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Aiming at cooperative navigation of multi‐aircrafts, an adaptive EKF‐SLAM algorithm for non‐fixed landmark is proposed. First, the aircraft motion model and angle measurement model are established. Second, the fixed landmark cooperative EKF‐SLAM algorithm is established. Furthermore, a distributed EKF‐SLAM algorithm is designed. Then, the adaptive EKF‐SLAM algorithm with non‐fixed landmark is proposed. Finally, simulation results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The problem of dynamic attitude alignment for strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation has become a research hotspot. Underwater complex environment makes DVL output vulnerable to non‐Gaussian noise pollution, which makes it difficult to converge the SINS misalignment angle to within 1° based on analytical coarse alignment. This is to say, the performance of SINS fine alignment via Kalman filter will be degraded because the model of initial alignment exhibits non‐linearity. The inaccurate and/or unknown prior information of measurement noise statistical characteristics will also degrade the filtering performance. To ensure the SINS/DVL alignment accuracy under non‐linear, non‐Gaussian and uncertain conditions, this paper proposed a robust adaptive unscented Kalman filter (UKF) based on Variational Bayesian (VB) method (VBRAUKF). The proposed VBRAUKF improves the adaptability and robustness of UKF from the following two main aspects. Firstly, an adaptive estimation strategy for the measurement noise covariance is designed based on the VB algorithm, which can restrain the effect of inaccurate observation model and improve the adaptive ability of the filtering method. Secondly, an expansion factor is designed based on Mahalanobis distance algorithm, which can restrain the effect of non‐Gaussian noise and improve the robustness of the filtering method. The experimental results for the problem of the SINS/DVL dynamic alignment under mixed Gaussian noise and/or outlier conditions demonstrate the superiority of the proposed VBRAUKF over the traditional ones. [ABSTRACT FROM AUTHOR]

This article presents a novel approach for adaptive nonlinear state estimation in a modified autoregressive time series with fixed coefficients, leveraging an adaptive polynomial Kalman filter (APKF). The proposed APKF dynamically adjusts the evolving system dynamics by selecting an appropriate autoregressive time‐series model corresponding to the optimal polynomial order, based on the minimum residual error. This dynamic selection enhances the robustness of the state estimation process, ensuring accurate predictions, even in the presence of varying system complexities and noise. The proposed methodology involves predicting the next state using polynomial extrapolation. Extensive simulations were conducted to validate the performance of the APKF, demonstrating its superiority in accurately estimating the true system state compared with traditional Kalman filtering methods. The root‐mean‐square error was evaluated for various combinations of standard deviations of sensor noise and process noise for different sample sizes. On average, the root‐mean‐square error value, which represents the disparity between the true sensor reading and estimate derived from the adaptive Kalman filter, was 35.31% more accurate than that of the traditional Kalman filter. The comparative analysis highlights the efficacy of the APKF, showing significant improvements in state estimation accuracy and noise resilience. [ABSTRACT FROM AUTHOR]

This study addresses the state estimation problem of discrete‐time non‐linear stochastic systems with non‐Gaussian noises, particularly impulsive noises. Instead of minimizing the mean square error of the state estimate, which tends to excessively focus on outliers caused by non‐Gaussian noises, the ℓ1${\ell }_1$ norm‐based non‐linear recursive filter (L1KF) is put forward in this paper. Here, minimizing the ℓ1${\ell }_1$ norm of model errors is actually to pursue the minimum sum of absolute values of all errors, which is equitable to all model errors rather than paying much attention on outliers. To further improve estimation accuracy, a recursive nonlinear smoother (L1KS) is proposed, based on minimizing the ℓ1${\ell }_1$ norm of model errors. The proposed ℓ1${\ell }_1$ norm‐based filter and smoother are implemented using unscented transformation for statistical linear regression applied to nonlinear models. Additionally, the computational complexity of the proposed method is analysed. Simulation results of tracking a radar target with impulsive noises demonstrate the effectiveness and robustness of the proposed estimator. [ABSTRACT FROM AUTHOR]

The application of the hybrid extended Kalman filter (HEKF), hybrid unscented Kalman filter (HUKF), hybrid particle filter (HPF), and hybrid extended Kalman particle filter (HEKPF) is discussed for hybrid non‐linear filter problems, when prediction equations are continuous‐time and the update equations are discrete‐time, and also the discrete extended Kalman filter (DEKF), discrete unscented Kalman filter (DUKF), discrete particle filter (DPF), and discrete extended Kalman particle filter (DEKPF) for discrete‐time non‐linear filter problems, when prediction equations and update equations are discrete‐time. In order to assess the performance of the filters, the authors consider the non‐linear dynamics for a re‐entry vehicle. The filters are used in two hybrid and discrete states to estimate the position, velocity, and drag parameter associated with the re‐entry vehicle. Theoretical topics concerning estimating the drag parameter of a vehicle in re‐entry phase have been dealt with. Drag parameter estimation is carried out using a combination of hybrid filters and discrete filters as an effective estimator and fixed value, forgetting factor, and Robbins‐Monro stochastic approximation methods as the noise covariance matrix adjuster of the parameter. [ABSTRACT FROM AUTHOR]

Due to the federal Kalman filter is used to directly fuse the measurement information into the main filter without processing, resulting in the problem of reduced filtering accuracy. An adaptive weighted federated Kalman filtering based on Mahalanobis distance was proposed in this paper. By calculating the Mahalanobis distance between the predicted value and the measurements of the system, the random fluctuation of the measurements is detected. The statistical characteristics of the system measurement noise are adjusted at any time according to random fluctuations in the measurements. And then by using a adaptive amplification factor to dynamically adjust the measurement noise in the subsystems, and reduce the impact of measurement information contamination in subfilters on the main filter. The adaptive federated information distribution coefficient is used to realize the global information fusion of the federal Kalman filter method, to reduce the influence of inaccurate estimation of subfilters on the main filter.Simulation results and comparison analysis prove that the filtering performance of the proposed is better than the traditional federated Kalman filter (FKF) and adaptive FKF, which can improve the accuracy of the integrated navigation system. [ABSTRACT FROM AUTHOR]

This paper proposes a novel variational Bayesian (VB) adaptive Kalman filter with mismatched process noise covariance matrix (PNCM). Firstly, this paper explains the reason why the predicted error covariance matrix (PECM) is chosen for variational inference. Secondly, compared with the earlier VB adaptive Kalman filter (VB‐AKF‐Q), the proposed filter calculate the dynamic model of the PECM with its historical estimation information. Therefore, the proposed filter can overcome the influence of mismatched PNCM on the initial value setting of PECM in the VB‐AKF‐Q. Finally, we use the evidence lower bound for the proposed filter and give the convergence criterion on this basis. Some examples with a target tracking simulation are carried out to demonstrate the superiority of the proposed filter. [ABSTRACT FROM AUTHOR]

In this study, a distributed fuzzy filter is proposed for a non‐linear state estimation problem on the possibilistic framework. Firstly, instead of Gaussian distribution on the probability framework, the process and observation noises are modelled as fuzzy random variables with trapezoidal possibility distributions. Secondly, a novel square root fuzzy cubature information filtering (SRFCIF) algorithm is proposed to deal with non‐linear state estimation with fuzzy noise; a fuzzy variable fusion (FVF) algorithm is used for fuzzy random variables fusion. Consequently, a distributed square root fuzzy cubature information filter (DSRFCIF) is proposed by embedding SRFCF and FVF into the consensus frame. Finally, consistency analysis and simulation demonstration are executed for the proposed filter. [ABSTRACT FROM AUTHOR]

Accurate spacecraft position and velocity is crucial for autonomous navigation technique for deep space mission. For the deep space spacecraft approaching a target planet, the rapid increase in the gravitational pull of the celestial body results in a swift rise in the error of the spacecraft's state integral, thereby making it challenging to accurately estimate the process noise covariance in real-time, which degrades the autonomous navigation system performance. Thus, state estimation for deep space spacecraft during the approach phase under unknown process noise is an important research topic. The traditional adaptive unscented Kalman filter (AUKF) algorithm, relying on measurement innovation, faces challenges in directly capturing abrupt variations in process noise, potentially leading to navigation system divergence. To address the issue of divergence in spacecraft navigation systems and enhance navigation accuracy, this paper proposes an adaptive unscented Kalman filter based on sequential state difference (AUKF-SSD). Additionally, a state-based chi-square test is employed for real-time detection of abrupt variations in process noise. Based on the dynamics model, the AUKF-SSD method is applied to state estimation of Mars spacecraft. Compared with the traditional unscented Kalman filter, the AUKF based on measurement innovation, and the AUKF based on variational Bayesian, the proposed method can effectively restrain estimation errors and solve the divergence problem, in the case of sudden changes in the system process noise. • A novel adaptive Kalman filter is proposed for deep space navigation. • Employs state-based chi-square test for real-time process noise detection. • The proposed method suppresses system divergence. • The effectiveness of the proposed method are verified by simulation experiments. • Offers a promising solution for accurate deep space navigation. [ABSTRACT FROM AUTHOR]

In this study, full adaptive Kalman filters are designed for continuous-time nonlinear fractional-order systems (FOSs) containing unknown parameters and fractional-orders. First, the estimated FOS is discretized using the Grünwald–Letnikov difference method to transform the fractional-order differential equation into a difference equation. Then, in terms of the nonlinear function contained in the investigated system, the Taylor expansion formula is adopted to linearize the discretized equation. Based on the method of augmented vector, an augmented state equation is established by state equations, unknown parameter equations, and fractional-order equation to achieve the state estimation. Besides, the sigmoid function is brought to ensure that the estimation of the fractional-order is performed in a suitable range. Because the covariance matrices of noises are difficult to be measured in physical systems, we also concern the problems on the state estimation, parameter estimation, and fractional-order estimation under the cases that the covariance matrix of process noise is unknown or the covariance matrix of measurement noise is unknown. Considering that the initial value can produce an error of state estimation and parameter identification for the FOS defined under the Caputo sense, the augmented vector method is used to achieve the initial value compensation. Finally, the effectiveness of the proposed algorithms is validated by four examples. [ABSTRACT FROM AUTHOR]

Abstract This study considers an asynchronous multirate data integration problem in the linear state space model with unknown and time-varying statistical parameters of the measurement noises. To improve performance of the multirate adaptive Kalman filter algorithm, a multi-sensor adaptive Kalman filtering algorithm based on variational Bayesian approximations has been developed in an asynchronous multirate multi-sensor integrated navigation system. The proposed filtering algorithm estimates measurement noise variances of the sensors adaptively and also it is robust to anomalous measurements of sensors and however, multirate adaptive Kalman filter is required to use an appropriate algorithm for outlier rejection to achieve a reliable and optimal estimation of position, velocity, and orientation. A navigation system composed of a strapdown inertial navigation system along with Doppler velocity log, inclinometer and depth meter with different sampling rates is designed to evaluate performance of multirate error state Kalman filter (MESKF) and multirate adaptive error state Kalman filter (MAESKF) algorithms and the proposed algorithm. Results of two experimental tests show that the average relative root mean square error (RMSE) of the position estimated by the proposed filtering algorithm can be decreased approximately 57% and 36% when compared to that of MESKF and MAESKF algorithms, respectively. Highlights • A multisensor adaptive Kalman filtering based on variational Bayesian is developed. • Asynchronous multirate multisensor integrated navigation improves performance of MAKF. • Proposed algorithm estimates measurement noise variances of the sensors adaptively. • Experimental results showed that the proposed algorithm is robust to outliers. [ABSTRACT FROM AUTHOR]

During the tracking, kernelized correlation filters may fail as the target is occluded seriously and goes out of view. To solve this problem, a long-term visual tracking algorithm based on adaptive correlation filters is proposed. First, we learn two correlation filters to locate the target and estimate the target scale, respectively. Meanwhile, we learn an independent target appearance correlation filter conservatively updated to know the occlusion degree of the target. Second, we combine the Kalman filter to predict and the support vector machine detector to redetect when tracking failure occurs, caused by the target undergoing severe occlusion or disappearing in the camera view. Third, to solve model drifts due to serious appearance changes of the target, we apply an adaptive model updating strategy to update the correlation filters and classifier. Extensive experimental results on the OTB2013 benchmark dataset demonstrate that our proposed method achieves the excellent overall performance against the nine state-of-the-art methods while running efficiently in real time. © 2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.5.053018] [ABSTRACT FROM AUTHOR]

This paper concerns the application of Huber-based robust unscented Kalman filter (HRUKF) in nonlinear system with non-Gaussian measurement noise. The tuning factor γ is key factor in determining the form of Huber cost function. Traditionally, γ is mainly determined by experience and/or experiments. It is hard to acquire optimal parameter or achieve an optimal filtering. To solve this problem, the influence of tuning factor γ on the performance of HRUKF is analyzed, and then, an adaptive strategy based on projection statistics algorithm for this parameter is proposed to improve filtering performance under the conditions that the measurement noise is contaminated by heavier tails and/or outliers. Simulation results for the problem of Reentry Vehicle Tracking demonstrate the superiority of the proposed method over the traditional ones. [ABSTRACT FROM AUTHOR]

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This paper presents an approach to railway track fault monitoring using signal processing techniques based on signal separation. The measured vibration signal is detected by sensors during the movement of the train on tracks at various speeds. The detected signal can be processed by using digital signal processing techniques. The signal consists of information about the tracks along with noise by various other sources. This noise is estimated by variance and mean values. Initially, the noise is eliminated using an adaptive Kalman filter. Further, to extract a specific frequency band where the peaks of the peaks of the signal are exited, a Finite Impulse Response (FIR) filter is applied. Later the peaks are compared subsequently and the peaks above the threshold of vibrations are detected by using the null space search (NSP) method, which decompose the signal into a series of subcomponents and wastes according to their characteristics. Experimental studies of the signals observed from the railroad during the movement of the train have verified the effectiveness of the current approach for the railway fault monitoring system. [ABSTRACT FROM AUTHOR]

This paper focuses on the application of low-cost sensors for autonomous underwater vehicle (AUV). We propose an intelligent assistance positioning methodology by combining the modified incremental smoothing and mapping (iSAM) and constrained optimally pruned extreme learning machine (OP-ELM) for low-cost sensors, which makes full use of GPS data and produce a variety of correction models. Compared to relinearizing and variable reordering by period batch step in the original iSAM, modified iSAM is implemented variable reordering alone and conducted adaptive relinearization when the value of local Chi-square exceeded a certain threshold. Meanwhile, a novel constrained OP-ELM is presented by mapping the output to the constraint space, which provides full guarantee for generating reliable correction model. When GPS is valid, the constrained OP-ELM is applied to the low-cost sensors to generate correction model. Simultaneous, the correction model of measurement for modified iSAM is also given by this way. Once GPS becomes invalid, the correction models are used to amend the low-cost sensors data and measurement model for getting more accurate location information. Experimental results and analysis show that the proposed method outperforms the traditional algorithm, which RMSE can improve by at most 83.8% than Extended Kalman Filter's (EKF). [ABSTRACT FROM AUTHOR]

An motor torque fault diagnosis for four wheel independent motor-drive vehicle is presented in this paper. Focusing on the fault that the actual motor torques differ from the motor torques commanded by vehicle control unit, the fault parameters are introduced to represent the differences and a nonlinear vehicle dynamics model is built. This model considers vehicle body nonlinear dynamics, wheel dynamics, and tire nonlinear characteristics, and the fault parameters are set to be the coefficients of motor torque commands. Then the motor fault diagnosis problem is converted to these fault parameters identification problem, and unscented Kalman filter is implemented to identify them in real time. With the road test data of a four wheel independent motor-drive vehicle, the fault diagnosis method is testified to be effective both in normal and extreme handling situations. [ABSTRACT FROM AUTHOR]

Kalman filter suffers from performance degradation when applied to dynamic systems with unknown noise statistics. To address this problem, the variational Bayesian noise adaptive Kalman filter (VB-AKF) jointly estimates the state and noise using the variational Bayesian approximation method. In this paper, a modified variational Bayesian noise adaptive Kalman filter (VB-MAKF) is proposed by designing a novel dynamic model for tracking the variances of measurement noise. In the proposed dynamic model, the change in estimated noise variance is utilized to control a continuous and bounded function, which is specifically designed to follow the change in real noise variance, adaptively. We see from the numerical simulations that, in comparison with VB-AKF, the proposed VB-MAKF can achieve higher estimation accuracy of noise variances and thus provide higher estimation accuracy of states. [ABSTRACT FROM AUTHOR]

Maximum likelihood estimation (MLE) has been researched for some acquisition and tracking applications of global navigation satellite system (GNSS) receivers and shows high performance. However, all current methods are derived and operated based on the sampling data, which results in a large computation burden. This paper proposes a low-complexity MLE carrier tracking loop for weak GNSS signals which processes the coherent integration results instead of the sampling data. First, the cost function of the MLE of signal parameters such as signal amplitude, carrier phase, and Doppler frequency are used to derive a MLE discriminator function. The optimal value of the cost function is searched by an efficient Levenberg–Marquardt (LM) method iteratively. Its performance including Cramér–Rao bound (CRB), dynamic characteristics and computation burden are analyzed by numerical techniques. Second, an adaptive Kalman filter is designed for the MLE discriminator to obtain smooth estimates of carrier phase and frequency. The performance of the proposed loop, in terms of sensitivity, accuracy and bit error rate, is compared with conventional methods by Monte Carlo (MC) simulations both in pedestrian-level and vehicle-level dynamic circumstances. Finally, an optimal loop which combines the proposed method and conventional method is designed to achieve the optimal performance both in weak and strong signal circumstances. [ABSTRACT FROM AUTHOR]

The design of unknown-input decoupled observers and filters requires the assumption of an existence condition in the literature. This paper addresses an unknown input filtering problem where the existence condition is not satisfied. Instead of designing a traditional unknown input decoupled filter, a Double-Model Adaptive Estimation approach is extended to solve the unknown input filtering problem. It is proved that the state and the unknown inputs can be estimated and decoupled using the extended Double-Model Adaptive Estimation approach without satisfying the existence condition. Numerical examples are presented in which the performance of the proposed approach is compared to methods from literature. [ABSTRACT FROM AUTHOR]

This paper proposes a novel unified structure to estimate tire forces. The proposed structure uses estimation modules to calculate/estimate tire forces by means of nonlinear observers. The novelty in the proposed approach lies in the independence of the estimates from the vehicle tire model, thereby making the structure robust against variations in vehicle mass, tire parameters due to tire wear, and, most importantly, road surface conditions. In the proposed structure, we have a dedicated module to estimate the longitudinal tire forces and another to calculate the vertical tire forces. Subsequently, these forces are fed into a third module that utilizes a nonlinear observer to estimate lateral tire forces. The proposed structure is validated through experimental studies. [ABSTRACT FROM PUBLISHER]

This paper presents a new method for adaptive estimation of kinematic model error in dynamic aircraft navigation. This method combines the concepts of random weighting and Sage windowing to online monitor predicted and observation residuals to control the influence of the kinematic model?s systematic error on system state estimation. Based on the Sage windowing, random weighting estimations are constructed within a moving time window for the systematic error of the kinematic model as well as the covariance matrices of the observation noise vector, the predicted residual vector, and the predicted state vector. Experimental results and comparison analysis demonstrate that the proposed method not only adjusts the covariance matrices of the observation noise vector and the predicted residual vector, but also effectively controls the influence of the kinematic model error on state parameter estimation, thus improving the navigation accuracy. [ABSTRACT FROM AUTHOR]

The Responsive Orbits division of the Center for Space Research and Assurance in the Aeronautics and Astronautics Department of the Air Force Institute of Technology investigates short-term tactical satellite missions that require frequent maneuvers. Pairing research in designing and conducting avoidance maneuvers with research in detecting and tracking maneuvering satellites sets the stage for developing and improving both areas. Strategies in stochastic filtering, smoothing, multiple model adaptive estimation, and maneuver reconstruction are developed to track a high-priority satellite. The article develops a wide range of simulations that modify the maneuver type, tracking antenna performance, antenna coverage, reconstruction method, filtering approach, and number of postmaneuver passes collected. The results determine the levels of error covariance necessary to perform successful maneuver reconstructions. Additionally, the results provide maneuver reconstruction confidence percentages based on the estimated covariance and number of postmaneuver passes. [ABSTRACT FROM AUTHOR]

In this paper, a generalized robust H∞ filtering method is proposed for a class of singular Markovian jump systems, whose generality is mainly embodied that the desired filter could bear perturbances in terms of uncertainties on its parameter matrices. Firstly, an LMI condition of robust mode-dependent filter is developed. Based on the given result, a new approach to mode-independent H∞ filter is presented, which establishes a direct connection between mode-dependent and mode-independent filters. Secondly, when the transition rate matrix is with elementwise bounded uncertainties or partially unknown, sufficient conditions of such robust mode-dependent and mode-independent filters are all developed within LMI frameworks. Finally, a numerical example is used to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This contribution is concerned with an adaptive control strategy for the mean temperature of an automotive three-way catalytic converter. Tailored finite-dimensional approximations of the complex infinitedimensional mathematical model of the catalytic converter serve as a basis for the design of an extended Kalman filter for state profile estimation and of an adaptive backstepping controller for the mean temperature. Although the model used for observer design relies on (semi-)discretising the infinite-dimensional model, a simple model for the mean temperature employing a phenomenological approach to describe the reaction heat is used for control design. The observer/controller is tested in simulation scenarios using a validated model of the three-way catalytic converter. [ABSTRACT FROM AUTHOR]

A solution based on a Kalman filter to trace the evolution of the atmospheric boundary layer (ABL) sensed by a ground-based elastic-backscatter tropospheric lidar is presented. An erf-like profile is used to model the mixing-layer top and the entrainment-zone thickness. The extended Kalman filter (EKF) enables to retrieve and track the ABL parameters based on simplified statistics of the ABL dynamics and of the observation noise present in the lidar signal. This adaptive feature permits to analyze atmospheric scenes with low signal-to-noise ratios (SNRs) without the need to resort to long-time averages or range-smoothing techniques, as well as to pave the way for future automated detection solutions. First, EKF results based on oversimplified synthetic and experimental lidar profiles are presented and compared with classic ABL estimation quantifiers for a case study with different SNR scenarios. [ABSTRACT FROM PUBLISHER]

In data assimilation applications using ensemble Kalman filter methods, localization is necessary to make the method work with high-dimensional geophysical models. For ensemble square root Kalman filters, domain localization (DL) and observation localization (OL) are commonly used. Depending on the localization method, appropriate values have to be chosen for the localization parameters, such as the localization length and the weight function. Although frequently used, the properties of the localization techniques are not fully investigated. Thus, up to now an optimal choice for these parameters is a priori unknown and they are generally found by expensive numerical experiments. In this study, the relationship between the localization length and the ensemble size in DL and OL is studied using twin experiments with the Lorenz-96 model and a two-dimensional shallow-water model. For both models, it is found that the optimal localization length for DL and OL depends linearly on an effective local observation dimension that is given by the sum of the observation weights. In the experiments no influence of the model dynamics on the optimal localization length was observed. The effective observation dimension defines the degrees of freedom that are required for assimilating observations, while the ensemble size defines the available degrees of freedom. Setting the localization radius such that the effective local observation dimension equals the ensemble size yields an adaptive localization radius. Its performance is tested using a global ocean model. The experiments show that the analysis quality using the adaptive localization is similar to the analysis quality of an optimally tuned constant localization radius. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, vibration database for damage growth monitoring and mitigation (active self-healing) in discrete structures operating with dynamic loads is considered. SHM in this context assumes a known damage location present in the structure and then determines its size at which it is present. The variations in the linear spring stiffness across the structure are considered as an indication of the damage growth. A transient response of the vibrating structure is assumed available to monitor the damage size using an extended Kalman filter algorithm. The growth of this damage is considered mitigated when the potential energy due to mode shapes that take tensile and compressive loads is minimized. These mode shapes are determined by a state feedback controller for which an actuator combination (more than one actuator) is assumed available. A linear spring-mass-damper system is considered to illustrate the active self-healing mechanisms presented in this paper. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Ultracapacitors (UCs) are the focus of increasing attention in electric vehicle and renewable energy system applications due to their excellent performance in terms of power density, efficiency, and lifespan. Modeling and parameterization of UCs play an important role in model-based regulation and management for a reliable and safe operation. In this paper, an equivalent circuit model template composed of a bulk capacitor, a second-order capacitance-resistance network, and a series resistance, is employed to represent the dynamics of UCs. The extended Kalman Filter is then used to recursively estimate the model parameters in the Dynamic Stress Test (DST) on a specially established test rig. The DST loading profile is able to emulate the practical power sinking and sourcing of UCs in electric vehicles. In order to examine the accuracy of the identified model, a Hybrid Pulse Power Characterization test is carried out. The validation result demonstrates that the recursively calibrated model can precisely delineate the dynamic voltage behavior of UCs under the discrepant loading condition, and the online identification approach is thus capable of extracting the model parameters in a credible and robust manner. [ABSTRACT FROM AUTHOR]

The article compared the tracking performance of the Traffic Alert and Collision Avoidance System (TCAS) nonlinear filter against seven different filters. It mentioned the integration of the Kilman filter and the modified Kalman filter into the next-generation TCAS logic and their evaluation on both operational radar data and a high-fidelity airspace encounter model. It found that adding a couple checks can improve performance of the modified Kalmal filter.

ABSTRACT Model-aided navigation increases navigation accuracy by including a vehicle dynamics model into the filter structure. The commonly used Inertial Navigation System (INS) is hence supplemented by another prediction model for the system state. However, the standard Kalman filter only allows for a single system model to propagate the estimation. The main contribution of this paper is the improvement of an existing approach to estimation with two valid state prediction models. By unifying the models, computation time and state vector size are reduced. Furthermore, the question of how the models must be coupled to achieve optimality and preserve filter stability is addressed. In integrated aircraft navigation, an INS as well as a vehicle dynamics model are available. The presented method unifies these two models and shows superior computational performance compared to existing model-aided navigation methods and among best results. Furthermore, it is easy to implement and easy to extend with aiding sensors. Copyright © 2013 Institute of Navigation. [ABSTRACT FROM AUTHOR]

The primary goal of this study is to track a ground-moving target using a machine-vision system installed on an unmanned helicopter, and to estimate its position if the target becomes unobservable. The machine-vision system is accomplished using real-time color images obtained from a charge-coupled device ( CCD) camera mounted on a computer-controlled gimbaled system that can pitch and yaw. To avoid real-time image-tracking failure resulting from a moving target becoming concealed, the Kalman filtering technique is applied to predict the target's follow-on position, so that the camera can continuously track the target. The entire system is initially tested on the ground and then mounted on a helicopter for in-flight testing. The following three cases are shown in the flight tests: (1) an uncovered static target; (2) a moving visible target; and (3) a target that moves in a straight line at a constant speed and becomes temporarily concealed. The vision-based tracking system with the developed algorithm is successfully applied in all three cases. [ABSTRACT FROM AUTHOR]

For all segments and tests, a modified Kalman filter and a quasi-static sensor fusion algorithm were equally accurate (precision and accuracy ~2-3°) compared to normalized least mean squares filtering, recursive least-squares filtering and standard Kalman filtering. The aims were to: (1) compare adaptive filtering techniques used for sensor fusion and (2) evaluate the precision and accuracy for a chosen adaptive filter. Motion sensors (based on inertial measurement units) are limited by accumulative integration errors arising from sensor bias. This drift can partly be handled with adaptive filtering techniques. To advance the measurement technique in this area, a new modified Kalman filter is developed. Differences in accuracy were observed during different tests especially drift in the internal/external rotation angle. This drift can be minimized if the sensors include magnetometers. [ABSTRACT FROM AUTHOR]

The paper deals with a speed estimation of the induction motor using observer with Model Reference Adaptive System and Kalman Filter. For simulation, Hardware in Loop Simulation method is used. The first part of the paper includes the mathematical description of the observer for the speed estimation of the induction motor. The second part describes Kalman filter. The third part describes Hardware in Loop Simulation method and its realization using multifunction card MF 624. In the last section of the paper, simulation results are shown for different changes of the induction motor speed which confirm high dynamic properties of the induction motor drive with sensorless control. [ABSTRACT FROM AUTHOR]

The performance of multichannel coherent maximum-likelihood sequence estimation (MLSE) reception in the presence of co-channel interference is limited by the channel estimation accuracy. An adaptive channel estimation approach is developed which improves the performance through interference cancellation. Significant performance gains (up to 8 dB) are demonstrated for the Digital Advanced Mobile Phone Service (D-AMPS) (IS-136) digital cellular system [ABSTRACT FROM PUBLISHER]

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter. [ABSTRACT FROM AUTHOR]

A robust Kalman filter is proposed for time-varying discrete-time linear systems with uncertainties in state, input noise, and measurement matrices. The filter is obtained by solving an optimisation problem such that the upper bound on the variance of estimation error to be minimised for all admissible uncertainties. A numerical example is presented to show the performance of the proposed robust filter. [ABSTRACT FROM AUTHOR]