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Combining a global navigation satellite system (GNSS) and microelectromechanical technology-based inertial system (MINS) has become essential in land vehicle navigation systems. In an urban environment, the error of position output by GNSS receiver increases because of the blocking and reflection of the signal by buildings, which affects the positioning accuracy of the integrated system. In order to improve the positioning accuracy under these scenarios, this paper proposed a new robust method based on the estimation of the standard deviation of GNSS positioning error. In our study, a method for estimating the standard deviation ${\boldsymbol{\sigma}}^p$ (or variance) of the position error of GNSS receiver is firstly proposed. Then a robust filtering method combined multi-factor scaling and bias estimation is proposed based on the estimation of ${\boldsymbol{\sigma}}^p$. The simulations and vehicle navigation test show that the proposed method has a robust positioning effect. When ${\boldsymbol{\sigma}}^p$ increases, the position accuracy is closed to the Sage-Husa filtering method with boundary constraint (SH-KF). Simulations also indicate that the proposed method has better robust effect than the traditional Kalman filter and SH-KF when a large bias error of position exists. [ABSTRACT FROM AUTHOR]

The calibration accuracy of the Doppler Velocity Log (DVL) error directly affects the performance of the SINS/DVL integrated navigation system. Previous studies of DVL calibration have not dealt with DVL measurement outliers. Therefore, in this paper, a novel DVL calibration method is proposed to reduce the influence of outliers on calibration accuracy. Introducing the Lie group theory to the DVL calibration study for the first time, and taking advantage of the Special Orthogonal Group of order 3 [SO(3)] representation, a new DVL calibration model based SO(3) group with the ability to represent large misalignment angles is established. And an invariant extended Kalman filter (IEKF) design for the DVL calibration model is introduced. Then, a robust IEKF is proposed by combining the linear error propagation equation of the SO(3)-based DVL calibration model with the statistical similarity measure (SSM) theory. The proposed robust IEKF algorithm enriches the IEKF theory and has the ability to accurately estimate the state of the Lie group model in outlier environments. The simulation and lake trial are used to illustrate the effectiveness and superiority of the proposed DVL calibration method. [ABSTRACT FROM AUTHOR]

In this paper, we consider a mobile molecular communication (MC) system consisting of two mobile nanomachines, a transmitter and a receiver, propelled by a positive drift velocity and Brownian motion in a realistic blood-vessel-type flow regime. Considering the nonlinear movement of the nanomachines, an extended Kalman filter is employed to estimate the distance from the transmitter. Furthermore, based on the predicted distance, to keep the number of received molecules for bit 1 at a stable level, we employ power control on the number of transmitted molecules based on the distance between the transmitter and the receiver and the residual molecules in the channel from the previous transmission. Finally, the optimal detection threshold is obtained by minimizing the error probability. It is verified that a fixed optimal detection threshold can be effective for the power control scheme in the mobile MC. The bit error rate (BER) performance of our scheme is verified via simulation results. [ABSTRACT FROM AUTHOR]

The available road grade information makes a significant impact on improving the quality of vehicle control. In order to solve the limited application scenario and insufficient accuracy of current road grade estimation methods, this paper presents a novel model-based road grade estimation approach. First, a Quaternion Unscented Kalman Filter (QUKF) using the three-axle angular velocities and three-axle accelerations from a low-cost Inertial Measurement Unit (IMU) and the vehicle speed from CAN bus is designed to estimate the pitch angle of the vehicle. In particular, the measurement noise of UKF is analyzed by integrating Allan variance method. Second, a simplified vehicle-road model is derived to represent the road grade with the estimated pitch angle and longitudinal acceleration. Then, the performance of the proposed algorithm is tested by co-simulation of MATLAB/Simulink and CarSim, which indicates that the error rate of estimation is within 4%. Finally, the feasibility and accuracy of the proposed method implemented in the embedded prototype are verified in experiments conducted on standard slopes. [ABSTRACT FROM AUTHOR]

In this paper, adaptive quantized state estimation fusion is deeply studied. To approach the model mismatching problem induced by random quantization, some quantized Kalman filters have been presented in the previous work, such as the quantized Kalman filter with strong tracking filtering (QKF-STF), the variational Bayesian adaptive quantized Kalman filter (VB-AQKF), and a centralized fusion frame-based complex quantized filter called variational Bayesian adaptive QKF-STF (VB-AQKF-STF). Based on the previous work for the single sensor system, a distributed complex quantized filter is designed in this paper. A novel quantized Kalman filter based on multiple-method fusion scheme (QKF-MMF) is proposed. Similar to the VB-AQKF-STF, the QKF-MMF can also realize joint estimation on the state and the quantization error covariance under the distributed fusion frame. Furthermore, it extends the single sensor results to multisensor tracking systems by using centralized and distributed fusion frames. Two multisensor quantized fusion estimators are proposed for a parallel structure with main-secondary processors in the fusion center. The weighted fusion and embedded integration ways are deeply applied to design the multisensor quantized fusion methods. The proposed work can perfect the quantized estimation algorithms and provide different choices for practical engineering applications. [ABSTRACT FROM AUTHOR]

This paper presents an estimation approach within the framework of uplink massive machine-type-communications (mMTC) that considers the energy limitations of the devices. We focus on a scenario where a group of sensors observe a set of parameters and send the measured information to a collector node (CN). The CN is responsible for estimating the original observations, which are spatially correlated and corrupted by measurement and quantization noise. Given the use of Gaussian sources, the minimum mean squared error (MSE) estimation is employed and, when considering temporal evolution, the use of Kalman filters is studied. Based on that, we propose a device selection strategy to reduce the number of active sensors and a quantization scheme with adjustable number of bits to minimize the overall payload. The set of selected sensors and quantization levels are, thus, designed to minimize the MSE. For a more realistic analysis, communication errors are also included by averaging the MSE over the error decoding probabilities. We evaluate the performance of our strategy in a practical mMTC system with synthetic and real databases. Simulation results show that the optimization of the payload and the set of active devices can reduce the power consumption without compromising the estimation accuracy. [ABSTRACT FROM AUTHOR]

Nowadays, the availability of accurate vehicle position becomes more and more indispensable. The GNSS/INS (Global Navigation Satellite Systems/Inertial Navigation System) is currently the most widely-used integrated navigation scheme for land vehicles, which is capable of provide high-accuracy and continuous positioning results in the open-sky environments. However, under the GNSS-denied conditions, the existing GNSS/INS integrated system often fails to provide reliable positioning results due to various and nonlinear errors contained in the MEMS (Micro-Electro-Mechanical System) IMU (Inertial Measurement Unit) measurements. To improve the positioning accuracy during GNSS outage, deep learning has been introduced into the GNSS/INS integrated system in recent years. In this paper, we propose a residual attention network-based confidence (i.e., measurement noise covariance) estimation algorithm for non-holonomic constraint in GNSS/INS integrated navigation system, which adopts a residual attention network to dynamically estimate the noise covariance of the pseudo-observation (i.e., non-holonomic constraint) for optimal Kalman filtering (KF) fusion. To emphasize the more representative features with larger weights for accurate noise covariance estimation, we introduce an attention mechanism to automatically assign proper weights to the learned features according to their contributions. We evaluate our proposed method on three practical road datasets and compare it with other seven methods including the traditional KF, Pure INS, KF with three deep learning networks, K-means, and the Input-Delayed Neural Networks based method. Extensive experimental results demonstrate that our proposed RA-NHC bounds the errors associated with velocities and achieves reasonable accuracy improvement in position and velocity estimation. [ABSTRACT FROM AUTHOR]

In heavy-duty vehicles, multiple signals are available to estimate the vehicle's kinematics, such as Inertial Measurement Unit (IMU), Global Positioning System (GPS) and linear and angular speed readings from wheel tachometers on the internal Controller Area Network (CAN). These signals have different noise variance, bandwidth and sampling rate (being the latter, possibly, irregular). In this paper we present a non-linear sensor fusion algorithm allowing asynchronous sampling and non-causal smoothing. It is applied to achieve accuracy improvements when incorporating odometry measurements from CAN bus to standard GPS+IMU kinematic estimation, as well as the robustness against missing data. Our results show that this asynchronous multi-sensor (GPS+IMU+CAN-based odometry) fusion is advantageous in low-speed manoeuvres, improving accuracy and robustness to missing data, thanks to non-causal filtering. The proposed algorithm is based on Extended Kalman Filter and Smoother, with exponential discretization of continuous-time stochastic differential equations, in order to process measurements at arbitrary time instants; it can provide data to subsequent processing steps at arbitrary time instants, not necessarily coincident with the original measurement ones. Given the extra information available in the smoothing case, its estimation performance is less sensitive to the noise-variance parameter setting, compared to causal filtering. Working Matlab code is provided at the end of this work. [ABSTRACT FROM AUTHOR]

Accurate and reliable capacity estimation for lithium-ion batteries adapting to uncertain environment plays a significant role in the fields of electric vehicles and renewable energy systems. This paper proposes a novel capacity fusion estimation approach based on three-parameter capacity fade model and constant current charging curves using dual unscented Kalman filters. In view of the battery cycle aging problem under variable temperature situation, a three-parameter capacity fade model is introduced to continuously estimate the battery cell capacity for unknown changes of ambient temperature. Nevertheless, the model parameters may mismatch in practical applications, so the first unscented Kalman filter views the discrete capacity based on constant current charging curves as observations to periodically update the parameters. Thereafter, the second one performs a fusion operation depending on the capacity estimated by constant current charging curves and the three-parameter capacity fade model with the corrected parameters, so as to improve the capacity estimation accuracy online. The results show that the fusion estimation errors of capacity are controlled within 2% by updating the model parameters, and the robustness of the proposed method is also validated. [ABSTRACT FROM AUTHOR]

In this paper, a vehicle sideslip angle (VSA) estimation method is presented and experimentally verified by fusing vehicle kinematics and dynamics information with fuzzy logic. First, a vehicle-kinematics-based (VK-based) reduced inertial navigation system (R-INS) is developed to calculate the VSA, velocities, and attitude. Then, with the velocities measured by a single-antenna global navigation satellite system (GNSS), a velocity-based Kalman filter (VBKF) is employed to estimate the velocity, attitude, and gyro bias errors in the R-INS and these errors will be adopted to correct accumulated errors of the R-INS. However, the heading error in the R-INS which is highly correlated with the VSA is not well observable when the vehicle is with low excitation. To address this observability issue, the vehicle-dynamics-based (VD-based) VSA estimation approach is leveraged to augment the heading error into the velocity measurements of the VBKF by a novel heading error measurement model, and an augmented Kalman filter (AKF) is designed. Next, according to the vehicle lateral excitation, a fuzzy logic method is proposed to fuse the heading errors from both the VBKF and AKF to take advantage of the VK-based and VD-based methods. Finally, a comprehensive experimental test is conducted, and the results confirm that the heading error observability issue in the VBKF has been tackled by fusing vehicle kinematics and dynamics and the VSA estimation accuracy has been advanced. The VSA estimation accuracy of the proposed method (the absolute mean error is only 0.126°) matches our previous work, which needs a dual-antenna GNSS, but the cost is reduced. [ABSTRACT FROM AUTHOR]

This paper investigates the path-tracking control issue for autonomous ground vehicles with the integral sliding mode control (ISMC) considering the transient performance improvement. The path-tracking control is converted into the yaw stabilization problem, where the sideslip-angle compensation is adopted to reduce the steady-state errors, and then the yaw-rate reference is generated for the path-tracking purpose. The lateral velocity and roll angle are estimated with the measurement of the yaw rate and roll rate. Three contributions have been made in this paper: first, to enhance the estimation accuracy for the vehicle states in the presence of the parametric uncertainties caused by the lateral and roll dynamics, a robust extended Kalman filter is proposed based on the minimum model error algorithm; second, an improved adaptive radial basis function neural network (RBFNN) considering the approximation error adaptation is developed to compensate for the uncertainties caused by the vertical motion; third, the RBFNN and composite nonlinear feedback (CNF) based ISMC is developed to achieve the yaw stabilization and enhance the transient tracking performance considering the input saturation of the front steering angle. The overall stability is proved with Lyapunov function. Finally, the superiority of the developed control strategy is verified by comparing with the traditional CNF with high-fidelity CarSim-MATLAB simulations. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive extended Kalman filter (AEKF) algorithm estimating the stator stationary axis components of stator currents, the stator stationary axis components of rotor fluxes, the rotor mechanical speed, and the load torque for speed-sensorless control applications of induction motors (IMs). The performance of a standard extended Kalman filter (SEKF) algorithm depends on the correct selection of system and measurement noise covariance matrices. In SEKF algorithms, these matrices are generally assumed as constant and determined by the trial-and-error method. However, they are affected by the operating conditions of IM and should be updated considering the operating conditions. For this purpose, instead of the time-consuming trial-and-error method for determining these matrices, an innovation-based adaptive estimation approach having the capability of online update is used in this paper. Finally, in order to verify the superiority of the AEKF algorithm, estimation performances of AEKF and SEKF algorithms are compared under challenging scenarios for a wide speed range considering computational burdens and parameter variations. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to enhance a random bearing vibration signal (repetitive character with a random part) corrupted by an oscillatory gear vibration signal. This approach, called the Taylor- $H_{\infty }$ (TH) estimator, is proposed in a variable speed condition. The latter is designed using the mathematical modeling of the raw signal in the state space. It is used to remove the oscillatory signal from the raw signal. The obtained residual signal is the enhanced signal of the bearing contribution. Different from the Vold–Kalman (VK) estimator, which results from the minimization of the mean squared error, the proposed method is based on the minimax optimization. The minimax approach for the TH estimator leads to the minimization of the estimation error for the worst possible amplification of the random signals. No prior knowledge about the statistical properties of the random signals is required. The proposed approach is assessed using both synthetic and experimental nonstationary vibration data. In addition, the squared envelope spectrum of the residual signal is analyzed to diagnose the bearings state. This paper shows the [ABSTRACT FROM AUTHOR]

In recent times, lithium iron phosphate (LFP) batteries have found wide usage in electric vehicles (EVs). Efficient utilization of the storage capacity of any battery used in EVs is heavily dependent on the accuracy of state of charge (SOC) estimation. The classical SOC-OCV (open circuit voltage) based approach for estimation of SOC is quite ineffective under rapid variation in operating condition of EVs. The impact of parameter variations and unknown disturbances due to widely varying environmental conditions and drive terrain, demands a robust observer for SOC estimation. In this regard, this paper proposes a novel SOC estimation technique using a nonlinear fractional-order unknown input observer (FOUIO). The scheme based on the combined framework of fractional calculus and state estimation with unknown input is able to meet the challenges of SOC estimation in EVs by decoupling the plant disturbance inputs from the state estimation process. The optimally designed FOUIO using a linear matrix inequality (LMI) based formulation has been analytically proved for convergence. The proposed FOUIO has been validated and compared with reported SOC estimation schemes for different drive profiles in terms of accuracy and convergence speed. The numerical simulations and hardware in loop (HIL) based experimental results confirm the effectiveness of the proposed FOUIO in estimating the SOC robustly under different scenarios of battery operation. [ABSTRACT FROM AUTHOR]

Battery management system (BMS) is one of the key subsystems of electric vehicle, and the battery state-of -charge (SOC) is a crucial input for the calculations of energy and power. Therefore, SOC estimation is a significant task for BMS. In this paper, a new method for online estimating SOC is proposed, which combines a novel adaptive extended Kalman filter (AEKF) and a parameter identification algorithm based on adaptive recursive least squares (RLS). Specifically, according to the first order R-C network equivalent circuit model, the battery model parameters are identified online using the RLS with multiple forgetting factors. Based on the identified parameters, the novel AEKF is used to accurately estimate the battery SOC. The online identification of parameter tracks the varying model. At the same time, due to the novel AEKF algorithm to dynamically adjust the system noise parameter, excellent accuracy of the SOC real-time estimation is obtained. Experiments are set to evaluate the accuracy and robustness of the proposed SOC estimation method. The simulation test results indicate that under DST and UDDS conditions, the maximum absolute errors are less than 0.015 after filtering convergence. In addition, the maximum absolute error is less than 0.02 in the simulation of DST with current and voltage measurement noise, so is in DST with current offset sensor error. The tests indicate that the proposed method can accurately estimate battery SOC and has strong robustness. [ABSTRACT FROM AUTHOR]

In this paper, the event-triggered distributed Kalman filtering problem is considered for a class of networked multisensor fusion systems (NMFSs) under sensor energy and network bandwidth constraint. A general event-triggered scheme is employed for the NMFSs to reduce the energy consumption and communication burden between the sensor nodes and fusion center (FC) under the communication bandwidth constraints. Local estimation information is allowed to transmit partial components to FC over the network with limited bandwidth. A group of binary variables are introduced to describe the component transmitting process when the triggering condition is violated. Furthermore, the untransmitted local estimation signals are compensated by the previous transmitted one, and a recursively event-triggered distributed fusion Kalman filter in the linear minimum mean square error sense is designed from the restructured local unbiased estimators. At each time instant, a set of binary variables are determined by an optimal judgement criterion. Finally, a simulation example is provided to illustrate the effectiveness and advantages of the proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, an enabling multi-sensor fusion-based longitudinal vehicle speed estimator is proposed for four-wheel-independently-actuated electric vehicles using a Global Positioning System and Beidou Navigation Positioning (GPS-BD) module, and a low-cost Inertial Measurement Unit (IMU). For accurate vehicle speed estimation, an approach combing the wheel speed and the GPS-BD information is firstly put forward to compensate for the impact of road gradient on the output horizontal velocity of the GPS-BD module, and the longitudinal acceleration of the IMU. Then, a multi-sensor fusion-based longitudinal vehicle speed estimator is synthesized by employing three virtual sensors which generate three longitudinal vehicle speed tracks based on multiple sensor signals. Finally, the accuracy and reliability of the proposed longitudinal vehicle speed estimator are examined under a diverse range of driving conditions through hardware-in-the-loop tests. The results show that the proposed method has high estimation accuracy, robustness, and real-time performance. [ABSTRACT FROM AUTHOR]

Railway traction vehicles transfer forces between wheels and rails through an adhesion coefficient. The adhesion coefficient value can decrease abruptly during the train run. Therefore, the wheels can simply gain high value of the wheels slip velocity. Thus, the vehicles are equipped with slip controllers for this case. During the past decades, many types of slip control strategies were developed, and the new methods are also developed today. Some perspective methods are based on an estimation of the adhesion coefficient or an adhesion force. However, correct operation of these methods is not guaranteed in all cases. Moreover, these methods have some weakness that can decrease their efficiency. Therefore, a novel method based on the adhesion condition estimation is presented in this paper. The adhesion condition is estimated by an unscented Kalman filter. When the method is connected to a controller, it is possible to eliminate the rising wheel slip velocity at its beginning and limit it to the appropriate value. The output of the method is directly used as an input of the controller without any additional calculation as it is used in traditional methods. The input signal does not need any additional filtration, and the method does not require information about the actual train velocity for its proper work. The verification of the method is done with the measured data that were measured on a freight train hauled by an electric locomotive. This paper also presents simulation results of the method with a controller based on the locomotive mathematical model. [ABSTRACT FROM AUTHOR]

As a common land vehicle motion constraint used in GNSS/INS integration system, the non-holonomic constraint (NHC) can effectively inhibit the navigation divergence of standalone inertial navigation system (INS) especially for micro-electro-mechanical system (MEMS) during GNSS signal outages. The correct utilization of non-holonomic constraint requires a compensation of lever arm between the IMU center and the valid point of this constraint on the vehicle. Non-holonomic constraint is not always valid for anywhere on the vehicle since rotation of the vehicle may produce a corresponding lateral/vertical velocity in motion. However, the lever arm impact to non-holonomic constraint has not been paid attention sufficiently and been researched thoroughly. The purpose of this paper is to investigate the effect of the lever arm error on the auxiliary ability of the non-holonomic constraint. The lateral/vertical velocity and additional position drift error during GNSS outages caused by the lever arm error were studied. Two integrated navigation systems with different grade of IMUs were used to analyze the accuracy requirements of the lever arm. Analysis results show that the forward lever arm is the most important influence factor, and the lever arm error should be controlled within sub-decimeter to ensure the accuracy and reliability of the non-holonomic constraint assistance. [ABSTRACT FROM AUTHOR]

This paper presents a parameter estimation technique for a variable-speed wind turbine with permanent magnet synchronous generator and back-to-back voltage source converter. The proposed technique applies the cubature Kalman filter for the joint estimation of the system dynamic state and a modified set of parameters from which the original model parameters can be algebraically recovered. To the authors knowledge, this work is the first attempt to apply such an innovative technique to a PMSM detailed estimation model including the control parameters of the voltage source converter. [ABSTRACT FROM AUTHOR]

Objective: The objective of this paper is to obtain accurate estimation of breathing rate (BR), using only the electrocardiogram (ECG) or the photoplethysmogram (PPG) signals, to avoid wearing cumbersome and uncomfortable sensors for direct measurements. Methods: Several respiration waveforms are derived from ECG or PPG signals based on amplitude, frequency, and baseline wander modulations. It is, however, difficult to determine their optimal combination for BR estimation due to the noise and patient specificity. We first propose to quantify the quality of modulation waveforms using respiratory quality indices (RQIs). We then present two methods: the first automatically selects the modulation signal with highest RQI for BR estimation, and the second tracks the respiration signal using the Kalman smoother to fuse modulation signals with highest RQI. Results: These two methods are evaluated on two independent datasets, one benchmark database (DB) with immobilized patients recordings and the second with those performing daily activities. Our results outperform existing methods in the literature in both the cases. Conclusion: Experimental results show that the RQIs coupled with a fusion algorithm increases the accuracy for BR estimations in dealing with derived modulation signals. Significance: This work describes a robust Kalman Smoother method applicable in multiple clinical contexts to improve breathing rate estimation from data fusion. [ABSTRACT FROM AUTHOR]

A novel robust Rauch–Tung–Striebel smoothing framework is proposed based on a generalized Gaussian scale mixture (GGScM) distribution for a linear state-space model with heavy-tailed and/or skew noises. The state trajectory, mixing parameters, and unknown distribution parameters are jointly inferred using the variational Bayesian approach. As such, a major contribution of this paper is unifying results within the GGScM distribution framework. Simulation and experimental results demonstrate that the proposed smoother has better accuracy than existing smoothers. [ABSTRACT FROM AUTHOR]

This paper considers nonlinear state estimation subject to inequality constraints in the form of linear and linear-matrix inequalities. Rewriting the standard maximum likelihood objective function used to derive the Kalman filter allows the Kalman gain to be found by solving a constrained optimization problem with a linear objective function subject to a linear-matrix-inequality constraint. Additional constraints, such as weighted-norm- or linear-inequality constraints, that the state estimate must satisfy are easily augmented to the constrained optimization problem. The proposed constrained estimation methodology is applied in the extended Kalman filter (EKF) and sigma point Kalman filter (SPKF) frameworks. Motivated by estimation problems involving a vehicle that can rotate and translate in space, multiplicative versions of the constrained EKF and SPKF formulations are discussed. Simulation results for a ground-based mobile robot operating in a constrained three-dimensional terrain are presented and are compared to results that use the traditional multiplicative EKF and SPKF, as well as filters that enforce inequality constraints by simply projecting the state estimate into the constrained domain along the shortest Euclidean distance. [ABSTRACT FROM AUTHOR]

This paper presents a new filter estimating quaternion using inertial and magnetic sensors. Using a reference coordinate system multiplicative quaternion error representation and a constrained structure filter gain, the proposed filter has a separation property, where the magnetic sensor output does not affect pitch and roll angle estimation. Furthermore, the proposed filter gain can be computed just from five scalar equations. Through simulation, the separation property of the proposed filter is verified. [ABSTRACT FROM AUTHOR]

Objective: Measuring three-dimensional (3-D) foot trajectories with foot-worn inertial measurement units (IMUs) is essential for a variety of applications, such as gait analysis and fall risk assessment. IMU-based foot trajectory is usually reconstructed by double integrating the global coordinate acceleration, in which drifts of signals are accumulated and lead to unbounded error increase. To reduce drift errors, a smoother-based method is proposed in this paper. Methods: The smoother-based method not only corrects initial values of integrations, but also smooths integrating processes through a backward update. Both the orientation estimation and the velocity estimation are improved in this concept, which contribute to the improvement of the trajectory estimation. Results: The final results are compared with an optical motion capture system as reference. Accuracy is evaluated with ground level walking of nine adult participants, 2302 strides in total. Errors are reduced by 62% on stride length and 44% on stride width of the estimation without our method, with final errors $-\text{0.24}\pm \text{1.11}\ \text{cm}$ and $-\text{0.02}\pm \text{0.95}\ \text{cm}$. Conclusion/Significance: Results prove that our method can improve the accuracy of 3-D foot trajectory estimation. Furthermore, this smoother-based method can reduce drift-related errors when estimating trajectories, which will allow it to expand its applications into other IMU-based measurements. [ABSTRACT FROM AUTHOR]

The availability of real-time information about the biological activity is a valuable matter in wastewater treatment employing activated sludge processes, and this is represented by the oxygen uptake rate (OUR). In these processes, OUR measurements have been used as a fast indicator of changes in waste load or influx of toxic organic compounds and for monitoring of the process efficiency. In this paper, we propose a method for OUR estimation by using Kalman filters and PWM control of the dissolved oxygen (DO) concentration in the activated sludge reactor. The proposed method presents a shorter response time than the standard online procedure and a lower measurement uncertainty compared to a previous feedback control method, which estimates the OUR from values of the applied control signal and the DO concentration measurements. Simulations are presented to illustrate the method and experiments are performed in a bench scale activated sludge reactor to show the applicability of the proposed estimator in a real system. The results confirm the fast response of the proposed method and its consistency with respect to the standard procedure for OUR computation. [ABSTRACT FROM AUTHOR]

Recent studies on optical flow typically focus on the estimation of the single flow field in between a pair of images but pay little attention to the multiple consecutive flow fields in a longer video sequence. In this paper, we propose an efficient video optical flow estimation method by exploiting the temporal coherence and context dynamics under a Kalman filtering system. In this system, pixel’s motion flow is first formulated as a second-order time-variant state vector and then optimally estimated according to the measurement and system noise levels within the system by maximum a posteriori criteria. Specifically, we evaluate the measurement noise according to the flow’s temporal derivative, spatial gradient, and warping error. We determine the system noise based on the similarity of contextual information, which is represented by the compact features learned by pre-trained convolutional neural networks. The context-aware Kalman filtering helps improve the robustness of our method against abrupt change of light and occlusion/dis-occlusion in complicated scenes. The experimental results and analyses on the MPI Sintel, Monkaa, and Driving video datasets demonstrate that the proposed method performs favorably against the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

To achieve high-performance control of modern dc–dc converters, using direct digital design techniques, an accurate discrete model of the converter is necessary. In this paper, a new parametric system identification method, based on a Kalman filter (KF) approach is introduced to estimate the discrete model of a synchronous dc–dc buck converter. To improve the tracking performance of the proposed KF, an adaptive tuning technique is proposed. Unlike many other published schemes, this approach offers the unique advantage of updating the parameter vector coefficients at different rates. The proposed KF estimation technique is experimentally verified using a Texas Instruments TMS320F28335 microcontroller platform and synchronous step-down dc–dc converter. Results demonstrate a robust and reliable real-time estimator. The proposed method can accurately identify the discrete coefficients of the dc–dc converter. This paper also validates the performance of the identification algorithm with time-varying parameters, such as an abrupt load change. The proposed method demonstrates robust estimation with and without an excitation signal, which makes it very well suited for real-time power electronic control applications. Furthermore, the estimator convergence time is significantly shorter compared to many other schemes, such as the classical exponentially weighted recursive least-squares method. [ABSTRACT FROM AUTHOR]

This paper presents how the time-differenced carrier phase (TDCP) can be implemented in nondifferential global positioning system/inertial measurement unit (GPS/IMU) tightly coupled navigation systems to improve positioning performance. The TDCP is expressed as a conventional TDCP, representing the carrier phase difference between two successive GPS epochs, and a modified TDCP, which is defined in this paper as the carrier phase difference between current and reference GPS epochs. Both of these two TDCP expressions are implemented in a GPS/IMU tightly coupled navigation Kalman filter as observations: The conventional TDCP with a more accurate approximation of the velocity integration is applied to estimate velocity, whereas the modified TDCP is used for position estimation. When initialized with a high-accuracy reference position, the proposed navigation strategy can achieve submeter positioning accuracy in the nondifferential mode without estimating ambiguities. Compared with the conventional TDCP-only method, the inherent position drift problem of the TDCP observations can also be avoided. The absolute positioning accuracy of the proposed conventional+modified TDCP measurement update method is determined by the reference position, since the TDCP, including conventional and modified TDCP, is a relative measurement. However, even when initialized with a low-accuracy reference position, the relative positioning accuracy (or precision) of the proposed method is still very high, and the reference positioning accuracy can be maintained, owing to the proposed method. The proposed method can greatly improve the positioning accuracy, as compared with traditional pseudorange+Doppler GPS/IMU tightly coupled navigation systems. [ABSTRACT FROM PUBLISHER]

Despite the importance of sparsity signal models and the increasing prevalence of high-dimensional streaming data, there are relatively few algorithms for dynamic filtering of time-varying sparse signals. Of the existing algorithms, fewer still provide strong performance guarantees. This paper examines two algorithms for dynamic filtering of sparse signals that are based on efficient $\ell _1$ optimization methods. We first present an analysis for one simple algorithm (BPDN-DF) that works well when the system dynamics are known exactly. We then introduce a novel second algorithm (RWL1-DF) that is more computationally complex than BPDN-DF but performs better in practice, especially in the case where the system dynamics model is inaccurate. Robustness to model inaccuracy is achieved by using a hierarchical probabilistic data model and propagating higher-order statistics from the previous estimate (akin to Kalman filtering) in the sparse inference process. We demonstrate the properties of these algorithms on both simulated data as well as natural video sequences. Taken together, the algorithms presented in this paper represent the first strong performance analysis of dynamic filtering algorithms for time-varying sparse signals as well as state-of-the-art performance in this emerging application. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

This paper presents a new method for estimating the rate of change of frequency (RoCoF) of voltage or current signals measured using instrument transformers. The method is demonstrably superior to currently available methods in the literature, in terms of estimation latency and estimation error. The estimation is performed in two steps. In the first step, the analog voltage or current signal obtained from an instrument transformer is statistically processed using interpolated discrete Fourier transform (IDFT) in order to obtain the means and variances of the signal parameters. These means and variances are then given as inputs to the second step, in which Kalman filtering (KF) is used to find the final RoCoF estimate. Accurate mathematical expressions for the means and variances of signal parameters have been derived and used in the second step, which is the main reason behind the superior performance of the method. The applicability of the method has been demonstrated on a benchmark power system model. [ABSTRACT FROM AUTHOR]

Online State of Charge (SoC) and State of Health (SoH) estimations are essential for efficient, safe, and reliable operation of Lithium ion batteries. Based on the first-order equivalent-circuit model (ECM), a multi-scale extended Kalman filter is adopted in this paper to estimate ECM parameters and battery SoC using dual time scales. The nature of the battery excitations significantly influences the estimation performance. When the input–output data, i.e., the input current and output voltage, is insufficiently rich in frequency content, the estimation performance is poor. Thus, the excitation current should be optimized for the accurate estimation of parameters and states. A Cramer–Rao bound analysis is conducted considering voltage noise, current amplitude, and current frequency, which shows the loss of accuracy in multi-parameter estimation (estimating all states and parameters) when compared to single-parameter estimation (estimating only one parameter/state). However, it also shows that the loss of accuracy can be significantly reduced when the excitation current is carefully chosen to satisfy certain criteria. Both simulation and experimental results verify the analysis results and show that a current profile with optimal frequency components achieves the best estimation performance, thereby, providing guidelines for designing battery current profiles for improved SoC and SoH estimation performance. [ABSTRACT FROM AUTHOR]

In this paper, a time-frequency (TF) jammer mitigation algorithm based on a Kalman filter is proposed for global navigation satellite system (GNSS) receivers. A new state-space representation of the received signal is formed considering the rapidly changing characteristics of the jamming signal. Using the instantaneous frequency (IF) and chirp rate estimates obtained by the TF analysis, the proposed algorithm applies a Kalman filter for jamming signal estimation. The estimated jamming signal is then subtracted from the received data to remove the jammer signal. To cope with the time-varying accuracy in the jammer parameter estimates, the methods for adaptively adjusting the process and measurement noise variances using chirp rate estimates are described. Simulations are performed to verify the effectiveness of the proposed method for mitigating various types of jammers. The proposed approach improves the jammer signal removal performance by minimizing the effect of the Kalman filter model mismatch when compared to other techniques reported in the literature. [ABSTRACT FROM AUTHOR]

Several modeling, estimation, and control strategies have been recently presented for simple reluctance devices like solenoid valves and electromagnetic switches. In this paper, we present a new algorithm to online estimate the flux linkage and the electrical time-variant parameters of these devices, namely the resistance and the inductance, only by making use of discrete-time measurements of voltage and current. The algorithm, which is robust against measurement noise, is able to deal with temperature variations of the device and provides accurate estimations during the motion of the armature. Additionally, an integral estimator that uses the start of each operation of the actuator as reset condition has been also implemented for comparative purposes. The performances of both estimation methods are studied and compared by means of simulations and experimental tests, and the benefits of our proposal are emphasized. Possible uses of the estimates and further modeling developments are also described and discussed. [ABSTRACT FROM AUTHOR]

Asynchronous multisensor systems have been widely equipped on various host platforms to meet the requirements of modern navigation campaigns. However, the existing asynchronous fusion algorithms are not efficient in many challenged environments, due to the limitations of communication condition, out-of-sequence observation, and computation affordance. To solve these problems, this paper proposes a batch state asynchronous fusion algorithm with feedback for distributed architecture systems. It is optimal in the sense of minimum mean-squared estimation and its fusion solution is rigorously derived by fully considering the correlations of sequential/cross channels and one-step prediction information. The final fusion solution is straightforward expressed with the combinations of multiple local Kalman filtering estimates outputs during one fusion period. Moreover, no additional interpolation, extrapolation or synchronization procedures are required. Furthermore, to alleviate the computational burden of our proposed algorithm, its approximation form is also given by replacing the weighting matrix by the weighting scalar. Simulations are carried out to illustrate the efficiency and the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Wildfires play an important role in forest management, while an accurate assessment of current wildfire status is imperative for fire management. In this paper, a Kalman filter-based methodology is proposed to estimate the wildfire progress with online measurements that are collected by a system of unmanned aerial vehicles. The method is developed to estimate the wildfire propagation behavior as well as fire front contour. It is enabled by developing a scalar field wildfire model and utilizing the Kalman filter to estimate the parameters of the model. In addition, an uncertainty function is developed to describe the performance of the estimation results. The effectiveness of the algorithm is demonstrated in an independent fire simulation environment. [ABSTRACT FROM AUTHOR]

This paper presents a novel filtering technique to estimate the position of a moving target based on discrete-time direction and velocity measurements. The velocity is assumed to be corrupted by an unknown constant bias, which is explicitly estimated in the process. A nonlinear system is first designed, describing the dynamics and observations associated to the target, followed then by a state augmentation that yields an equivalent linear time-varying system. An observability analysis for the latter is conducted based on necessary and sufficient conditions that are related to the target's motion. The final estimation solution resorts to a Kalman Filter with globally exponentially stable error dynamics. Its performance is assessed via realistic numerical simulations, including Monte Carlo runs and a comparison with both the standard extended Kalman filter and the Bayesian Cramér–Rao bound. A set of experimental results achieved within the scope of a realistic underwater mission scenario is also presented that allows to further assess the proposed technique. [ABSTRACT FROM AUTHOR]

The ever increasing requirements in wireless communications have led to the search for unexploited correlations that could improve channel estimation and tracking. Kalman filtering (KF) has been proposed to exploit several such correlations, e.g., the time correlation in each tap in a multipath channel. When making full use of this correlation, however, a capital disadvantage of KF is its weak performance in the face of a significant, occasional nonlinearity, such as the potential birth of a new tap in a multipath channel or an active tap's death. These nonlinearities are typical for vehicular applications. So far, solutions proposed to this birth-death nonlinearity problem have been shown to be computationally prohibitive. In this paper, a simplified detection framework is introduced and a computationally inexpensive simplified maximum a posteriori estimator is derived. Under low to medium SNR conditions, simulations show the channel tracking error can be approximately halved (versus simple KF) by this novel estimator in orthogonal frequency division multiplexing systems. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have emerged as the state-of-the-art energy storage for portable electronics, electrified vehicles, and smart grids. An enabling Battery Management System holds the key for efficient and reliable system operation, in which State-of-Charge (SOC) estimation and State-of-Health (SOH) monitoring are of particular importance. In this paper, an SOC and SOH co-estimation scheme is proposed based on the fractional-order calculus. First, a fractional-order equivalent circuit model is established and parameterized using a Hybrid Genetic Algorithm/Particle Swarm Optimization method. This model is capable of predicting the voltage response with a root-mean-squared error less than 10 mV under various driving-cycle-based tests. Comparative studies show that it improves the modeling accuracy appreciably from its second- and third-order counterparts. Then, a dual fractional-order extended Kalman filter is put forward to realize simultaneous SOC and SOH estimation. Extensive experimental results show that the maximum steady-state errors of SOC and SOH estimation can be achieved within 1%, in the presence of initial deviation, noise, and disturbance. The resilience of the co-estimation scheme against battery aging is also verified through experimentation. [ABSTRACT FROM AUTHOR]

This paper introduces a filter for tracking a ballistic missile during its boost phase. This filter includes a new switching algorithm and a modified interacting multiple model unscented filter (IMMUF), where the Markov transition matrix is time variable. Position, velocity, and all unknown parameters of a medium-range ballistic missile model are reconstructed. Simulations demonstrate that the new filter is able to consistently estimate a missile's trajectory and all unknown parameters and to outperform the previous forms of the IMMUF. [ABSTRACT FROM AUTHOR]