions must be pursued in the development of navigation systems. In this paper, filters for the position, linear velocity, and acceleration of underwater vehicles are derived by combining a long baseline acoustic positioning system with an inertial navigation system. A dynamic model is devised via state augmentation, including the bias of the pseudo-ranges, which accounts for the effect of the unknown offset between the emitters’ and receivers’ clocks. With this technique, the resulting dynamics are linear, in spite of the original nonlinear nature of the problem. The proposed solution, which includes the explicit dynamic estimation of the bias of the pseudo-ranges, is a linear Kalman filter. The observability of the system is assessed, which allows to establish globally exponentially stable error dynamics. The performance of the solution is evaluated with realistic simulation results, considering sensor noise and discrete-time measurements. Finally, the comparison with the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and the Bayesian Cramer–Rao bound is presented, including Monte Carlo simulations. This comparison shows the goodness of the proposed solution, which converges for all initial conditions and exhibits performance comparable to the EKF and the UKF, whereas the EKF and UKF are shown to lack global convergence.

The stellar refraction navigation is a type of autonomous celestial navigation method for satellites that uses high-accuracy star sensors to obtain the stellar refraction. The apparent height and the stellar refraction angle are two kinds of measurements for the satellite stellar refraction navigation system. A better navigation performance can be achieved by the stellar refraction angle rather than the refraction apparent height. However, the measurement model of the stellar refraction angle is an implicit function, which needs an implicit filter method. A new implicit unscented Kalman filter (UKF) method is proposed and applied to the satellite stellar refraction navigation system in this paper. The main difference between the classical UKF and the implicit UKF lies in the measurement update. First, since the implicit measurement model can be rearranged into an equation whose one side is zero, the vector of zeros is regarded as the equivalent measurement, and the difference between the predicted measurement and its true value is utilized for updating the predicted state and its covariance matrix. Second, the measurement noise and its covariance matrix are transformed based on the unscented transformation principle. In addition, the implicit UKF is compared with the implicit extended Kalman filter (IEKF), the iterative IEKF, and the UKF. Simulations show that the proposed implicit UKF method can obtain about 19.0% and 17.5% improvements in three-dimensional root mean square position estimation compared to the IEKF and the iterative IEKF, respectively. Although the navigation performance of the UKF is close to the implicit UKFs, the computational time of the implicit UKF is much shorter than the UKFs. In addition, the sensitivities of the four different Kalman filters to the star sensor's faults and the atmosphere perturbation are compared, respectively.

Non-linearity in the underwater robotics system has a significant influence on navigation. The most commonly used filtering algorithm is an Extended Kalman filter or Indirect Kalman filter, which suffers from non-linear system dynamics and non-linearity in sensor measurements. In addition, the Unscented Kalman filter and the Particle filter estimation accuracy depend on particles and sigma points which contribute to high computation load. The processing load has a direct relationship with power consumption which is undesirable for long missions. The work in this paper proposes an improvement in underwater navigation and positioning of vehicles by a Multilayer perceptron neural network trained with Backpropagation. The proposed method utilizes the learning capability of a neural network and augments the noise handling ability of Kalman filtering for the state estimation which produces a new and improved underwater localization algorithm. Furthermore, the algorithm uses knowledge of state-space for neural network recursive learning that improves the overall response significantly. The algorithm is named BIKF, which has better accuracy and reliability than the Indirect Kalman filter for the prediction of underwater state variables.

Traffic state estimation problems must deal with imperfect information. Imperfect information may occur as a result of misalignments between reality and the assumptions or simplifications made when developing the models as well as incomplete or corrupted data. One of the well-known methods of handling imperfect information is the Kalman filter algorithm and its variations, such as the extended Kalman filter and the unscented Kalman filter. While the standard Kalman filter and the extended Kalman filter have been widely applied to the state estimation of linear systems, the unscented Kalman filter has been reported the better choice in the multiclass traffic state estimation. In many applications of Kalman filters to traffic estimation problems, the model and measurement noise covariance matrices are normally estimated. When there is a mismatch between the true and the assumed noise distribution, however, the filter often suffers from performance degradation and even divergence in certain situations. To this end, this paper presents a more efficient and accurate algorithm embedded in the unscented Kalman filter to simultaneously estimate the traffic state and the model noise distribution statistics. The proposed method is facilitated through a simultaneous update of the model noise covariance matrix in the predicted covariance equations of the unscented Kalman filter algorithm. It is found through simulation that the proposed algorithm may improve the model performance over the standard algorithm.

In this paper we use four nonlinear blending filters in order to integrate global positioning system and strapdown inertial navigation system (GPS/SDINS). As we will see in this paper, the Unscented Kalman filter (UKF) in comparison with extended Kalman filter (EKF), central difference Kalman filter (CDKF) and particle filters (PFs) has the best performance both in estimation accuracy and computation time. It will also give us more reliable solution when encountering satellite signal blockage as a probable problem in land navigation. The results have clearly demonstrated that the UKF would improve the guidance from the point of accuracy and computation time to the mentioned problems.

Fast non-intrusive estimation of liquid-phase interface and boundary heat flux in participating media by the Kalman filtering methods

State of Charge (SOC) estimation plays a crucial role in managing the performance and longevity of electric vehicle (EV) batteries. Various estimation techniques, including various Kalman Filters (KF), have been employed for this purpose. The mathematical process and limits of different KF family algorithms are analysed. In this study, an impact of nonlinear KF parameters on SOC estimation accuracy for EV batteries is analyzed. Specifically, the performance of Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF) is examined. EKF is most preferred for SOC estimation whereas introducing sigma points in UKF is the basic difference between EKF and UKF. In comparison to EKF and UKF, PF is more flexible since it applies to nonlinear systems with non-Gaussian distributions. Utilising a sequence of noisy measurements, KF is a recursive method that approximates the state of a dynamic system. It requires accurate parameters such as process noise covariance (Q) and measurement noise covariance (R) to work effectively. In this work, the effect of these parameters on state of charge estimation using nonlinear KFs has been analysed under different settings of noise covariance matrices. Tuning these parameters is essential for optimizing the performance of the KF and ensuring accurate and reliable state estimation in real-world applications. To find parameter values that provide the best balance between accuracy, robustness, and computational efficiency, the tuning process involves experimentation, simulation, and validation using real-world data. According to the results observed, the filter parameters such as three diagonal elements of Q are selected as 2e-8, 0.005, 0.003 and R as 2e-6. By evaluating the state estimation accuracy under different driving scenarios, robustness and sensitivity of filtering technique in estimating SOC for EV batteries has been studied. This analysis contributes to the optimization of SOC estimation algorithms, enhancing the reliability and efficiency of EV battery management systems.

This paper presents the efficient Unscented Kalman filter (UKF) based estimation and control of a 3-DOF Omni bundle robot. The angular trajectory tracking becomes more difficult under highly non-linear measurement noise and process noise. A rapidly converging Unscented Kalman Filter (UKF) is presented in the paper. The extended Kalman filter (EKF) provides less accurate results in the highly non-linear system and sometime may become unstable because of convergence related issues, whereas in UKF the results in the non-linear system are better than EKF. UKF is more accurate in state and noise estimation. The computed torque technique is taken as a model to apply UKF. After, estimation the noise is removed. The experiment is conducted on Omni bundle with process and measurement noise. The results prove the superiority of presented UKF over conventional UKF and EKF.

Nowadays, experimental techniques allow scientists to have access to large amounts of data. In order to obtain reliable information from the complex systems that produce these data, appropriate analysis tools are needed. The Kalman filter is a frequently used technique to infer, assuming a model of the system, the parameters of the model from uncertain observations. A well-known implementation of the Kalman filter, the unscented Kalman filter (UKF), was recently shown to be able to infer the connectivity of a set of coupled chaotic oscillators. In this work, we test whether the UKF can also reconstruct the connectivity of small groups of coupled neurons when their links are either electrical or chemical synapses. In particular, we consider Izhikevich neurons and aim to infer which neurons influence each other, considering simulated spike trains as the experimental observations used by the UKF. First, we verify that the UKF can recover the parameters of a single neuron, even when the parameters vary in time. Second, we analyze small neural ensembles and demonstrate that the UKF allows inferring the connectivity between the neurons, even for heterogeneous, directed, and temporally evolving networks. Our results show that time-dependent parameter and coupling estimation is possible in this nonlinearly coupled system.

Unscented Kalman Filter (UKF) has been proved to be a superior alternative to the extended Kalman filter (EKF) when solving the nonlinear system in recent years. In order to improve the real-time of the UKF, A new kind of UKF called Sequence UKF is proposed in this paper. Like Rao-Blackwellised Unscented Kalman Filter (RBUKF) [4], it also deals with nonlinear stochastic discrete-time system with linear measurement equation, however it can decrease the computational complexity with the same filtering accuracy. This algorithm reduces the measurement vector to scalars in measurement-update of UKF by sequence method, so it can avoid inversing the covariance of measurement and reduce a great mount of computation bound. Special algorithm is deduced in this paper. The high performance of sequence UKF is verified by using Monte Carlo simulations.

The prediction of the human motion plays an important role in the predictive motion controller of human-assisting mobile robot, and precise prediction requires a precise motion model. In this paper, a novel human motion prediction method is proposed. This prediction method is based on a hybrid motion model which combines the inverted pendulum model and constant velocity model. Derivations of the kinematics, especially the inverted pendulum model, are presented in this paper. Estimation of the prediction by inverted pendulum and constant velocity models based on the Unscented kalman filter(UKF) and Kalman filter(KF) respectively, are also given. The hybrid model precisely describes the walking cycle. Experiments verifying the effectiveness of the proposed model are also presented.

Continuous tracking and detecting position systems are significant services that can using in many applications such as detecting accident cars on Freeways. This paper introduces Artificial intelligent (AI) method to quick discover the accident and determine the position of car on Freeways that will reduce the rate of fatalities. Most tracking and detecting position systems are based on Global Navigation Satellite System (GNSS). But the GNSS signal in certain areas such as buildings and tunnel is unavailable which make a big problem for these systems. To solve this problem, most traditional methods are based on integrated GNSS with other navigation sensors such as accelerometers, gyroscopes, odometers and so on. But these integrated methods still high cost and need to long time processing. Today, most modern Vehicles are equipping with several On-board Car Motion Sensors (OVMS) such as Acceleration Sensors (AS), Wheel Velocity Sensor (WVS), Gravity Sensor (GS), Yaw Rate Sensor (YRS) and additional to GNSS that can improve the safety and also use to determine and detect the position of accident vehicle. This paper develops an approach AI-accident detection system based on integrating GNSS with On-board Vehicle Motion Sensors (OVMS) using Extended Kalman Filter (EKF) algorithm. During GPS outages, the three Acceleration Sensors (3-ASs) are processing to determine velocities and then position of vehicle on three x, y and z axes. While the YRS and GS sensors are using to determine velocities angles on three x, y and z axes, respectively. In same time the three WVS sensors are using to correct velocities errors of three acceleration sensors (3-ASs) until GNSS signal is return again. The proposed AI-accident detection system is tested in different scenarios during GPS outage signal. The results of proposed method can actually detect accident and determine the position of vehicle with high efficiency.