A Fusion Location Algorithm Based on Federated Kalman Filtering

MATLAB-based general approach for square-root extended-unscented and fifth-degree cubature Kalman filtering methods

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.

Positioning systems play an important role in industrial and scientific applications, making their performance improvements and functional expansions crucial. Positioning systems generally consist of mechanisms, actuators, controllers, and sensors, with performances and functions depending on their integration. Accordingly, positioning technologies focus on these elements and their integration into the system. Actuators are fundamental and critical elements that characterize positioning systems. Electromagnetic actuators are the most widely used because of their high basic performance, and piezoelectric actuators are often used for fine movement. In addition, research on new actuators with unique characteristics is actively being pursued. In component integration, control technology plays a crucial role in effectively utilizing the characteristics of elements and addressing their drawbacks. With the recent advances in digital technology facilitating nonlinear representations and structural modifications, various control methods have been proposed and evaluated to benefit from these advances. In addition to proposing modeling methods, the effects of manufacturing processes on system performance have been evaluated. This special issue contains 18 papers covering the design and characteristics of mechanisms and actuators, control technology, system design, and modeling, along with three review papers on positioning technology and its applications. We would like to express our sincere gratitude to the authors for their excellent contributions to the special issue. We also thank the reviewers for their incisive efforts in editing this special issue. We hope that this special issue will help readers enhance their knowledge and understanding of positioning technology and its applications, thereby contributing to its progress.

Because of the environment and the nature of the signal itself, GPS or wireless mobile positioning technology always has any unavoidable defect. But higher coverage percentage, reliability or precision than single positioning can be gained if integrating at least two positioning technologies' advantages and avoiding their deficiencies. So the developmental trend of mobile positioning technology is hybrid positioning and lots of research has been made. Data fusion is one of the main methods in hybrid positioning field. This paper gives an effective high-precision hybrid positioning model with GPS and wireless mobile positioning based on least squares method and Kalman filtering. Single GPS positioning based on carrier phase is also one of the highest precision methods, but current difficulty is the fast integer ambiguity resolution. This paper gives an algorithm based Z transformation to speed up the ambiguity resolution. By test and verification, this algorithm has a higher positioning accuracy than commonly pseudo-range differential positioning.

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.

This paper proposed a cost function of features based mode switching algorithm for indoor positioning to solve the problem of choosing the best positioning mode in the overlapping area. The experiments show that when the precisions of the alternative location modes are of no big difference, using this algorithm is much more reasonable. Along with the growth of urban scale, the probability of people's appearance in the commercial buildings, supermarkets, exhibition halls or gymnasiums and other large -scale buildings are higher. Whether doing business, buying food, visiting the exhibition or watching sports game, people need to know all kinds of location information. Location-based services (LBS) have been considered the second killer business, after the service of short messages. The main methods of current positioning in interior environment are WLAN, WSN, RFID, infrared, ultrasonic and UWB, etc.. Each interior location technology in positioning accuracy, cost, facilities and equipments are so varied, that is, each positioning technology has its own scope of application. Only in its scope of application, it is the ideal way. There is no location technology that is applicable for every situation. With more and more large-scale constructions been built, like the large theaters, stadiums, exhibition centers, a single positioning technology obviously can not satisfy the actual demands in such complex constructions. There is an inevitable need for the comprehensive utilization of a wide range of positioning technology. Thus, according to the actual positioning demands of large-scale buildings, we made a research on how to make effective use of a variety of positioning techniques and how to integrate these indoor location technologies. This article discussed positioning mode selection problem of hybrid positioning technology by WLAN and WSN in overlapping range in interior environment and proposed a cost function of features based mode switching algorithm for indoor positioning

Due to the rapid development of precision manufacturing technology, much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities in terms of measurement accuracy and information richness, thereby improving the efficiency and precision of manufacturing. In a multisensor system, each sensor independently measures certain parameters. Then, the system uses a relevant signal-processing algorithm to combine all of the independent measurements into a comprehensive set of measurement results. The purpose of this paper is to describe multisensor measurement and data fusion technology and its applications in precision monitoring systems. The architecture of multisensor measurement systems is reviewed, and some implementations in manufacturing systems are presented. In addition to the multisensor measurement system, related data fusion methods and algorithms are summarized. Further perspectives on multisensor monitoring and data fusion technology are included at the end of this paper.

Stochastic differential equations (SDE) are used as dynamical models for cross‐sectional discrete time measurements (panel data). Thus causal effects are formulated on a fundamental infinitesimal time scale. Cumulated causal effects over the measurement interval can be expressed in terms of fundamental effects which are independent of the chosen sampling intervals (e.g. weekly, monthly, annually). The nonlinear continuous–discrete filter is the key tool in deriving a recursive sequence of time and measurement updates. Several approximation methods including the extended Kalman filter (EKF), higher order nonlinear filters (HNF), the local linearization filter (LLF), the unscented Kalman filter (UKF), the Gauss–Hermite filter (GHF) and generalizations (GGHF), as well as simulated filters (functional integral filter FIF) are compared.

Aiming at solving the navigation and obstacle avoidance of the unmanned vehicle,the multi sensor data fusion technology and unmanned vehicle obstacle avoidance navigation algorithm were studied profoundly. According to the requirements of the application of unmanned vehicle navigation and obstacle avoidance system, multisensor data fusion technology was applied to unmanned vehicle navigation and obstacle avoidance control system. In addition, A*VFF navigation and obstacle avoidance algorithm based on fuzzy neural network was improved. Finally, through the construction of the simulation platform, simulation experiment of the unmanned vehicle obstacle avoidance navigation was completed, and a better route was planned for unmanned vehicle in a more complex environment. The results showed that it realized the autonomous navigation of unmanned vehicle and obstacle avoidance function. Based on the above findings, it is concluded that the application of artificial intelligence detection system has good performance.

In this day and age, the necessities for route and situating exactness are getting ever more elevated. The global positioning and navigation system (GPS) can give high-accuracy and long haul route and situating data. But it largely depends on the external environment and is susceptible to environmental disturbances. As a result, the number of visible stars is insufficient, and even placement fails. The research on node positioning technology is of great significance to the research of wireless sensor networks, and node positioning technology is one of the important technologies in wireless sensor networks. Therefore, this paper will introduce the relevant algorithms and technologies of positioning in detail. The purpose of this text is to research how to analyze and research based on Internet of things (IoT) satellite navigation and positioning technology. And the wireless sensor network is described. The two simulation results have showed that, with the positioning technology proposed in this paper, the average positioning error of the anchor node can be kept a small constant regardless of the conditions of different packet sending intervals, changing moving rates, or increasing the transmission power and changing distance. The average positioning error provided in this paper has been kept at about 0.80 m, and the positioning accuracy is high, which is naturally newer and better than the Ssu positioning technology.

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.

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.

Flying vehicle’s navigation, direction, and control in real‐time results in the design of a strap‐down inertial navigation system (INS). The strategy results in low accuracy, performance with correctness. Aiming at the attitude estimation problem, many data fusion or filtering methods had been applied, which fail in many cases, which attains the nonlinear measurement model, process dynamics, and high navigation range. The main problem in unmanned aerial vehicles (UAVs) and flying vehicles is the determination of attitude angles. A novel attitude estimation algorithm is proposed in this study for the unmanned aerial vehicle (UAV). This research article designs two filtering algorithms for fixed‐wing UAVs which are nonlinear for the attitude estimation. The filters are based on Kalman filters. The unscented Kalman filter (UKF) and cubature Kalman filter (CKF) were designed with different parameterizations of attitude, i.e., Euler angle (EA) and INS/unit quaternion (UQ) simultaneously. These filters, EA‐UKF and INS‐CKF, use the nonlinear process and measurement model. The computational results show that among both filters, the CKF attains a high accuracy, robustness, and estimation for the attitude estimation of the fixed‐wing UAV.

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.