Using Unscented Kalman Filter Research Articles

Natural Navigation System Design for Indoor Mobile Robots

Natural navigation simply refers to free navigation without the necessity of tapes, magnets, reflectors, or even wires. Many autonomous vehicles possess this as world maps are readily available and provide a perfect basis for machine learning solutions. However, this is not so much the case for indoor applications. Here, paths are often dynamic and more constrained; therefore, requiring the continuous identification, mapping and localization of the surrounding area. This work focuses on developing an indoor natural navigation system; the localization is achieved with a fusion of the wheel’s odometry to the on-board Inertial Measurement Unit (IMU i.e., a combination of relative localization and absolute localization) using Unscented Kalman Filter (UKF) as system’s encoder’s accumulation of errors is desired to be nullified while employing a PID control in correcting reference state errors. The map is simultaneously constructed using laws of geometry based on static points obtained from a Lidar, subsequently converted to an occupancy grid layout for effective path planning. In operation, tangency is applied in the avoidance of dynamic obstacles. The simulation results obtained in this study confirms the possibility of a simple, educational, indoor navigation system approach easily integrable by other mobile robots of the differential drive model.

MISSILE POSITION ESTIMATION USING UNSCENTED KALMAN FILTER

Missiles are military rocket weapons having an automatic control system to locate its targets or adjust its direction. Indonesia itself, which is a country of archipelago, covers air area of its largest territory, followed by sea area and land area. Logically, the existence of missile defense equipment (the main weapon system) or precisely the type of long-range missile is acceptable to support the defense and security of the Republic of Indonesia, but its consequences to be operated in the territory of Indonesia itself, in case of an occufanct of an error in targeting the target, will fall on of harm to its own national territory. Therefore, trajectory estimation for guided missiles is the basic requirement for guided missiles to be aimed at the precise targets. The trajectory is used as a guide to direct that the missile reach the target by following the given path. To maintain the accuracy of the trajectory continuously, the missile trajectory estimation was made by using Unscented Kalman Filter (UKF) Algorithm. This algorithm was used to estimate nonlinear dynamic models The simulation results showed that the UKF method was effective, showing the accuracy of 97% by the UKF method

Abstract 64: Diffusion Tensor Imaging Suggests Decreased Axonal Myelination In Children With Moyamoya Without Stroke

Introduction: Moyamoya is a progressive cerebrovascular disease leading to arterial stenosis of the circle of Willis. Using diffusion tensor imaging (DTI), adults with moyamoya have significantly decreased white matter fractional anisotropy (FA) compared to controls even when conventional imaging is normal. This raises concern for under recognized white matter injury. Children with moyamoya have been shown to have significantly increased whole brain apparent diffusion coefficient (ADC) values. However, to our knowledge the involvement of specific white matter tracts in children with moyamoya, relevant to specific cognitive domains, has not been evaluated. Methods: We present a cohort of 16 children with moyamoya with 25 affected hemispheres without stroke compared to 25 children controls. We analyzed DTI data using unscented Kalman filter (UKF) tractography and extracted major white matter pathways with a fiber clustering method. We compared the FA, ADC, AD, and RD globally as well as within each white matter tract using ANOVA. Results: Age and sex were not significantly different between children with moyamoya and controls. For all white matter tracts, children with moyamoya had significantly lower FA (-7.0%, p=0.04) as well as higher ADC (5.7%, p=0.002) and RD (9.5%, p=0.002). AD was also higher but not significant (2.3%, p=0.21). Specific white matter tracts that were significantly affected included: inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus and thalamo-frontal tract (Table 1). Discussion: Lower FA with higher ADC and RD is consistent with decreased axonal myelination. Tracts in watershed regions were especially affected in children with moyamoya. These findings are also consistent with concerns that children with moyamoya are chronically hypoperfused. In conclusion, future research is needed to determine whether these findings are reversible.

Unscented Kalman filter for airship model uncertainties and wind disturbance estimation.

An airship is lighter than an air vehicle with enormous potential in applications such as communication, aerial inspection, border surveillance, and precision agriculture. An airship model is made up of dynamic, aerodynamic, aerostatic, and propulsive forces. However, the computation of aerodynamic forces remained a challenge. In addition to aerodynamic model deficiencies, airship mass matrix suffers from parameter variations. Moreover, due to the lighter-than-air nature, it is also susceptible to wind disturbances. These modeling issues are the key challenges in developing an efficient autonomous flight controller for an airship. This article proposes a unified estimation method for airship states, model uncertainties, and wind disturbance estimation using Unscented Kalman Filter (UKF). The proposed method is based on a lumped model uncertainty vector that unifies model uncertainties and wind disturbances in a single vector. The airship model is extended by incorporating six auxiliary state variables into the lumped model uncertainty vector. The performance of the proposed methodology is evaluated using a nonlinear simulation model of a custom-developed UETT airship and is validated by conducting a kind of error analysis. For comparative studies, EKF estimator is also developed. The results show the performance superiority of the proposed estimator over EKF; however, the proposed estimator is a bit expensive on computational grounds. However, as per the requirements of the current application, the proposed estimator can be a preferred choice.

UKF-Based State Estimation for Electrolytic Oxygen Generation System of Space Station

Electrolytic oxygen generation system (EOGS) is the only system that can provide oxygen for astronauts in a physicochemical regenerative way in a long-term manned spacecraft. In order to ensure that the astronauts in the cabin can obtain a continuous and enough oxygen supply, it is necessary to carry out real-time condition monitoring and fault diagnosis of the EOGS. This paper deals with condition monitoring and fault diagnosis of the EOGS. Firstly, the dynamic model of the system is established based on the principle electrolysis for actual oxygen production system and the state observer of the system has been designed by using unscented Kalman filter (UKF). The total pressure in the cabin and the partial pressure of oxygen in the electrolytic cell can be observed. Then, considered the actual conditions of the manned space mission with one more astronaut, i.e., 3 astronauts, the simulation experiment is carried out. The simulation results show that the method can effectively estimate the system state, and it is of great significance to ensure the normal operation of the electrolytic EOGS system in the space station.

Tool condition monitoring: unscented Kalman filter for tool flank wear estimation in turning of Inconel 718

This article presents flank wear estimation during the turning process of Inconel 718 in dry cutting condition using unscented Kalman filter (UKF). A discrete flank wear model is developed where two components of flank wear due to abrasion and diffusion are considered as state variables and the cutting force is taken as the output variable. The proposed model can be implemented for online tool wear monitoring and the UKF predicts the actual states of tool flank wear in real-time by measuring the cutting force variation. The simulation result of flank wear estimation using UKF is compared with the extended Kalman filter (EKF) under a similar cutting environment. The error of estimation for UKF is obtained less than that of EKF indicating better accuracy of UKF than EKF for tool condition monitoring of the proposed model. Hardware experiments are performed to validate simulated results of both UKF and EKF on the proposed model correlating the cutting force and flank wear components.

Accuracy Improvement of RSSI-based Distance Localization using Unscented Kalman Filter (UKF) Algorithm for Wi-Fi Tracking Application

<p class="0abstract">In this report, we perform the digital filter computation using Matlab for Wi-Fi tracking application. This work motivates to improve the accuracy of filter algorithm in the RSSI-based distance localization system. There are several aspects that we can improve, e.g., in the Filter part and Path-loss model. But, in this work, we focus on filter part; Unscented Kalman Filter (UKF) is implemented to replace linear Kalman Filter (KF), which is used in previous work. Based on the performance comparison, UKF has 90% hit ratio while linear KF has only 81.15 % hit ratio. We found that UKF can handle the noise in RSSI. Further work, the UKF algorithm is then embedded on the server system.</p>

Estimation of Parasitic Capacitance of Common Mode Noise in Vehicular Applications: An Unscented Kalman Filter-Based Approach

Parasitic capacitance has a considerable impact on conductive electromagnetic interference (EMI), especially on the common mode (CM) noise. In vehicular applications, such as more electric aircraft and electrical vehicles, the value of CM stray capacitance can vary during the drive cycle, due to some change in environmental, and operating conditions, resulting in fluctuations in EMI level. Designing EMI filters according to potentially overestimated CM capacitance results in additional weight, volume, and cost which should be avoided. In this paper, for the first time, the parasitic capacitance relevant to CM noise is estimated in real-time using unscented Kalman filter (UKF). The UKF is employed due to its ability in dealing with nonlinearity and stochastic nature of the EMI as well as the measurement noises. The proposed method is tested on a typical dc-dc buck converter in different operating conditions. The effectiveness of the proposed method is tested using some hardware-in-the-loop experiments using dSPACE1104 development platform.

Identification of nonlinear aerodynamic damping from stochastic crosswind response of tall buildings using unscented Kalman filter technique

This study presents a new approach for identification of aerodynamic damping of tall buildings from stochastic crosswind response time history using unscented Kalman filter (UKF) technique. The system damping ratio is expressed as a polynomial function of building displacement or velocity, which is equivalent to a polynomial function of amplitude of harmonic motion. The structural system with nonlinear system damping under stochastic excitation is modeled as a single degree of freedom system with a white noise input. The augmented state variables of the system, which also include unknown system frequency, damping and stochastic excitation parameters, are estimated simultaneously with the UKF technique from the response measurement data. The aerodynamic damping is then extracted from the system damping by subtracting the structural damping. Firstly, the stochastic response time histories of a tall building model at various wind speeds with a known nonlinear aerodynamic model are simulated and the performance of the UKF technique and the influence of selections of various parameters involved are investigated. Secondly, the aerodynamic damping of a square-shaped tall building is identified based on aeroelastic building model wind tunnel test data. The response statistics are then computed using the identified damping and compared with the measurement data to verify the accuracy of identification.

Adaptive model predictive climate control of multi-unit buildings using weather forecast data

Energy use in buildings contributes a large part in global energy demand. To reduce energy use in this group of consumers, specially in cold seasons, an automatic control technique is proposed. In this paper, a model predictive controller (MPC) is employed to minimize the boiler activation time. The method uses the building model and incorporates the weather forecast data to act on the actuator in an optimal fashion while treating the user comfort constraints. This technique, as a part, can be embedded into the building energy management system. The building model parameters are obtained via an online identification process using unscented kalman filter (UKF). This identification is performed on-the-fly so the model of a building is continuously updated. The results of the system identification as well as the control performance are shown via Monte Carlo simulations, and compared with the results of a conventional control law. The comparison shows that the proposed method saves %13 energy consumption in the boiler activation.

Experimental investigation and numerical analysis for concrete-CLT connections

The mechanical characteristics of concrete to cross laminated timber (CLT) connections plays an essential role in a hybrid structure. This study mainly deals with an experimental and numerical study on the load-slip behaviour of concrete-CLT connections under cyclic loading, where six specimens with two different loading types (i.e., parallel and perpendicular to the grain of CLT) were executed. The failure modes of all specimens were the bolts shear off at the location of the interface between concrete and CLT, associated with the cracking and splitting of CLT panels depending on loading directions. Then the numerical model whose nonlinearity is concentrated on the spring element is created. To calibrate the model parameters, the procedure of parameter estimation using unscented Kalman filter (UKF) is employed to identify the parameters of a hysteresis model. Two assessment methods, including the equivalent energy elastic-plastic curve assessment and an energy-based damage assessment, are used to comprehensively evaluate the estimated result. The model estimate is consistent with the experimental result, demonstrating the identified parameters having good accuracy. The calibrated parameters of a hysteresis model provide a good database when such type of connections are incorporated into a finite element model of a structural system.

Research on Two Types of Estimation Algorithms in Carrier Tracking Loop

This paper analyzes two estimation algorithms in the carrier loop, i.e. maximum likelihood estimation (MLE) and modern filtering based on Bayesian recursion. The limitations of the MLE algorithm are pointed out by establishing the MLE model. In addition, several modern filters based on Bayesian recursion are analyzed. In this process, the article draws two inferences affecting high dynamics and low signal-to-noise ratio (SNR) carrier tracking from two aspects of state equation and measurement equation. Thus, the optimal algorithm using unscented Kalman filter (UKF) is proposed by the inferences mentioned. at the end, simulation results demonstrate the combined strong tracking algorithm has advantages in case of high dynamic and low SNR.

이동로봇의 분산점 칼만 필터 기반 옥외용 위치평가

This paper proposes a practical method, for evaluating positioning of outdoor mobile robots using Unscented Kalman Filter (UKF). Since the UKF method does not require the linearization process unlike EKF localization, it can minimize effects of errors caused by linearization of non-linear models for position estimation. This method enables relatively high performance position estimation, using only non-inertial sensors such as low-precision GPS and a digital compass. Effectiveness of the UKF localization method was verified through actual experiments and performance of position estimation was compared with that of the existing EKF method. Experimental results revealed the proposed method has better performance than the EKF method, and it is stable regardless of initial error size, and observation period.

Preliminary study on estimation of 12.7 x 99 mm caliber projectile using Unscented Kalman Filter method

The projectile dynamics model is non-linear system. The specifications of projectile used are 12.7 x 99 mm caliber. In the projectile motion, to achieve the target can be estimated by using some estimation methods. In this study, the estimation of 12.7 x 99 mm caliber projectile motion is computed by using Unscented Kalman Filter (UKF) method. In this simulation, we estimate model of the system by measuring each variables position. The estimation results show the performance of UKF and KF in non-linear systems is different, based on their respective characteristics. The final result of this study shows that the error estimation of UKF is 81 % smaller in x-position, 85 % smaller in h-position and ν-position, 84 % smaller in γ-position. UKF performance is very good for non-linear system and the approach with KF algorithm has less optimal result.

Optimization of microalgal photobioreactor system using model predictive control with experimental validation.

To maximize biomass and lipid concentrations, various optimization methods were investigated in microalgal photobioreactor systems under mixotrophic conditions. Lipid concentration was estimated using unscented Kalman filter (UKF) with other measurable sources and subsequently used as lipid data for performing model predictive control (MPC). In addition, the maximized biomass and lipid trajectory obtained by open-loop optimization were used as target trajectory for tracking by MPC. Simulation studies and experimental validation were performed and significant improvements in biomass and lipid productivity were achieved in the case where MPC was applied. However, occurence of a lag phase was observed while manipulating the feed flow rates, which is induced by large amount of inputs. This is an important phenomenon that can lead to model-plant mismatch and requires further study for the optimization of microalgal photobioreactors.

Novel method for static pressure calibration using Unscented Kalman Filter

Static pressure plays an important role in modern fighters in terms of providing stability augmentation and control. Static pressure is measured by pitot static systems. They can be installed at different locations in the aircraft. Each of them measure different values and hence calibration of these pitot static systems is essential to produce reliable static pressure. This paper addresses a novel method for static pressure calibration using Unscented Kalman Filter (UKF). The method proposed here can be extended to Flush Air Data Systems (FADS). The technique is tested using the flight test data of a high performance aircraft.

Model-Based Estimation for Vehicle Dynamics States at the Limit Handling

This technical brief proposes a new model-based estimation method for the vehicle sideslip angle, yaw rate, roll angle, and roll rate using unscented Kalman filter (UKF). Since a vehicle wheel could potentially lift off the ground during the limit handling, a switched vehicle roll dynamics model (wheel lift and no wheel lift) is developed and integrated within the proposed model-based estimation approach considering the availability of wheel speed sensor. The simulation results and analyses demonstrate the performance enhancement of the proposed estimation method over the method not considering wheel lift during the limit handling.

An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction

The gradual decreasing capacity of lithium-ion batteries can serve as a health indicator for tracking the degradation of lithium-ion batteries. It is important to predict the capacity of a lithium-ion battery for future cycles to assess its health condition and remaining useful life (RUL). In this paper, a novel method is developed using unscented Kalman filter (UKF) with relevance vector regression (RVR) and applied to RUL and short-term capacity prediction of batteries. A RVR model is employed as a nonlinear time-series prediction model to predict the UKF future residuals which otherwise remain zero during the prediction period. Taking the prediction step into account, the predictive value through the RVR method and the latest real residual value constitute the future evolution of the residuals with a time-varying weighting scheme. Next, the future residuals are utilized by UKF to recursively estimate the battery parameters for predicting RUL and short-term capacity. Finally, the performance of the proposed method is validated and compared to other predictors with the experimental data. According to the experimental and analysis results, the proposed approach has high reliability and prediction accuracy, which can be applied to battery monitoring and prognostics, as well as generalized to other prognostic applications.

Identification of synchronous generator model with frequency control using unscented Kalman filter

In this paper, phasor measurement unit (PMU) data-based synchronous generator model identification is carried out using unscented Kalman filter (UKF). The identification not only gives the model of a synchronous generator's swing dynamics, but also gives its turbine-governor model along with the primary and secondary frequency control block models. PMU measurements of active power and voltage magnitude, are treated as the inputs to the system while the measurements of voltage phasor angle, reactive power and frequency are treated as the outputs. UKF-based estimation is carried out to estimate the dynamic states and the parameters of the model. The estimated model is then built and excited with the injection of the inputs from the PMU measurements. The outputs of the estimation model and the outputs from the PMU measurements are compared. Case studies based on PMU measurements collected from a simulation model and real-world PMU data demonstrate the effectiveness of the proposed estimation scheme.

Rotor Position and Speed Estimation of Interior Permanent Magnet Synchronous Motor using Unscented Kalman Filter

This paper proposes the rotor position and rotor speed estimation for an interior permanent magnet synchronous machines (IPMSM) using Unscented Kalman Filter (UKF) in alpha-beta coordinate system. Conventional algorithms using UKF are based on the simple observer model of IPMSM in d-q coordinate system. Rotor acceleration is neglected within the sampling step. An expansion of the observer model in an alphabeta coordinate system with the consideration of the rotor speed variation provides the improved rotor position and speed estimation. The results show good stability concerning the expansion of observer model for the IPMSM.