Showing papers on "Kalman filter published in 2016"

Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine

Abstract: An accurate algorithm for lithium polymer battery state-of-charge (SOC) estimation is proposed based on adaptive unscented Kalman filters (AUKF) and least-square support vector machines (LSSVM). A novel approach using the moving window method is applied, with AUKF and LSSVM to accurately establish the battery model with limited initial training samples. The effectiveness of the moving window modeling method is validated by both simulations and lithium polymer battery experimental results. The measurement equation of the proposed AUKF method is established by the LSSVM battery model and AUKF has the advantage of adaptively adjusting noise covariance during the estimation process. In addition, the developed LSSVM model is continuously updated online with new samples during the battery operation, in order to minimize the influence of the changes in battery internal characteristics on modeling accuracy and estimation results after a period of operation. Finally, a comparison of accuracy and performance between the AUKF and UKF is made. Simulation and experiment results indicate that the proposed algorithm is capable of predicting lithium battery SOC with a limited number of initial training samples.

Mutual Information-Based Multi-AUV Path Planning for Scalar Field Sampling Using Multidimensional RRT*

Abstract: Autonomous underwater vehicles (AUVs) have been widely employed in ocean survey, monitoring, and search and rescue tasks for both civil and military applications. It is beneficial to use multiple AUVs that perform environmental sampling and sensing tasks for the purposes of efficiency and cost effectiveness. In this paper, an adaptive path planning algorithm is proposed for multiple AUVs to estimate the scalar field over a region of interest. In the proposed method, a measurable model composed of multiple basis functions is defined to represent the scalar field. A selective basis function Kalman filter is developed to achieve model estimation through the information collected by multiple AUVs. In addition, a path planning method, the multidimensional rapidly exploring random trees star algorithm, which uses mutual information, is proposed for the multi-AUV system. Employing the path planning algorithm, the sampling positions of the AUVs are determined to improve the quality of future samples by maximizing the mutual information between the scalar field model and observations. Extensive simulation results are provided to demonstrate the effectiveness of the proposed algorithm. Additionally, an indoor experiment using four robotic fishes is carried out to validate the algorithms presented.

Isolated intersection control for various levels of vehicle technology: Conventional, connected, and automated vehicles

Abstract: Connected vehicle technology can be beneficial for traffic operations at intersections. The information provided by cars equipped with this technology can be used to design a more efficient signal control strategy. Moreover, it can be possible to control the trajectory of automated vehicles with a centralized controller. This paper builds on a previous signal control algorithm developed for connected vehicles in a simple, single intersection. It improves the previous work by (1) integrating three different stages of technology development; (2) developing a heuristics to switch the signal controls depending on the stage of technology; (3) increasing the computational efficiency with a branch and bound solution method; (4) incorporating trajectory design for automated vehicles; (5) using a Kalman filter to reduce the impact of measurement errors on the final solution. Three categories of vehicles are considered in this paper to represent different stages of this technology: conventional vehicles, connected but non-automated vehicles (connected vehicles), and automated vehicles. The proposed algorithm finds the optimal departure sequence to minimize the total delay based on position information. Within each departure sequence, the algorithm finds the optimal trajectory of automated vehicles that reduces total delay. The optimal departure sequence and trajectories are obtained by a branch and bound method, which shows the potential of generalizing this algorithm to a complex intersection. Simulations are conducted for different total flows, demand ratios and penetration rates of each technology stage (i.e. proportion of each category of vehicles). This algorithm is compared to an actuated signal control algorithm to evaluate its performance. The simulation results show an evident decrease in the total number of stops and delay when using the connected vehicle algorithm for the tested scenarios with information level of as low as 50%. Robustness of this algorithm to different input parameters and measurement noises are also evaluated. Results show that the algorithm is more sensitive to the arrival pattern in high flow scenarios. Results also show that the algorithm works well with the measurement noises. Finally, the results are used to develop a heuristic to switch between the different control algorithms, according to the total demand and penetration rate of each technology.

Weighted Average Consensus-Based Unscented Kalman Filtering

Abstract: In this paper, we are devoted to investigate the consensus-based distributed state estimation problems for a class of sensor networks within the unscented Kalman filter (UKF) framework. The communication status among sensors is represented by a connected undirected graph. Moreover, a weighted average consensus-based UKF algorithm is developed for the purpose of estimating the true state of interest, and its estimation error is bounded in mean square which has been proven in the following section. Finally, the effectiveness of the proposed consensus-based UKF algorithm is validated through a simulation example.

Machine Learning Techniques in Optical Communication

Abstract: Machine learning techniques relevant for nonlinearity mitigation, carrier recovery, and nanoscale device characterization are reviewed and employed. Markov Chain Monte Carlo in combination with Bayesian filtering is employed within the nonlinear state-space framework and demonstrated for parameter estimation. It is shown that the time-varying effects of cross-phase modulation (XPM) induced polarization scattering and phase noise can be formulated within the nonlinear state-space model (SSM). This allows for tracking and compensation of the XPM induced impairments by employing approximate stochastic filtering methods such as extended Kalman or particle filtering. The achievable gains are dependent on the autocorrelation (AC) function properties of the impairments under consideration which is strongly dependent on the transmissions scenario. The gain of the compensation method are therefore investigated by varying the parameters of the AC function describing XPM-induced polarization scattering and phase noise. It is shown that an increase in the nonlinear tolerance of more than 2 dB is achievable for 32 Gbaud QPSK and 16-quadratic-amplitude modulation (QAM). It is also reviewed how laser rate equations can be formulated within the nonlinear state-space framework which allows for tracking of nonLorentzian laser phase noise lineshapes. It is experimentally demonstrated for 28 Gbaud 16-QAM signals that if the laser phase noise shape strongly deviates from the Lorentzian, phase noise tracking algorithms employing rate equation-based SSM result in a significant performance improvement ( $>$ 8 dB) compared to traditional approaches using digital phase-locked loop. Finally, Gaussian mixture model is reviewed and employed for nonlinear phase noise compensation and characterization of nanoscale devices structure variations.

Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction

Abstract: Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art multiobjective evolutionary algorithms (MOEAs). Nevertheless, the interest in applying them to solve dynamic multiobjective optimization problems has only been tepid. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. One or more objectives may change with time in dynamic optimization problems. The optimization algorithm must be able to track the moving optima efficiently. A prediction model can learn the patterns from past experience and predict future changes. In this paper, a new dynamic MOEA using Kalman filter (KF) predictions in decision space is proposed to solve the aforementioned problems. The predictions help to guide the search toward the changed optima, thereby accelerating convergence. A scoring scheme is devised to hybridize the KF prediction with a random reinitialization method. Experimental results and performance comparisons with other state-of-the-art algorithms demonstrate that the proposed algorithm is capable of significantly improving the dynamic optimization performance.

Fast Complementary Filter for Attitude Estimation Using Low-Cost MARG Sensors

Abstract: This paper proposes a novel quaternion-based attitude estimator with magnetic, angular rate, and gravity (MARG) sensor arrays. A new structure of a fixed-gain complementary filter is designed fusing related sensors. To avoid using iterative algorithms, the accelerometer-based attitude determination is transformed into a linear system. Stable solution to this system is obtained via control theory. With only one matrix multiplication, the solution can be computed. Using the increment of the solution, we design a complementary filter that fuses gyroscope and accelerometer together. The proposed filter is fast, since it is free of iteration. We name the proposed filter the fast complementary filter (FCF). To decrease significant effects of unknown magnetic distortion imposing on the magnetometer, a stepwise filtering architecture is designed. The magnetic output is fused with the estimated gravity from gyroscope and accelerometer using a second complementary filter when there is no significant magnetic distortion. Several experiments are carried out on real hardware to show the performance and some comparisons. Results show that the proposed FCF can reach the accuracy of Kalman filter. It successfully finds a balance between estimation accuracy and time consumption. Compared with iterative methods, the proposed FCF has much less convergence speed. Besides, it is shown that the magnetic distortion would not affect the estimated Euler angles.

Design of interval observers for uncertain dynamical systems

Abstract: Interval state observers provide an estimate on the set of admissible values of the state vector at each instant of time. Ideally, the size of the evaluated set is proportional to the model uncertainty, thus interval observers generate the state estimates with estimation error bounds, similarly to Kalman filters, but in the deterministic framework. Main tools and techniques for design of interval observers are reviewed in this tutorial for continuous-time, discrete-time and time-delayed systems.

Two-Channel False Data Injection Attacks Against Output Tracking Control of Networked Systems

Abstract: This paper addresses the design problem of false data injection (FDI) attacks against the output tracking control of networked systems, where the network-induced delays in the feedback and forward channels are considered. The main contributions of this paper are as follows. 1) To actively compensate for the two-channel network-induced delays, a Kalman filter-based networked predictive control scheme is designed for stochastic linear discrete-time systems. 2) From an attacker’s perspective, stealthy FDI attacks are proposed for both the feedback and forward channels so as to disrupt the stability of the resulting closed-loop system while avoiding the detection of a Kalman filter-based attack detector. 3) Both numerical simulations and practical experiments are carried out to show the effectiveness of the proposed method.

A Localized Particle Filter for High-Dimensional Nonlinear Systems

Abstract: This paper presents a new data assimilation approach based on the particle filter (PF) that has potential for nonlinear/non-Gaussian applications in geoscience. Particle filters provide a Monte Carlo approximation of a system’s probability density, while making no assumptions regarding the underlying error distribution. The proposed method is similar to the PF in that particles—also referred to as ensemble members—are weighted based on the likelihood of observations in order to approximate posterior probabilities of the system state. The new approach, denoted the local PF, extends the particle weights into vector quantities to reduce the influence of distant observations on the weight calculations via a localization function. While the number of particles required for standard PFs scales exponentially with the dimension of the system, the local PF provides accurate results using relatively few particles. In sensitivity experiments performed with a 40-variable dynamical system, the local PF require...

Sensor Fault Detection, Isolation, and Identification Using Multiple-Model-Based Hybrid Kalman Filter for Gas Turbine Engines

Abstract: In this paper, a novel sensor fault detection, isolation, and identification (FDII) strategy is proposed using the multiple-model (MM) approach. The scheme is based on multiple hybrid Kalman filters (MHKFs), which represents an integration of a nonlinear mathematical model of the system with a number of piecewise linear (PWL) models. The proposed fault detection and isolation (FDI) scheme is capable of detecting and isolating sensor faults during the entire operational regime of the system by interpolating the PWL models using a Bayesian approach. Moreover, the proposed MHKF-based FDI scheme is extended to identify the magnitude of a sensor fault using a modified generalized likelihood ratio method that relies on the healthy operational mode of the system. To illustrate the capabilities of our proposed FDII methodology, extensive simulation studies are conducted for a nonlinear gas turbine engine. Various single and concurrent sensor fault scenarios are considered to demonstrate the effectiveness of our proposed online hierarchical MHKF-based FDII scheme under different flight modes. Finally, our proposed hybrid Kalman filter (HKF)-based FDI approach is compared with various filtering methods such as the linear, extended, unscented, and cubature Kalman filters corresponding to both interacting and noninteracting MM-based schemes. Our comparative studies confirm the superiority of our proposed HKF method in terms of promptness of the fault detection, lower false alarm rates, as well as robustness with respect to the engine health parameter degradations.

A Linear Kalman Filter for MARG Orientation Estimation Using the Algebraic Quaternion Algorithm

Abstract: Real-time orientation estimation using low-cost inertial sensors is essential for all the applications where size and power consumption are critical constraints. Such applications include robotics, human motion analysis, and mobile devices. This paper presents a linear Kalman filter for magnetic angular rate and gravity sensors that processes angular rate, acceleration, and magnetic field data to obtain an estimation of the orientation in quaternion representation. Acceleration and magnetic field observations are preprocessed through a novel external algorithm, which computes the quaternion orientation as the composition of two algebraic quaternions. The decoupled nature of the two quaternions makes the roll and pitch components of the orientation immune to magnetic disturbances. The external algorithm reduces the complexity of the filter, making the measurement equations linear. Real-time implementation and the test results of the Kalman filter are presented and compared against a typical quaternion-based extended Kalman filter and a constant gain filter based on the gradient-descent algorithm.

Understanding the Ensemble Kalman Filter

Abstract: The ensemble Kalman filter (EnKF) is a computational technique for approximate inference in state-space models. In typical applications, the state vectors are large spatial fields that are observed sequentially over time. The EnKF approximates the Kalman filter by representing the distribution of the state with an ensemble of draws from that distribution. The ensemble members are updated based on newly available data by shifting instead of reweighting, which allows the EnKF to avoid the degeneracy problems of reweighting-based algorithms. Taken together, the ensemble representation and shifting-based updates make the EnKF computationally feasible even for extremely high-dimensional state spaces. The EnKF is successfully used in data-assimilation applications with tens of millions of dimensions. While it implicitly assumes a linear Gaussian state-space model, it has also turned out to be remarkably robust to deviations from these assumptions in many applications. Despite its successes, the EnKF is ...

Application of Kalman Filter to Estimate Junction Temperature in IGBT Power Modules

Abstract: Knowledge of instantaneous junction temperature is essential for effective health management of power converters, enabling safe operation of the power semiconductors under all operating conditions. Methods based on fixed thermal models are typically unable to compensate for degradation of the thermal path resulting from aging and the effect of variable cooling conditions. Thermosensitive electrical parameters (TSEPs), on the other hand, can give an estimate of junction temperature ${T_J}$ , but measurement inaccuracies and the masking effect of varying operating conditions can corrupt the estimate. This paper presents a robust and noninvasive real-time estimate of junction temperature that can provide enhanced accuracy under all operating and cooling conditions when compared to model-based or TSEP-based methods alone. The proposed method uses a Kalman filter to fuse the advantages of model-based estimates and an online measurement of TSEPs. Junction temperature measurements are obtained from an online measurement of the on-state voltage, ${V}_{{\bf CE(ON)}}$ , at high current and processed by a Kalman filter, which implements a predict-correct mechanism to generate an adaptive estimate of ${T_ J}$ . It is shown that the residual signal from the Kalman filter may be used to detect changes in thermal model parameters, thus allowing the assessment of thermal path degradation. The algorithm is implemented on a full-bridge inverter and the results verified with an IR camera.

Multisensor Data Fusion

Abstract: Multisensor data fusion is the process of combining observations from a number of different sensors to provide a robust and complete description of an environment or process of interest. Data fusion finds wide application in many areas of robotics such as object recognition, environment mapping, and localization.

Cooperative space object tracking using space-based optical sensors via consensus-based filters

Abstract: Cooperative tracking plays a key role in space situation awareness for scenarios with a limited number of observations or poor performance of a single sensor or both. To use the information from multiple networked sensors, both centralized and decentralized fusion algorithms can be used. Compared with centralized fusion algorithms, decentralized fusion algorithms are more robust in terms of communication failure and computational burden. One popular distributed estimation approach is based on the average consensus that asymptotically converges to the estimate by multiple exchanges of neighborhood information. Consensus-based algorithms have become popular in recent years due to the fact that they do not require global knowledge of the network or routing protocols. The main contributions of this paper are 1) an effective space-based object (SBO) measurement model that considers the geometric relation of the Sun, the space object, the SBO sensor, and the Earth; 2) two consensus-based filters, the information-weighted consensus filter (ICF) and the Kalman consensus filter (KCF), are used to track space objects by using multiple SBO sensors; and 3) the cubature rule-embedded ICF (Cub-ICF) and KCF (Cub-KCF) are proposed to improve the accuracy of the ICF and KCF. Three scenarios that contain one or two space objects and four SBOs are used to test proposed algorithms. We also compare the consensus-based space object tracking algorithms with the centralized extended information filter (centralized EIF) and the centralized cubature information filter (centralized Cub-IF). The simulation results indicate that 1) cooperative space object tracking algorithms provide better results than algorithms using a single sensor, 2) the consensus-based tracking algorithms can achieve performance close to that of the centralized algorithms, and 3) the Cub-ICF and Cub-KCF outperform the conventional ICF and KCF for a challenging space object tracking case shown in the paper. The proposed Cub-ICF and Cub-KCF algorithms should facilitate the application of using consensus-based filters for cooperative space object tracking.

Robust Relative Navigation by Integration of ICP and Adaptive Kalman Filter Using Laser Scanner and IMU

Abstract: This paper presents a robust six-degree-of-freedom relative navigation by combining the iterative closet point (ICP) registration algorithm and a noise-adaptive Kalman filter in a closed-loop configuration together with measurements from a laser scanner and an inertial measurement unit (IMU). In this approach, the fine-alignment phase of the registration is integrated with the filter innovation step for estimation correction, while the filter estimate propagation provides the coarse alignment needed to find the corresponding points at the beginning of ICP iteration cycle. The convergence of the ICP point matching is monitored by a fault-detection logic, and the covariance associated with the ICP alignment error is estimated by a recursive algorithm. This ICP enhancement has proven to improve robustness and accuracy of the pose-tracking performance and to automatically recover correct alignment whenever the tracking is lost. The Kalman filter estimator is designed so as to identify the required parameters such as IMU biases and location of the spacecraft center of mass. The robustness and accuracy of the relative navigation algorithm is demonstrated through a hardware-in-the loop simulation setting, in which actual vision data for the relative navigation are generated by a laser range finder scanning a spacecraft mockup attached to a robotic motion simulator.

The Accurate Continuous-Discrete Extended Kalman Filter for Radar Tracking

Abstract: This paper elaborates the Accurate Continuous-Discrete Extended Kalman Filter grounded in an ODE solver with global error control and its comparison to the Continuous-Discrete Cubature and Unscented Kalman Filters. All these state estimators are examined in severe conditions of tackling a seven-dimensional radar tracking problem, where an aircraft executes a coordinated turn. The latter is considered to be a challenging one for testing nonlinear filtering algorithms. Our numerical results show that all the methods can be used for practical target tracking, but the Accurate Continuous-Discrete Extended Kalman Filter is more flexible and robust. It treats successfully (and without any manual tuning) the air traffic control scenario for various initial data and for a range of sampling times.

Highway Traffic State Estimation With Mixed Connected and Conventional Vehicles

Abstract: We present a macroscopic model-based approach for the estimation of the total density and flow of vehicles, for the case of “mixed” traffic, i.e., traffic comprising both ordinary and connected vehicles, utilizing only average speed measurements reported by connected vehicles and a minimum number (sufficient to guarantee observability) of spot-sensor-based total flow measurements. The approach is based on the realistic and validated assumption that the average speed of conventional vehicles is roughly equal to the average speed of connected vehicles, and consequently, it can be obtained at the (local or central) traffic monitoring and control unit from connected vehicles' reports. Thus, complete traffic state estimation (for arbitrarily selected segments in the network) may be achieved by estimating the total density of vehicles. Recasting the dynamics of the total density of vehicles, which are described by the well-known conservation law equation, as a linear parameter-varying system, we employ a Kalman filter for the estimation of the total density. We demonstrate the fact that the developed approach allows for a variety of different measurement configurations. We also present an alternative estimation methodology in which traffic state estimation is achieved by estimating the percentage of connected vehicles with respect to the total number of vehicles. The alternative development relies on the alternative requirement that the density and flow of connected vehicles are known to the traffic monitoring and control unit on the basis of their regularly reported positions. We validate the performance of the developed estimation schemes through simulations using a well-known second-order traffic flow model as ground truth for the traffic state.

Detection of Grid Voltage Fundamental and Harmonic Components Using Kalman Filter and Generalized Averaging Method

Abstract: The proper operation of single-phase and three-phase grid-connected power converters depends on the synchronization with utility networks. The major challenge of the synchronization is how to quickly and precisely extract the ac signal and fundamental positive sequence in single- and three-phase power systems, respectively. This paper proposes a new detection technique based on a modified Kalman filter and the generalized averaging method. The method has an open-loop structure, and uses the orthogonal signals which are obtained directly from the Kalman filter. The resulted detection system is very simple and robust even in the presence of power quality disturbances, such as voltage imbalance, harmonics, and voltage fluctuations. The proposed technique can detect the fundamental and harmonics frequencies within or less than half a cycle in all situations, such as small and considerable frequency variations. Meanwhile, the method guarantees the zero steady-state error in complicated harmonic scenarios, including all typical single-phase and three-phase harmonics. Various case studies are assessed and the performance of the proposed detection method is verified by experiments.

Step Detection for ZUPT-Aided Inertial Pedestrian Navigation System Using Foot-Mounted Permanent Magnet

Abstract: A foot-mounted pedestrian dead reckoning system is a self-contained technique for indoor localization. An inertial pedestrian navigation system includes wearable MEMS inertial sensors, such as an accelerometer, gyroscope, or digital compass, which enable the measurement of the step length and the heading direction. Therefore, the use of zero velocity updates is necessary to minimize the inertial drift accumulation of the sensors. The aim of this paper is to develop a foot-mounted pedestrian dead reckoning system based on an inertial measurement unit and a permanent magnet. Our approach enables the stance phase and the step duration detection based on the measurements of the permanent magnet field during each gait cycle. The proposed system involves several parts: inertial state estimation, stance phase detection, altitude measurement, and error state Kalman Filter with zero velocity update and altitude measurement update. Real indoor experiments demonstrate that the proposed algorithm is capable of estimating the trajectory accurately with low estimation error.