Improving the Estimation of the Tracking Error Signal in Real Time for IR Seeker Using Nonlinear Kalman Filters

2020 ◽  
Vol 29 (15) ◽  
pp. 2050242
Author(s):  
Osama Abbas ◽  
Mohammad Reza Arvan ◽  
Ali Mahmoodi
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Tracking Error ◽  
Target Position ◽  
Raspberry Pi ◽  
Main Task ◽  
Error Signal ◽  
Ir Detector ◽  
Information Signal

In nonimaging IR seekers, the received target radiation on the IR detector is modulated via a reticle and produces the information signal (IS). The IS contains the tracking error signal (TES), which is proportional with the target position. TES is used in the control and optic section in the missile. The main task is to extract the TES from IS. The accuracy of TES extraction may be affected by several items such as noise from engines. In this paper, we used for the first time this field Square Root Unscented Kalman Filter (SRUKF) and Extended Kalman Filter (EKF) to estimate the TES from the IS for wagon wheel reticle. Due to the high computational complexity of these algorithms, their execution in real time is not an easy task especially if there is space limitation for hardware. By using the minicomputer, such as Raspberry Pi 3 Model [Formula: see text] platform, the task can be done. The results showed that the SRUKF presented the best phase estimation for TES. The implementation by using Raspberry Pi was in real time because all algorithm executions for one period was less than 5[Formula: see text]ms, this time in our problem is less than strict timing window.

scholarly journals Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 750
Author(s):  
Wenkang Wan ◽  
Jingan Feng ◽  
Bao Song ◽  
Xinxin Li
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Degrees Of Freedom ◽  
Unscented Kalman Filter ◽  
State Parameter ◽  
State Observation ◽  
Straight Line ◽  
Distributed Drive Electric Vehicle ◽  
Huber Method ◽  
Yaw Moment

Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy.

Real-Time Signal Denoising Algorithm in Wheel Force Transducer

2014 ◽  
Vol 615 ◽  
pp. 244-247
Author(s):  
Dong Wang ◽  
Guo Yu Lin ◽  
Wei Gong Zhang
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Force Transducer ◽  
Time Signal ◽  
Signal Denoising ◽  
Observation Function ◽  
Simulation Results ◽  
Wheel Force Transducer

The wheel force transducer (WFT) is used to measure dynamic wheel loads. Unlike other force sensors, WFT is rotating with the wheel. For this reason, the outputs and the inputs of the transducer are nonlinearly related, and traditional Kalman Filter is not suitable. In this paper, a new real-time filter algorithm utilizing Quadrature Kalman Filter (QKF) is proposed to solve this problem. In Quadrature Kalman Filter, Singer model is introduced to track the wheel force, and the observation function is established for WFT. The simulation results illustrate that the new filter outperforms the traditional Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF).

scholarly journals Orbit Propagation and Determination of Low Earth Orbit Satellites

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ho-Nien Shou
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Nonlinear Problems ◽  
Low Earth Orbit ◽  
Earth Orbit ◽  
Positioning Accuracy ◽  
Orbit Propagation

This paper represents orbit propagation and determination of low Earth orbit (LEO) satellites. Satellite global positioning system (GPS) configured receiver provides position and velocity measures by navigating filter to get the coordinates of the orbit propagation (OP). The main contradictions in real-time orbit which is determined by the problem are orbit positioning accuracy and the amount of calculating two indicators. This paper is dedicated to solving the problem of tradeoffs. To plan to use a nonlinear filtering method for immediate orbit tasks requires more precise satellite orbit state parameters in a short time. Although the traditional extended Kalman filter (EKF) method is widely used, its linear approximation of the drawbacks in dealing with nonlinear problems was especially evident, without compromising Kalman filter (unscented Kalman Filter, UKF). As a new nonlinear estimation method, it is measured at the estimated measurements on more and more applications. This paper will be the first study on UKF microsatellites in LEO orbit in real time, trying to explore the real-time precision orbit determination techniques. Through the preliminary simulation results, they show that, based on orbit mission requirements and conditions using UKF, they can satisfy the positioning accuracy and compute two indicators.

scholarly journals Application of Kalman Filtering Methods to Online Real-Time Structural Identification: A Comparison Study

2016 ◽  
Vol 16 (06) ◽  
pp. 1550016 ◽  
Author(s):  
Mohsen Askari ◽  
Jianchun Li ◽  
Bijan Samali
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Structural Parameters ◽  
Measurement Data ◽  
Linear Structure ◽  
Structural Identification ◽  
Input Output ◽  
Output Measurement ◽  
Highly Nonlinear

System identification refers to the process of building or improving mathematical models of dynamical systems from the observed experimental input–output data. In the area of civil engineering, the estimation of the integrity of a structure under dynamic loadings and during service condition has become a challenge for the engineering community. Therefore, there has been a great deal of attention paid to online and real-time structural identification, especially when input–output measurement data are contaminated by high-level noise. Among real-time identification methods, one of the most successful and widely used algorithms for estimation of system states and parameters is the Kalman filter and its various nonlinear extensions such as extended Kalman filter (EKF), Iterated EKF (IEKF), the recently developed unscented Kalman filter (UKF) and Iterated UKF (IUKF). In this paper, an investigation has been carried out on the aforementioned techniques for their effectiveness and efficiencies through a highly nonlinear single degree of freedom (SDOF) structure as well as a two-storey linear structure. Although IEKF is an improved version of EKF, results show that IUKF generally produces better results in terms of structural parameters and state estimation than UKF and IEKF. Also IUKF is more robust to noise levels compared to the other approaches.

PREDICTING UNKNOWN MOTION FOR MODEL INDEPENDENT VISUAL SERVOING

2001 ◽  
Vol 01 (03) ◽  
pp. 287-302 ◽  
Author(s):  
PATRICE WIRA ◽  
JEAN-PHILIPPE URBAN
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
Real Time ◽  
Visual Servoing ◽  
Dimensional Space ◽  
Tracking Error ◽  
State Model ◽  
Divide And Conquer ◽  
Image Feature ◽  
Robot Arm

Prediction in real-time image sequences is a key-feature for visual servoing applications. It is used to compensate for the time-delay introduced by the image feature extraction process in the visual feedback loop. In order to track targets in a three-dimensional space in real-time with a robot arm, the target's movement and the robot end-effector's next position are predicted from the previous movements. A modular prediction architecture is presented, which is based on the Kalman filtering principle. The Kalman filter is an optimal stochastic estimation technique which needs an accurate system model and which is particularly sensitive to noise. The performances of this filter diminish with nonlinear systems and with time-varying environments. Therefore, we propose an adaptive Kalman filter using the modular framework of mixture of experts regulated by a gating network. The proposed filter has an adaptive state model to represent the system around its current state as close as possible. Different realizations of these state model adaptive Kalman filters are organized according to the divide-and-conquer principle: they all participate to the global estimation and a neural network mediates their different outputs in an unsupervised manner and tunes their parameters. The performances of the proposed approach are evaluated in terms of precision, capability to estimate and compensate abrupt changes in targets trajectories, as well as to adapt to time-variant parameters. The experiments prove that, without the use of models (e.g. the camera model, kinematic robot model, and system parameters) and without any prior knowledge about the targets movements, the predictions allow to compensate for the time-delay and to reduce the tracking error.

A Method for Real-Time Estimating Pose of Large Aircraft Component in Process of Pose Adjustment Based on the Unscented Kalman Filter

2012 ◽  
Vol 442 ◽  
pp. 251-255
Author(s):  
Zheng Ying
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Kinematic Model ◽  
Estimation Method ◽  
Time Estimation ◽  
Measuring Point ◽  
Aircraft Component ◽  
Forward Kinematic ◽  
Large Aircraft

To estimate the pose of large aircraft component in pose adjustment quickly and accurately, a real-time estimation method based on Unscented Kalman filter (UKF) is proposed. Firstly, in the process of the aircraft component adjustment, a rough value of aircraft component’s pose is acquired by using forward kinematic model and the displacement of positioners on real time. Then, position of a measuring point fixed on aircraft component is obtained by a laser tracker. At last, UKF is employed to integrate the previous rough value and the measuring point position for evaluating the accurate pose of aircraft component. Numerical simulation results show that the presented method is achieved easily, calculated fast and high accurate.

Radar Target Tracking Error Research Using Kalman Filter-Based Algorithms

2012 ◽  
Vol 239-240 ◽  
pp. 1184-1187
Author(s):  
Qian Long Chai ◽  
Yu Long Bai ◽  
Cun Hui Dong
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Tracking Error ◽  
Substantial Effect ◽  
Specific Model ◽  
Radar Target ◽  
Tracking Accuracy ◽  
Tracking Errors ◽  
Radar Systems

The methods of radar target tracking have a substantial effect on the accuracy of the whole radar systems. The basic principles and implementing steps of the Extended Kalman filter (the EKF) and the Unscented Kalman filter (the UKF) are briefly introduced. The main sources of radar observation errors and the limitation of the current methods are analyzed. According to the requirements of tracking a CV target, the EKF and the UKF are used to simulate the experiments by establishing the specific model of radar target tracking. The results show that the tracking errors can be constrained within a certain range and the whole systems also have the high tracking accuracy.

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