Showing papers on "Kalman filter published in 2018"
TL;DR: Simulation results for a target tracking example illustrate that the proposed VBAKF has better robustness to resist the uncertainties of process and measurement noise covariance matrices than existing state-of-the-art filters.
Abstract: In this paper, a novel variational Bayesian (VB)-based adaptive Kalman filter (VBAKF) for linear Gaussian state-space models with inaccurate process and measurement noise covariance matrices is proposed. By choosing inverse Wishart priors, the state together with the predicted error and measurement noise covariance matrices are inferred based on the VB approach. Simulation results for a target tracking example illustrate that the proposed VBAKF has better robustness to resist the uncertainties of process and measurement noise covariance matrices than existing state-of-the-art filters.
388 citations
15 Jan 2018
TL;DR: Kumar et al. as mentioned in this paper presented a filter-based stereo visual inertial odometry that uses the multistate constraint Kalman filter, which is comparable to state-of-the-art monocular solutions in terms of computational cost.
Abstract: In recent years, vision-aided inertial odometry for state estimation has matured significantly. However, we still encounter challenges in terms of improving the computational efficiency and robustness of the underlying algorithms for applications in autonomous flight with microaerial vehicles, in which it is difficult to use high-quality sensors and powerful processors because of constraints on size and weight. In this letter, we present a filter-based stereo visual inertial odometry that uses the multistate constraint Kalman filter. Previous work on the stereo visual inertial odometry has resulted in solutions that are computationally expensive. We demonstrate that our stereo multistate constraint Kalman filter (S-MSCKF) is comparable to state-of-the-art monocular solutions in terms of computational cost, while providing significantly greater robustness. We evaluate our S-MSCKF algorithm and compare it with state-of-the-art methods including OKVIS, ROVIO, and VINS-MONO on both the EuRoC dataset and our own experimental datasets demonstrating fast autonomous flight with a maximum speed of 17.5 m/s in indoor and outdoor environments. Our implementation of the S-MSCKF is available at https://github.com/KumarRobotics/msckf_vio.
285 citations
TL;DR: Comparison of the predictive schemes leads to the conclusion that the both MPC approaches achieve similar performances, however, the CCS-MPC scheme has a smaller current ripple and is of low computational complexity.
Abstract: This paper presents a comparative study of two predictive speed control schemes for induction machine (IM) in terms of their design and performance. The first control scheme is finite control set-model predictive control (FCS-MPC) with modulation control and the second control scheme is continuous control set-model predictive control (CCS-MPC) with space vector-pulse width modulation. The two schemes adopt the cascaded control approach, which consists of an inner MPC loop for torque control and outer MPC loop for speed control using two individual cost functions. The outer MPC produces the required torque to drive the IM at the reference speed while the reference torque is taken as the input of the inner MPC, which in turn generates control signals for the inverter. The control states of the two MPCs are constrained with the maximum limits of the drive system. The state feedback is achieved with a standard Kalman filter, which estimates the nonmeasured load torque. For a fair comparison, both approaches are applied to the same IM at the same operational circumstances. The control approaches are implemented and validated in an experimental environment using the same sampling frequency on the same test bench (3.7 kW IM drive). The behavior of the control approaches is assessed by applying reference and disturbance steps to the system in different operational modes. Comparison of the predictive schemes leads to the conclusion that the both MPC approaches achieve similar performances. However, the CCS-MPC scheme has a smaller current ripple and is of low computational complexity. The computing duration is not very different for the three tested schemes. CCS-MPC can cope with a less powerful DSP than for FCS.
262 citations
TL;DR: Comparison results indicate that IMMTP could achieve a more accurate prediction trajectory with a long prediction horizon than the existing physics- and maneuver-based approaches.
Abstract: Vehicle trajectory prediction helps automated vehicles and advanced driver-assistance systems have a better understanding of traffic environment and perform tasks such as criticality assessment in advance. In this study, an integrated vehicle trajectory prediction method is proposed by combining physics- and maneuver-based approaches. These two methods were combined for the reason that the physics-based trajectory prediction method could ensure accuracy in the short term with the consideration of vehicle running dynamic parameters, and the maneuver-based prediction approach has a long-term insight into future trajectories with maneuver estimation. In this study, the interactive multiple model trajectory prediction (IMMTP) method is proposed by combining the two predicting models. The probability of each model in the interactive multiple models could recursively adjust according to the predicting variance of each model. In addition, prediction uncertainty is considered by employing unscented Kalman filters in the physics-based prediction model. To the maneuver-based method, random elements for uncertainty are introduced to the trajectory of each maneuver inferred by using the dynamic Bayesian network. The approach is applied and analyzed in the lane-changing scenario by using naturalistic driving data. Comparison results indicate that IMMTP could achieve a more accurate prediction trajectory with a long prediction horizon.
250 citations
TL;DR: In this article, a navigation technology based on Adaptive Kalman Filter with attenuation factor is proposed to restrain noise in order to improve the precision of navigation information, and the accuracy of the integrated navigation can be improved due to the reduction of the influence of environment noise.
191 citations
TL;DR: Experimental results illustrate that the proposed adaptive extended Kalman filter has better localization accuracy than existing state-of-the-art algorithms.
Abstract: To solve the problem of unknown noise covariance matrices inherent in the cooperative localization of autonomous underwater vehicles, a new adaptive extended Kalman filter is proposed. The predicted error covariance matrix and measurement noise covariance matrix are adaptively estimated based on an online expectation-maximization approach. Experimental results illustrate that, under the circumstances that are detailed in the paper, the proposed algorithm has better localization accuracy than existing state-of-the-art algorithms.
184 citations
TL;DR: An improved PF algorithm, that is, the unscented particle filter (UPF) based on linear optimizing combination resampling (U-LOCR-PF) to improve the prediction accuracy, and shows higher accuracy in the RUL prediction of lithium-ion battery, compared with the existing PF-based and UPF-based prognostic methods.
165 citations
TL;DR: This paper solves the problem of “how much power the attacker should use to jam the channel in each time” and proposes an attack power allocation algorithm and shows the computational complexity of the proposed algorithm is not worse than $\mathcal{O}(T)$
Abstract: This paper considers a remote state estimation problem, where a sensor measures the state of a linear discrete-time process and has computational capability to implement a local Kalman filter based on its own measurements. The sensor sends its local estimates to a remote estimator over a communication channel that is exposed to a Denial-of-Service (DoS) attacker. The DoS attacker, subject to limited energy budget, intentionally jams the communication channel by emitting interference noises with the purpose of deteriorating estimation performance. In order to maximize attack effect, following the existing answer to “when to attack the communication channel”, in this paper we manage to solve the problem of “how much power the attacker should use to jam the channel in each time”. For the static attack energy allocation problem, when the system matrix is normal, we derive a sufficient condition for when the maximum number of jamming operations should be used. The associated jamming power is explicitly provided. For a general system case, we propose an attack power allocation algorithm and show the computational complexity of the proposed algorithm is not worse than $\mathcal{O}(T)$ , where $T$ is the length of the time horizon considered. When the attack can receive the real-time ACK information, we formulate a dynamic attack energy allocation problem, and transform it to a Markov Decision Process to find the optimal solution.
149 citations
TL;DR: Online detection of false data injection attacks and denial of service attacks in the smart grid is studied and a novel event-based sampling scheme called level-crossing sampling with hysteresis is proposed that is shown to exhibit significant advantages compared with the conventional uniform-in-time sampling scheme.
Abstract: In this paper, online detection of false data injection attacks and denial of service attacks in the smart grid is studied. The system is modeled as a discrete-time linear dynamic system and state estimation is performed using the Kalman filter. The generalized cumulative sum algorithm is employed for quickest detection of the cyber-attacks. Detectors are proposed in both centralized and distributed settings. The proposed detectors are robust to time-varying states, attacks, and set of attacked meters. Online estimates of the unknown attack variables are provided, that can be crucial for a quick system recovery. In the distributed setting, due to bandwidth constraints, local centers can only transmit quantized messages to the global center, and a novel event-based sampling scheme called level-crossing sampling with hysteresis is proposed that is shown to exhibit significant advantages compared with the conventional uniform-in-time sampling scheme. Moreover, a distributed dynamic state estimator is proposed based on information filters. Numerical examples illustrate the fast and accurate response of the proposed detectors in detecting both structured and random attacks and their advantages over existing methods.
147 citations
TL;DR: In this article, a detailed assessment of optimization-driven moving horizon estimation (MHE) framework by means of a reduced electrochemical model is conducted for state-of-charge estimation, the standard MHE and two variants in the framework are examined by a comprehensive consideration of accuracy, computational intensity, effect of horizon size, and fault tolerance.
Abstract: Efficient battery condition monitoring is of particular importance in large-scale, high-performance, and safety-critical mechatronic systems, e.g., electrified vehicles and smart grid. This paper pursues a detailed assessment of optimization-driven moving horizon estimation (MHE) framework by means of a reduced electrochemical model. For state-of-charge estimation, the standard MHE and two variants in the framework are examined by a comprehensive consideration of accuracy, computational intensity, effect of horizon size, and fault tolerance. A comparison with common extended Kalman filtering and unscented Kalman filtering is also carried out. Then, the feasibility and performance are demonstrated for accessing internal battery states unavailable in equivalent circuit models, such as solid-phase surface concentration and electrolyte concentration. Ultimately, a multiscale MHE-type scheme is created for State-of-Health estimation. This study is the first known systematic investigation of MHE-type estimators applied to battery management.
147 citations
TL;DR: This review aims to provide an accessible introduction to the methodology of invariant Kalman filtering and to allow readers to gain insight into the relevance of the method as well as its important differences with the conventional EKF.
Abstract: The Kalman filter—or, more precisely, the extended Kalman filter (EKF)—is a fundamental engineering tool that is pervasively used in control and robotics and for various estimation tasks in autonomous systems. The recently developed field of invariant extended Kalman filtering uses the geometric structure of the state space and the dynamics to improve the EKF, notably in terms of mathematical guarantees. The methodology essentially applies in the fields of localization, navigation, and simultaneous localization and mapping (SLAM). Although it was created only recently, its remarkable robustness properties have already motivated a real industrial implementation in the aerospace field. This review aims to provide an accessible introduction to the methodology of invariant Kalman filtering and to allow readers to gain insight into the relevance of the method as well as its important differences with the conventional EKF. This should be of interest to readers intrigued by the practical application of mathemati...
TL;DR: A computationally-efficient receiver, which uses a phase-locked loop (PLL)-aided delay- Locked loop (DLL) to track the received LTE signals is presented, demonstrating robust multipath mitigation for high transmission LTE bandwidths.
Abstract: Mitigating multipath of cellular long-term evolution (LTE) signals for robust positioning in urban environments is considered. A computationally efficient receiver, which uses a phase-locked loop (PLL)–aided delay-locked loop (DLL) to track the received LTE signals, is presented. The PLL-aided DLL uses orthogonal frequency division-multiplexing (OFDM)–based discriminator functions to estimate and track the time-of-arrival. The code phase and carrier phase performances in an additive white Gaussian noise (AWGN) channel are evaluated numerically. The effects of multipath on the code phase and carrier phase are analyzed, demonstrating robust multipath mitigation for high transmission LTE bandwidths. The average of the DLL discriminator functions over multiple LTE symbols is presented to reduce the pseudorange error. The proposed receiver is evaluated on a ground vehicle in an urban environment. Experimental results show a root mean square error (RMSE) of 3.17 m, a standard deviation of 1.06 m, and a maximum error of 6.58 m between the proposed LTE receiver and the GPS navigation solution over a 1.44 km trajectory. The accuracy of the obtained pseudoranges with the proposed receiver is compared against two algorithms: estimation of signal parameters by rotational invariance techniques (ESPRIT) and EKAT (ESPRIT and Kalman filter).
TL;DR: Sufficient conditions for the stochastic boundedness of the Kalman-consensus filter are established and it is shown that the filtering performance is directly influenced by the network connectivity and the collective observability.
Abstract: This paper is concerned with the distributed state estimation problem over wireless sensor networks. The communication links are unreliable that are subject to random link failures modeled as a set of independent Bernoulli processes. To estimate the plant state collaboratively, a Kalman-consensus filtering approach is developed where the sensors spread the local information obtained from the Kalman filtering algorithm by performing a consensus of the inverse covariance matrices at each time instant. Sufficient conditions for the stochastic boundedness of the Kalman-consensus filter are established. It is shown that the filtering performance is directly influenced by the network connectivity and the collective observability. A numerical example is illustrated to verify the proposed results.
TL;DR: It is proved how the filter guarantees stability (mean-square boundedness of the estimation error in each node) under network connectivity and system collective observability and practical effectiveness of the distributed filter for trading off estimation performance versus transmission rate is demonstrated.
05 Nov 2018
TL;DR: This chapter aims for Those who need to teach Kalman filters to others, or for those who do not have a strong background in estimation theory, to help readers easily grasp how the Kalman filter work.
Abstract: We provide a tutorial-like description of Kalman filter and extended Kalman filter. This chapter aims for those who need to teach Kalman filters to others, or for those who do not have a strong background in estimation theory. Following a problem definition of state estimation, filtering algorithms will be presented with supporting examples to help readers easily grasp how the Kalman filters work. Implementations on INS/GNSS navigation, target tracking, and terrain-referenced navigation (TRN) are given. In each example, we discuss how to choose, implement, tune, and modify the algorithms for real world practices. Source codes for implementing the examples are also provided. In conclusion, this chapter will become a prerequisite for other contents in the book.
TL;DR: This work investigates the DeKF performance from a high-level perspective, involving different load dynamics and SOH stages, and shows that the DEKF partly improves the accuracy of the SOC estimation compared to the simple EKF over battery lifetime within the operational limits of an automotive application.
Abstract: One of the most discussed topics in battery research is the state-of-charge (SOC) and state-of-health (SOH) determination of traction batteries. Unfortunately, neither is directly measurable and both must be derived from sensor signals using model-based algorithms. These signals can be noisy and erroneous, leading to an inaccurate estimate and, hence, to a limitation of usable battery capacity. A popular approach tackling these difficulties is the dual extended Kalman filter (DEKF). It consists of two extended Kalman filters (EKFs), that synchronously estimate both the battery states and parameters. An analysis of the reliability of the DEKF estimation against realistically fading battery parameters is still a widely discussed subject. This work investigates the DEKF performance from a high-level perspective, involving different load dynamics and SOH stages. A numerical optimization-based approach for the crucial filter parameterization is employed. We show that the DEKF partly improves the accuracy of the SOC estimation compared to the simple EKF over battery lifetime within the operational limits of an automotive application. However, capacity and internal resistance estimation becomes unreliable and partly diverges from the reference under constant and realistic load scenarios coupled with advanced degradation. As a consequence, a downstream use of both parameters in a SOC or SOH estimation is hampered over the battery lifetime. Extensions are needed to improve reliability and enable employment in real-world applications.
TL;DR: A refined strong tracking unscented Kalman filter (RSTUKF) is developed to enhance the UKF robustness against kinematic model error and maintains the optimal UKF estimation in the absence of kinematics model error.
TL;DR: A model-free method based on an adaptive Kalman filter is developed to accomplish path tracking for a continuum robot using only pressures and tip position, which shows good robustness against the system uncertainty and external disturbance, and lowers the number of sensors.
Abstract: Continuum robots with structural compliance have promising potential to operate in unstructured environments. However, this structural compliance brings challenges to the controller design due to the existence of considerable uncertainties in the robot and its kinematic model. Typically, a large number of sensors are required to provide the controller the state variables of the robot, including the length of each actuator and position of the robot tip. In this paper, a model-free method based on an adaptive Kalman filter is developed to accomplish path tracking for a continuum robot using only pressures and tip position. As the Kalman filter operates only with a two-step algebraic calculation in every control interval, the low computational load and real-time control capability are guaranteed. By adding an optimal vector to the control law, buckling of the robot can also be avoided. Through simulation analysis and experimental validation, this control method shows good robustness against the system uncertainty and external disturbance, and lowers the number of sensors.
TL;DR: This study presents a combined parameter and state estimation algorithm for a bilinear system described by its observer canonical state-space model based on the hierarchical identification principle to reduce the computation burden and improve the parameter tracking capability.
Abstract: This study presents a combined parameter and state estimation algorithm for a bilinear system described by its observer canonical state-space model based on the hierarchical identification principle. The Kalman filter is known as the best state filter for linear systems, but not applicable for bilinear systems. Thus, a bilinear state observer (BSO) is designed to give the state estimates using the extremum principle. Then a BSO-based recursive least squares (BSO-RLS) algorithm is developed. For comparison with the BSO-RLS algorithm, by dividing the system into three fictitious subsystems on the basis of the decomposition–coordination principle, a BSO-based hierarchical least squares algorithm is proposed to reduce the computation burden. Moreover, a BSO-based forgetting factor recursive least squares algorithm is presented to improve the parameter tracking capability. Finally, a numerical example illustrates the effectiveness of the proposed algorithms.
TL;DR: An interactive estimation algorithm for unmeasurable states and parameters based on the hierarchical identification principle for bilinear systems with measurement noise in the form of the moving average model is presented.
Abstract: This paper considers the identification problem of bilinear systems with measurement noise in the form of the moving average model. In particular, we present an interactive estimation algorithm for unmeasurable states and parameters based on the hierarchical identification principle. For unknown states, we formulate a novel bilinear state observer from input-output measurements using the Kalman filter. Then a bilinear state observer based multi-innovation extended stochastic gradient (BSO-MI-ESG) algorithm is proposed to estimate the unknown system parameters. A linear filter is utilized to improve the parameter estimation accuracy and a filtering based BSO-MI-ESG algorithm is presented using the data filtering technique. In the numerical example, we illustrate the effectiveness of the proposed identification methods.
TL;DR: The main contribution of this paper is the comparison of performance between KF, EKF, JSSE, and JSSE-M when they are used on a relatively complex nonlinear system which is extremely dependent on its parameters, namely the quadrotor.
Abstract: In real-time problems, the possibilities of having a precise mathematical model describing the dynamics of the nonlinear system are scarce. Besides, the measurements invariably are tainted with noise which makes the problem of estimating the actual states of the system more difficult. The most common way of solving this issue involves the application of the Kalman Filter (KF) or the Extended Kalman Filter (EKF), for linear and nonlinear systems, respectively; although in both cases, the estimation heavily relies on linear techniques. In a different way, the James-Stein Filter provides a robust approach to estimate linear and nonlinear systems under parametric uncertainties of the mathematical model. In this brief note, a slightly different James-Stein State Estimator (JSSE), named Modified James-Stein State Estimator (JSSE-M), is presented as an alternative to filtering the states of nonlinear systems within a control scheme. The main contribution of this paper is the comparison of performance between KF, EKF, JSSE, and JSSE-M when they are used on a relatively complex nonlinear system which is extremely dependent on its parameters, namely the quadrotor. In this sense, some interesting comparisons focused on both, the effectiveness and processing time are provided.
TL;DR: A systematic introduction to the Bayesian state estimation framework is offered and various Kalman filtering U+0028 KF U-0029 techniques are reviewed, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KFFor nonlinear systems.
Abstract: This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades of research effort. To date, one of the most promising and popular approaches is to view and address the problem from a Bayesian probabilistic perspective, which enables estimation of the unknown state variables by tracking their probabilistic distribution or statistics U+0028 e.g., mean and covariance U+0029 conditioned on a system U+02BC s measurement data. This article offers a systematic introduction to the Bayesian state estimation framework and reviews various Kalman filtering U+0028 KF U+0029 techniques, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KF for nonlinear systems. It also overviews other prominent or emerging Bayesian estimation methods including Gaussian filtering, Gaussian-sum filtering, particle filtering and moving horizon estimation and extends the discussion of state estimation to more complicated problems such as simultaneous state and parameter U+002F input estimation.
TL;DR: In this paper, the exponential smoothing model (ESM), autoregressive integrated moving average model (ARIMA), and nonlinear auto-regressive neural network (NAR) neural network are combined in a state-space model framework to increase the accuracy of forecasting that accounts for problems in accurate diagnosis of linear and non-linear patterns in economic and financial time series such as for crude oil prices.
TL;DR: A robust state estimation algorithm against FDI attack is presented and it is shown that the proposed method is able to detect malicious attack, which is undetectable by traditional bad data detection (BDD) methods.
Abstract: The evolution of traditional energy networks toward smart grids increases security vulnerabilities in the power system infrastructure. State estimation plays an essential role in the efficient and reliable operation of power systems, so its security is a major concern. Coordinated cyber-attacks, including false data injection (FDI) attack, can manipulate smart meters to present serious threats to grid operations. In this paper, a robust state estimation algorithm against FDI attack is presented. As a solution to mitigate such an attack, a new analytical technique is proposed based on the Markov chain theory and Euclidean distance metric. Using historical data of a set of trusted buses, a Markov chain model of the system normal operation is formulated. The estimated states are analyzed by calculating the Euclidean distance from the Markov model. States, which match the lower probability, are considered as attacked states. It is shown that the proposed method is able to detect malicious attack, which is undetectable by traditional bad data detection (BDD) methods. The proposed robust dynamic state estimation algorithm is built on a Kalman filter, and implemented on the massively parallel architecture of graphic processing unit using fine-grained parallel programming techniques. Numerical simulations demonstrate the efficiency and accuracy of the proposed mechanism.
TL;DR: In this paper, a decentralized derivative-free dynamic state estimation method is proposed to address cases when system linearization is cumbersome or impossible, where several inputs such as the excitation voltage are characterized by uncertainty in terms of their status.
Abstract: This paper proposes a decentralized derivative-free dynamic state estimation method in the context of a power system with unknown inputs, to address cases when system linearization is cumbersome or impossible. The suggested algorithm tackles situations when several inputs, such as the excitation voltage, are characterized by uncertainty in terms of their status. The technique engages one generation unit only and its associated measurements, and it remains totally independent of other system wide measurements and parameters, facilitating in this way the applicability of this process on a decentralized basis. The robustness of the method is validated against different contingencies. The impact of parameter errors, process, and measurement noise on the unknown input estimation performance is discussed. This understanding is further supported through detailed studies in a realistic power system model.
08 Sep 2018
TL;DR: This paper model the video artifact reduction task as aKalman filtering procedure and restore decoded frames through a deep Kalman filtering network and builds a recursive filtering scheme based on the Kalman model.
Abstract: When lossy video compression algorithms are applied, compression artifacts often appear in videos, making decoded videos unpleasant for human visual systems. In this paper, we model the video artifact reduction task as a Kalman filtering procedure and restore decoded frames through a deep Kalman filtering network. Different from the existing works using the noisy previous decoded frames as temporal information in the restoration problem, we utilize the less noisy previous restored frame and build a recursive filtering scheme based on the Kalman model. This strategy can provide more accurate and consistent temporal information, which produces higher quality restoration results. In addition, the strong prior information of prediction residual is also exploited for restoration through a well designed neural network. These two components are combined under the Kalman framework and optimized through the deep Kalman filtering network. Our approach can well bridge the gap between the model-based methods and learning-based methods by integrating the recursive nature of the Kalman model and highly non-linear transformation ability of deep neural network. Experimental results on the benchmark dataset demonstrate the effectiveness of our proposed method.
TL;DR: In this paper, a new unscented Kalman filter with guaranteed positive semidifinite estimation error covariance (UKF-GPS) is proposed and compared with five existing approaches, including UKF-schol, UKF -boldsymbol {\kappa }$, UKF modified, UK F-boldsymbmbol {\Delta Q}$, and the square-root UKF (SR-UKF).
Abstract: In this paper, in order to enhance the numerical stability of the unscented Kalman filter (UKF) used for power system dynamic state estimation, a new UKF with guaranteed positive semidifinite estimation error covariance (UKF-GPS) is proposed and compared with five existing approaches, including UKF-schol, UKF- $\boldsymbol {\kappa }$ , UKF-modified, UKF- $\boldsymbol {\Delta Q}$ , and the square-root UKF (SR-UKF). These methods and the extended Kalman filter (EKF) are tested by performing dynamic state estimation on WSCC 3-machine 9-bus system and NPCC 48-machine 140-bus system. For WSCC system, all methods obtain good estimates. However, for NPCC system, both EKF and the classic UKF fail. It is found that UKF-schol, UKF- $\boldsymbol {\kappa }$ , and UKF- $\boldsymbol {\Delta Q}$ do not work well in some estimations while UKF-GPS works well in most cases. UKF-modified and SR-UKF can always work well, indicating their better scalability mainly due to the enhanced numerical stability.
TL;DR: A new kinematic calibration method based on the extended Kalman filter (EKF) and particle filter (PF) algorithm that can significantly improves the positioning accuracy of the robot.
Abstract: Precise positioning of a robot plays an very important role in advanced industrial applications, and this paper presents a new kinematic calibration method based on the extended Kalman filter (EKF) and particle filter (PF) algorithm that can significantly improves the positioning accuracy of the robot. Kinematic and its error models of a robot are established, and its kinematic parameters are identified by using the EKF algorithm first. But the EKF algorithm has a kind of linear truncation error and it is useful for the Gauss noise system in general, so its identified accuracy will be affected for the highly nonlinear robot kinematic system with a non-Gauss noise system. The PF algorithm can solve this with non-Gauss noise and a high nonlinear problem well, but its calibration accuracy and efficiency are affected by the prior distribution of the initial values. Therefore, this paper proposes to use the calibration value of the EKF algorithm as the prior value of the PF algorithm, and then, the PF algorithm is used further to calibrate the kinematic parameters of the robot. Enough experiments have been carried out, and the experimental results validated the viability of the proposed method with the robot positioning accuracy improved significantly.
TL;DR: This paper forms this secure estimation problem into the classical error correction problem (Candes and Tao, 2005) and shows that accurate decoding can be guaranteed and proposes a combined secure estimation method with the proposed secure estimator and the Kalman Filter for improved practical performance.
TL;DR: A novel localization methodology to enhance the accuracy from two aspects, i.e., adapting to the uncertain noise of microelectromechanical system-based inertial navigation system (MEMS-INS) and accurately predicting INS errors.
Abstract: In this paper, we propose a novel localization methodology to enhance the accuracy from two aspects, i.e., adapting to the uncertain noise of microelectromechanical system-based inertial navigation system (MEMS-INS) and accurately predicting INS errors. First, an interacting multiple model (IMM)-based sequential two-stage Kalman filter is proposed to fuse the information of MEMS-INS, global positioning system (GPS), and in-vehicle sensors. Three bias filters are built with different covariance matrices to cover a wide range of noise characteristics. Then, IMM algorithm provides a soft switching among the three bias filters to adapt to the uncertain noise of MEMS-INS. Further, an elaborate predictor is developed to accurately predict INS errors during GPS outages. The elaborate predictor comprises an online trained autoregressive integrated moving average (ARIMA) model and an offline trained extreme learning machine (ELM) model. The ARIMA model is designed to predict the basic accumulation process of INS errors, while the ELM model is designed to correct the errors caused by the changes of noise characteristics. Thus, the INS errors can be properly compensated when GPS observations are not available. In all, the proposed localization methodology can achieve accurate performance when facing uncertain noises of MEMS-INS and GPS outages simultaneously. To verify the effectiveness of the proposed methodology, road test experiments with various driving scenarios were performed. The experimental results illustrate the feasibility and effectiveness of the proposed methodology.