Integrated Kalman Filter Research Articles

State of Charge Estimation of Lithium Battery Based on Integrated Kalman Filter Framework and Machine Learning Algorithm

Research on batteries’ State of Charge (SOC) estimation for equivalent circuit models based on the Kalman Filter (KF) framework and machine learning algorithms remains relatively limited. Most studies are focused on a few machine learning algorithms and do not present comprehensive analysis and comparison. Furthermore, most of them focus on obtaining the state space parameters of the Kalman filter frame algorithm models using machine learning algorithms and then substituting the state space parameters into the Kalman filter frame algorithm to estimate the SOC. Such algorithms are highly coupled, and present high complexity and low practicability. This study aims to integrate machine learning with the Kalman filter frame algorithm, and to estimate the final SOC by using different combinations of the input, output, and intermediate variable values of five Kalman filter frame algorithms as the input of the machine learning algorithms of six main streams. These are: linear regression, support vector Regression, XGBoost, AdaBoost, random forest, and LSTM; the algorithm coupling is lower for two-way parameter adjustment and is not applied between the machine learning and Kalman filtering framework algorithms. The results demonstrate that the integrated learning algorithm significantly improves the estimation accuracy when compared to the pure Kalman filter framework or the machine learning algorithms. Among the various integrated algorithms, the random forest and Kalman filter framework presents the highest estimation accuracy along with good real-time performance. Therefore, it can be implemented in various engineering applications.

Integrated Tracking and ISAR Imaging Using an Integrated Kalman Filter With Wideband Radar

Traditionally, inverse synthetic aperture radar (ISAR) imaging and target tracking are two separate and independent processes. ISAR imaging is usually achieved after data collecting and tracking for a long time, resulting in the problems of low imaging efficiency, information loss, and probing resource allocation difficulty. In this article, we proposed an integration method of tracking and imaging with wideband radar by constructing the relation among signal, data, and image domains, which can realize accurate tracking and high data-rate imaging at the same time. In this integrated imaging and tracking method, a complex-valued reference high-resolution range profile (HRRP) is generated and predicted by exploiting the relation among range-Doppler image, echo in time domain, and target’s motion state. Then, a Bayes estimation model is built to extract range phase error between the predicted HRRP and the real-measured HRRP, and the Kalman filter can update the motion state. In addition, in order to increase the efficiency, the decentralized Kalman filter is also introduced to realize the subaperture parallel processing and data fusion in this article. Finally, the sequential motion compensation is carried out to realize accurate motion compensation and sequential imaging. The simulated and real-measured data confirm the proposed algorithm’s robust imaging and tracking performance. Experimental results show that the proposed method has excellent efficiency and can simultaneously realize sequential imaging and accurate tracking.

측정치 시간지연을 가진 전자광학 추적장비의 초기 자세 결정 알고리즘

In order to have accurate target positions of the EOTS(Electro-Optical Tracking System), more accurate navigation outputs are required. An IMU(Inertial Measurement Unit) can be attached to the EOTS to have high rate INS ouputs. Due to the time-delay of the vehicle’s navigation outputs, it is not easy to have accurate target positions. When the vehicle moves at high speed and with high maneuverability, the accuracy becomes even worse. In this paper, an initial attitude determination algorithm of the EOTS with measurement time-delay is proposed. In order to compensate the measurement time-delay, the time-delay variable is augmented into the integrated Kalman filter for the IMU of EOTS and vehicle’s measurement output. The proposed algorithm was evaluated for sensor’s model-based measurement generator outputs, and a land vehicle experiments were performed. The evaluation results show that the proposed algorithm compensates the measurement time-delay well and gives more accurate initial attitude.

Integrated Kalman Filter of Accurate Ranging and Tracking With Wideband Radar

Accurate ranging and wideband tracking are treated as two independent and separate processes in traditional radar systems. As a result, limited by low data rate due to nonsequential processing, accurate ranging usually performs low efficiency in practical application. Similarly, without applying accurate ranging, the data after thresholding and clustering are used in wideband tracking, leading to a significant decrease in tracking accuracy. In this article, an integrated Kalman filter of accurate ranging and tracking is proposed using methods of phase-derived-ranging and Bayesian inference in wideband radar. Besides the motion state, in this integrated Kalman filter, the complex-valued high-resolution range profile (HRRP) is also introduced as a reference signal by coherent integration in a sliding window, which incorporates target’s scattering distribution and phase characteristics. Corresponding kinetic equations are derived to predict the motion state and the reference signal in the next moment. A ranging process is constructed based on the received signal and the predicted reference signal in order to estimate innovation using methods of phase-derived-ranging and Bayesian inference, and a sequential update for motion state can be accomplished with the Kalman filter as well. In every recursion, the complex-valued reference signal is also updated by coherently integrating the latest pulses. The integrated Kalman filter takes full use of high range resolution and phase information, improving both efficiency and precision compared with conventional approaches of ranging and wideband tracking. Implemented in a sequential manner, the integrated Kalman filter can be applied in a real-time application, realizing simultaneous ranging with high precision and wideband tracking. Finally, simulated and real-measured experiments confirm the remarkable performance.

An Innovative High-Precision Scheme for a GPS/MEMS-SINS Ultra-Tight Integrated System.

The Doppler-assisted error provided by a low-precision microelectromechanical system (MEMS) strapdown inertial navigation system (SINS) increases rapidly. Therefore, the bandwidth of the tracking loop for a global positioning system (GPS)/MEMS-SINS ultra-tight integration system is too narrow to track Doppler shift. GPS measurement error is correlated with the MEMS-SINS velocity error when the Doppler-assisted error exists, leading to tracking loop lock loss. The estimated precision of the integrated Kalman filter (IKF) also decreases. Even the integrated system becomes unstable. To solve this problem, an innovative GPS/MEMS-SINS ultra-tight integration scheme based on using high-precision carrier phase measurements as the IKF measurements is proposed in this study. By assisting the tracking loop with time-differenced carrier phase (TDCP) velocity, the carrier loop noise bandwidth and code correlator spacing are reduced. The tracking accuracies of the carrier and code are increased. The navigation accuracy of GPS/MEMS-SINS ultra-tight integration is further improved.

Dual-Antenna GNSS Integrated With MEMS for Reliable and Continuous Attitude Determination in Challenged Environments

Attitude determination based on global navigation satellite systems (GNSS) is characterized by high-precision, low-cost, and no error accumulation. However, GNSS harsh-signal environments will degrade the accuracy and reliability of GNSS attitude. In this case, micro-electro-mechanical system (MEMS) inertial sensors are often integrated with dual-antenna GNSS for more reliable and continuous attitude determination. In this paper, we fused dual-antenna GNSS and MEMS to acquire heading, pitch, and roll with high accuracy in GNSS challenged environments. Instead of a Euler angle representation, the misalignment is used to build the state model in the integrated Kalman filter. Attitudes derived from dual-antenna GNSS and smoothed acceleration are adopted as measurements. It can be found that this filtering architecture is actually a subset of loosely coupled GNSS/MEMS integration. Therefore, the proposed module can be easily embedded into loosely coupled integration. In addition, due to the disadvantage that GNSS is sensitive to signal interference and obstacles, the fault detection and exclusion strategy was proposed to avoid the filtering divergence and to improve the reliability of attitude determination. Finally, two vehicular tests with a 1.12-m baseline conducted in GNSS friendly and challenged environments showed that the proposed attitude determination algorithm could detect and exclude all GNSS-attitude outliers, thus achieve the high accuracy of 0.2°, 0.39°, and 0.28° for heading, pitch, and roll angles in the challenged environment, respectively. With the MEMS-gyroscope aiding, the attitude gaps caused by GNSS outages are bridged. For a 300-s outage duration, the average accumulated attitudes errors are 2.37°, 0.88°, and 1.40°.

On robust constrained Kalman filter for dynamic errors-in-variables model

A robust Kalman filter algorithm is proposed to solve nonlinear errors-in-variables dynamic problems in the presence of outliers. This algorithm is robust constrained integrated total Kalman filter (RCITKF). The method iteratively reweights the predicted solution when the observable quantities are contaminated by gross errors (outliers). It can impose the quadratic constrains which may appear in some problems. Moreover, the RCITKF algorithm can consider the neglected random unknowns of the functional model of the dynamic problem which gives an added advantage over the previous Kalman filters. In two geodetic applications, the efficiency of these algorithms is demonstrated in contrast to the extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithms.

ON A NEW FAMILY OF KALMAN FILTER ALGORITHMS FOR INTEGRATED NAVIGATION

Abstract. Here we present a review on a new family of Kalman filter algorithms which recently developed for integrated navigation. In particular it is useful for vision based navigation due to the type of data. Here we mainly focus on three algorithms namely weighted Total Kalman filter (WTKF), integrated Kalman filter (IKF) and constrained integrated Kalman filter (CIKF). The common characteristic of these algorithms is that they can consider the neglected random observed quantities which may appear in the dynamic model. Moreover, our approach makes use of condition equations and straightforward variance propagation rules. The WTKF algorithm can deal with problems with arbitrary weight matrixes. Both of the observation equations and system equations can be dynamic-errors-in-variables (DEIV) models in the IKF algorithms. In some problems a quadratic constraint may exist. They can be solved by CIKF algorithm. Finally, we compare four algorithms WTKF, IKF, CIKF and EKF in numerical examples.

On constrained integrated total Kalman filter for integrated direct geo-referencing

A constrained integrated total Kalman filter algorithm is developed. It considers a quadratic constraint which may appear in some problems of integrated direct geo-referencing in particular when INS data is used as system equations of a Kalman filter algorithm. In such a case one encounters with a dynamic errors-in-variables (DEIV) model for system equations, although DEIV model has been already considered for equations of the Kalman filter algorithm and a solution namely integrated total Kalman filter (ITKF) has been given to it. Also this algorithm can be simplified to unconstraint case which is useful for some problems. It considers DEIV model for both observation equations and system equations of the Kalman filter algorithm. The predicted residuals for all variables including the random noise at the first epoch, the observational noise, the random system noise and the corresponding noise of two coefficient matrixes (in the system equations and the observation equations) besides the variance matrix of the unknown parameters are obtained. In two numerical examples, integrated direct geo-referencing problem is solved for a GPS-INS system.

Differential games missile guidance with bearings-only measurements

The implementation of missile guidance laws on collision course trajectories with bearings-only measurements may result in bad range observability. This in turn may result in poor engagement performance for those guidance algorithms that exploit range information in their steering law. Maneuvering away from the collision course trajectory improves range observability. To propose a sizing criterion for this maneuver, this work presents a new guidance strategy that exploits the information from the error covariance matrix of the homing loop integrated Kalman filter in the framework of a pursuit-evasion game. The new strategy has been tested in several scenarios against other guidance laws, such as the classical formulation of the pursuit-evasion game and proportional navigation. Numerical simulations on a set of Monte Carlo samples show that the proposed solution is able to improve engagement performance in terms of both miss distance and range observability.

Performance analysis of a federated ultra-tight global positioning system/inertial navigation system integration algorithm in high dynamic environments

The Doppler frequency changes rapidly due to high dynamics of vehicle, which leads to the loose lock and even the abnormal performance of global positioning system (GPS) receiver. To solve this problem, a federated ultra-tight integration algorithm based on pre-filters is proposed to optimal estimate both receiver tracking control commands and inertial navigation system (INS) navigation solutions. Firstly, the INS error model and GPS receiver tracking loop structure are built to present the fundamental architecture of the proposed ultra-tightly coupled system. Meanwhile, in order to reduce the load of the integrated filter, the pre-filters are incorporated to the ultra-tightly coupled system, and the state variables are fed into the integrated Kalman filter. Secondly, the intrinsic relevance between the phase and frequency biases of replica signals and INS states is analyzed to accomplish the deep fusion of INS and tracking loop. Finally, semi-physical simulations are performed by using a GPS signal simulator to generate signals of two high dynamic trajectories. The experimental results indicate that the proposed ultra-tight integration algorithm can achieve a good performance on reliable positioning and robust tracking in high dynamic environments, compared with the conventional approaches such as tightly coupled integration strategy and third-order phase-locked loops.

Real-time coseismic wave retrieving by integrated Kalman filter with observations of GPS, Glonass and strong-motion sensor

A method of real-time coseismic wave retrieving was proposed based on the tight integration of GPS, Glonass and strong-motion sensor observations, the validation and precision analysis have been made by an experimental data. The series of results have been shown that: by the integrated Kalman filter and multi-sensors, the coseismic waves can be optimally recovered by complement the advantages of each other, especially when the observation conditions are very bad. In additional, the results are not significantly effected by different receiver clock error processes for the integration solution.

Adaptively Constrained Kalman Filtering for Navigation Applications

Some constraints exist in the kinematic state parameters used for integrated navigation; if these are taken into account, the accuracy of positioning and navigation can be improved. The abnormal disturbances of the kinematic model are controlled in an integrated Kalman filter by introducing adaptive factors. Adaptive Kalman filters with constraints with respect to both a unified adaptive factor and multi adaptive factors are derived based on the generalized maximum likelihood Lagrangian condition. The adaptive factors are constructed from the discrepancies of states and predicted residuals. The approximate estimator of the adaptively constrained Kalman filter with multi adaptive factors is realized by considering that the measurement vectors are independent, and that the predicted state vectors of the kinematic model are also independent. If the constraints between the parameters of the state vector of various sensors are neglected, then the individual state estimates of adaptive Kalman filtering are obtained. The approximate estimator with unified adaptive factor is also given which is very simple to calculate. It is shown by a simulated example, that the precision of the state estimates provided by the adaptively constrained Kalman filter is better than those provided by the unconstrained adaptive Kalman filter or constrained Kalman filter.

Fault-Tolerant Position/Attitude Estimation of Free-Floating Space Objects Using a Laser Range Sensor

This paper presents a fault-tolerant 3D vision system for autonomous robotic operation. In particular, pose estimation of space objects is achieved using 3D vision data in an integrated Kalman filter (KF) and an Iterative Closest Point (ICP) algorithm in a closed-loop configuration. The initial guess for the internal ICP iteration is provided by the state estimate propagation of the Kalman filer. The Kalman filter is capable of not only estimating the target's states but also its inertial parameters. This allows the motion of the target to be predictable as soon as the filter converges. Consequently, the ICP can maintain pose tracking over a wider range of velocity due to the increased precision of ICP initialization. Furthermore, incorporation of the target's dynamics model in the estimation process allows the estimator continuously provide pose estimation even when the sensor temporally loses its signal namely due to obstruction. The capabilities of the pose estimation methodology is demonstrated by a ground testbed for Automated Rendezvous & Docking. In this experiment, Neptec's Laser Camera System (LCS) is used for real-time scanning of a satellite model attached to a manipulator arm, which is driven by a simulator according to orbital and attitude dynamics. The results showed that robust tracking of the free-floating tumbling satellite can be achieved only if the Kalman filter and ICP are in a closed-loop configuration.

Road vehicle state estimation using low-cost GPS/INS

Assuming known vehicle parameters, this paper proposes an innovative integrated Kalman filter (IKF) scheme to estimate vehicle dynamics, in particular the sideslip, the heading and the longitudinal velocity. The IKF is compared with the 2DoF linear bicycle model, the triple Kalman filter (KF) and a model-based KF (MKF) in a simulation environment. Simulation results show that the proposed IKF is superior to other KF designs (both Kinematic KF and MKF) on state estimation when tyre characteristics are within the linear region (i.e. manoeuvres below 55 kph).

An Integrated MEMS Gyroscope Array with Higher Accuracy Output.

In this paper, an integrated MEMS gyroscope array method composed of two levels of optimal filtering was designed to improve the accuracy of gyroscopes. In the firstlevel filtering, several identical gyroscopes were combined through Kalman filtering into a single effective device, whose performance could surpass that of any individual sensor. The key of the performance improving lies in the optimal estimation of the random noise sources such as rate random walk and angular random walk for compensating the measurement values. Especially, the cross correlation between the noises from different gyroscopes of the same type was used to establish the system noise covariance matrix and the measurement noise covariance matrix for Kalman filtering to improve the performance further. Secondly, an integrated Kalman filter with six states was designed to further improve the accuracy with the aid of external sensors such as magnetometers and accelerometers in attitude determination. Experiments showed that three gyroscopes with a bias drift of 35 degree per hour could be combined into a virtual gyroscope with a drift of 1.07 degree per hour through the first-level filter, and the bias drift was reduced to 0.53 degree per hour after the second-level filtering. It proved that the proposed integrated MEMS gyroscope array is capable of improving the accuracy of the MEMS gyroscopes, which provides the possibility of using these low cost MEMS sensors in high-accuracy application areas.

An overview of a Global Positioning System Mission Planner implemented on a personal computer

The Global Positioning System (GPS) Mission Planner (GMP) program, which has been implemented on an IBM PC, is described in terms of its features and architecture, and sample outputs are presented. The GMP was written to permit operational units to plan missions and to accomplish survivability and navigation assessments based on realistic trajectories, GPS almanac data, broadband jammer specifications, and digital terrain elevation data (DTED). GMP supports trajectory generation for generic air, land, or naval vehicles and has 'sanity' checks for altitude acceleration, terrain slope, and velocity limits. A survivability measure is computed based on exposure time to various threat types. Yuma-type almanac data are used to support the GMP to define GPS satellite orbits. Jammers, threats, and trajectory wavepoints may be defined by either keyboard entry (e.g. longitude, latitude, and altitude) or via mouse and cursor on a displayed pseudo-color DTED map on the PC monitor. Satellite visibility and best dilution-of-precision (DOP) are computed using DTED. jammer visibility and power levels at the vehicle are similarly computed. A realistic body masking and antenna gain model is used to compute carrier-to-noise densities for each visible satellite. A navigation assessment program emulates a multichannel receiver to generate position and velocity measurement uncertainties. An integrated Kalman filter generates position and velocity navigation estimates. Results are graphically displayed to the operator. >