scholarly journals Self-Driving Car Location Estimation Based on a Particle-Aided Unscented Kalman Filter

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2544 ◽  
Author(s):  
Ming Lin ◽  
Jaewoo Yoon ◽  
Byeongwoo Kim
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Urban Environments ◽  
Location Estimation ◽  
Measurement Information ◽  
High Definition ◽  
Position Measurement ◽  
Localization Performance ◽  
Non Gaussian ◽  
Self Driving Cars

Localization is one of the key components in the operation of self-driving cars. Owing to the noisy global positioning system (GPS) signal and multipath routing in urban environments, a novel, practical approach is needed. In this study, a sensor fusion approach for self-driving cars was developed. To localize the vehicle position, we propose a particle-aided unscented Kalman filter (PAUKF) algorithm. The unscented Kalman filter updates the vehicle state, which includes the vehicle motion model and non-Gaussian noise affection. The particle filter provides additional updated position measurement information based on an onboard sensor and a high definition (HD) map. The simulations showed that our method achieves better precision and comparable stability in localization performance compared to previous approaches.

scholarly journals Extended Particle-Aided Unscented Kalman Filter Based on Self-Driving Car Localization

2020 ◽  
Vol 10 (15) ◽  
pp. 5045 ◽  
Author(s):  
Ming Lin ◽  
Byeongwoo Kim
Keyword(s):  
Kalman Filter ◽  
Real World ◽  
Gaussian Noise ◽  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Two Dimensions ◽  
Localization Problem ◽  
Simulation Results ◽  
Non Gaussian ◽  
Self Driving Cars

The location of the vehicle is a basic parameter for self-driving cars. The key problem of localization is the noise of the sensors. In previous research, we proposed a particle-aided unscented Kalman filter (PAUKF) to handle the localization problem in non-Gaussian noise environments. However, the previous basic PAUKF only considers the infrastructures in two dimensions (2D). This previous PAUKF 2D limitation rendered it inoperable in the real world, which is full of three-dimensional (3D) features. In this paper, we have extended the previous basic PAUKF’s particle weighting process based on the multivariable normal distribution for handling 3D features. The extended PAUKF also raises the feasibility of fusing multisource perception data into the PAUKF framework. The simulation results show that the extended PAUKF has better real-world applicability than the previous basic PAUKF.

scholarly journals Robust Huber-Based Cubature Kalman Filter for GPS Navigation Processing

2016 ◽  
Vol 70 (3) ◽  
pp. 527-546 ◽  
Author(s):  
Chien-Hao Tseng ◽  
Sheng-Fuu Lin ◽  
Dah-Jing Jwo
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Unscented Kalman Filter ◽  
Measurement Information ◽  
Gps Navigation ◽  
Cubature Kalman Filter ◽  
Global Positioning ◽  
M Estimation ◽  
Non Gaussian ◽  
Distribution Assumption

A robust state estimation technique based on the Huber-based Cubature Kalman Filter (HCKF) is proposed for Global Positioning System (GPS) navigation processing. The Cubature Kalman Filter (CKF) employs a third-degree spherical-radial cubature rule to compute the Gaussian weighted integration, such that the numerical instability induced by round-off errors can be avoided. In GPS navigation, the filter-based estimation of the position and velocity states can be severely degraded due to contaminated measurements caused by outliers or deviation from a Gaussian distribution assumption. For the signals contaminated with non-Gaussian noise or outliers, a robust scheme combining the Huber M-estimation methodology and the CKF framework is beneficial where the Huber M-estimation methodology is used to reformulate the measurement information of the CKF. GPS navigation processing using the HCKF algorithm has been carried out and the performance has been compared to those based on the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and CKF approaches. Simulation and experimental results presented in this paper confirm the effectiveness of the method.

scholarly journals Estimation of Full Strap-down Rotating Bomb Guidance Information Based on Unscented Kalman Filter

2020 ◽  
Vol 38 (3) ◽  
pp. 604-609
Author(s):  
Wei Gao ◽  
Benbing Gao ◽  
Hongsong Fang ◽  
Xin Lu
Keyword(s):  
Mathematical Model ◽  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Angular Rate ◽  
The Body ◽  
Filter Method ◽  
Measurement Information ◽  
Kalman Filter Method ◽  
The Mathematical Model ◽  
Navigation Information

In this paper, the full strap-down seeker of rotating bomb is taken as the research object, and the method of extracting the LOS (line-of-sight) angle and angular rate of the full strap-down seeker of the rotating bomb is studied. The structure of the full strap-down seeker is quite different from that of the conventional rate gyro seeker. The measurement system of full strap-down seeker is fixed to the missile, the seeker can only obtain the measurement information in the projectile coordinate system, and the measurement information is coupled with the body posture information, so it cannot be directly used for the control guidance of the rotating projectile. First, based on the conversion relationship between coordinate systems, the mathematical model of the inertial LOS angle of the rotating bomb is established, and the mathematical model of the extraction of the inertial LOS angle and angular rate of the rotating bomb is further established. Then, the Kalman filter is designed by using the unscented Kalman filter method (UKF), and the extracted LOS angle containing noise information is filtered. Finally, the mathematical simulation is carried out to verify the validity of the mathematical model of LOS angle and angular rate extraction. Compared with the Extended Kalman filter method (EKF), the UKF has a higher accuracy for estimating the navigation information of the full strap-down rotating projectile.

scholarly journals Underwater surveillance in non-Gaussian noisy environment

2020 ◽  
Vol 53 (1-2) ◽  
pp. 250-261
Author(s):  
B Omkar Lakshmi Jagan ◽  
S Koteswara Rao
Keyword(s):  
Kalman Filter ◽  
Lower Bound ◽  
Extended Kalman Filter ◽  
Gaussian Noise ◽  
Unscented Kalman Filter ◽  
Doppler Frequency ◽  
Gaussian Mixture ◽  
Noisy Environment ◽  
Sampled Measurements ◽  
Non Gaussian

The aim of this paper is to evaluate the performance of different filtering algorithms in the presence of non-Gaussian noise environment for tracking underwater targets, using Doppler frequency and bearing measurements. The tracking using Doppler frequency and bearing measurements is popularly known as Doppler-bearing tracking. Here the measurements, that is, bearings and Doppler frequency, are considered to be corrupted with two types of non-Gaussian noises namely shot noise and Gaussian mixture noise. The non-Gaussian noise sampled measurements are assumed to be obtained (a) randomly throughout the process and (b) repeatedly at some particular time samples. The efficiency of these filters with the increase in non-Gaussian noise samples is discussed in this paper. The performance of filters is compared with that of Cramer-Rao Lower Bound. Doppler-bearing extended Kalman filter and Doppler-bearing unscented Kalman filter are chosen for this work.

Non Linear and Non Gaussian States and Parameters Estimation using Bayesian Methods-Comparatives Studies

2014 ◽  
pp. 711-748 ◽  
Author(s):  
Majdi Mansouri ◽  
Moustafa Mohamed-Seghir ◽  
Hazem Nounou ◽  
Mohamed Nounou ◽  
Haitham A. Abu-Rub
Keyword(s):  
Kalman Filter ◽  
Bayesian Methods ◽  
Process Model ◽  
Unscented Kalman Filter ◽  
Nonlinear Process ◽  
Parameters Estimation ◽  
Gaussian States ◽  
Estimation Algorithms ◽  
Non Linear ◽  
Non Gaussian

This chapter deals with the problem of non-linear and non-Gaussian states and parameters estimation using Bayesian methods. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). In the current work, the authors consider two systems (biological model and power system) to perform evaluation of estimation algorithms. The results of the comparative studies show that the UKF provides a higher accuracy than the EKF due to the limited ability of EKF to accurately estimate the mean and covariance matrix of the estimated states through lineralization of the nonlinear process model. The results also show that the PF provides a significant improvement over the UKF because, unlike UKF, PF is not restricted by linear-Gaussian assumptions which greatly extends the range of problems that can be tackled.

Three-Dimensional Submarine-to-Submarine Passive Target Tracking in the Presence of Non-Gaussian Noises

2021 ◽  
Vol 17 (3) ◽  
pp. 1-24
Author(s):  
Kavitha Lakshmi M. ◽  
Koteswara Rao S. ◽  
Subrahmanyam Kodukula
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Gaussian Noise ◽  
Target Location ◽  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Noise Environment ◽  
Cubature Kalman Filter ◽  
Highly Nonlinear ◽  
Non Gaussian

In underwater surveillance, three-dimensional target tracking is a challenging task. The angles-only measurements (i.e., bearing and elevation) obtained by hull mounted sensors are considered to appraise the target motion parameter. Due to noise in measurements and nonlinearity of the system, it is very hard to find out the target location. For many applications, UKF is best estimator that remaining algorithms. Recently, cubature Kalman filter (CKF) is also popular. It is proposed to use UKF (unscented Kalman filter) and CKF (cubature Kalman filter) algorithms that minimize the noise in measurements. So far, researchers carried out this work (target tracking) in Gaussian noise environment, whereas in this paper same work is carried out for non-Gaussian noise environment. The performance evaluation of the filters using Monte-Carlo simulation and Cramer-Rao lower bound (CRLB) is accomplished and the results are analyzed. Result shows that UKF is well suitable for highly nonlinear systems than CKF.

Line-of-Sight Angle Rate Reconstruction for Phased Array Strapdown Seeker

2013 ◽  
Vol 645 ◽  
pp. 196-201
Author(s):  
Ying Liu ◽  
Wei Feng Tian ◽  
Jian Kang Zhao ◽  
Shi Qing Zhu ◽  
Ge Wen Yang
Keyword(s):  
Kalman Filter ◽  
Phased Array ◽  
Unscented Kalman Filter ◽  
Measurement Noise ◽  
Line Of Sight ◽  
Measurement Information ◽  
Reconstruction Method ◽  
Proportional Navigation ◽  
Filtering Algorithm ◽  
Simulation Results

The phased array strapdown radar seeker’s detecting information is coupled with missile attitude information. Hence, the measurement information can not be used for proportional navigation directly. The method of reconstructing inertial line of sight (LOS) rate in phased array strapdown seeker is presented using the missile-target relative motion geometric and filtering algorithm. Considering measurement noise and nonlinearity of the phased array strapdown radar guidance systems, the principle of unscented kalman filter (UKF) is introduced to estimate LOS rate. The simulation results show that the reconstruction method is correct and the extraction of LOS rate is effective.

Inversion Algorithm Based on the Unscented Kalman Filter for Inverse Heat Conduction Problems

2013 ◽  
Vol 705 ◽  
pp. 474-482
Author(s):  
Pan Chu
Keyword(s):  
Kalman Filter ◽  
Heat Conduction ◽  
Unscented Kalman Filter ◽  
Initial Conditions ◽  
Measurement Data ◽  
Measurement Information ◽  
Inverse Heat Conduction ◽  
Space Problem ◽  
Inversion Model ◽  
Inverse Heat Conduction Problems

The inverse heat conduction problems (IHCP) analysis method provides a promising approach for acquiring the thermal physical properties of materials, the boundary conditions and the initial conditions from the known temperature measurement data, where the efficiency of the inversion algorithms plays a crucial role in real applications. In this paper, an inversion model that simultaneously utilizes the process evolution information of the objects to be estimated and the measurement information is proposed. The original IHCP is formulated into a state-space problem, and the unscented Kalman filter (UKF) method is developed for solving the proposed inversion model. The implementation of the proposed method does not require the gradient vector, the Jacobian matrix or the Hessian matrix, and thus the computational complexity is decreased. Numerical simulations are implemented to evaluate the feasibility of the proposed algorithm. For the cases simulated in this paper, satisfactory results are obtained, which indicates that the proposed algorithm is successful in solving the IHCP.

scholarly journals Modeling and Prediction of Time-Varying Environmental Data Using Advanced Bayesian Methods

2014 ◽  
pp. 112-137 ◽  
Author(s):  
Majdi Mansouri ◽  
Benjamin Dumont ◽  
Marie-France Destain
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
State Parameter ◽  
Prediction Errors ◽  
Time Varying ◽  
State Variables ◽  
Crop Models ◽  
Modeling And Prediction ◽  
Input Variables ◽  
Non Gaussian

The problem of state/parameter estimation represents a key issue in crop models, which are nonlinear, non-Gaussian, and include a large number of parameters. The prediction errors are often important due to uncertainties in the equations, the input variables, and the parameters. The measurements needed to run the model and to perform calibration and validation are sometimes not numerous or known with some uncertainty. In these cases, estimating the state variables and/or parameters from easily obtained measurements can be extremely useful. In this chapter, the authors address the problem of modeling and prediction of time-varying Leaf area index and Soil Moisture (LSM) to better handle nonlinear and non-Gaussian processes without a priori state information. The performances of various conventional and state-of-the-art estimation techniques are compared when they are utilized to achieve this objective. These techniques include the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF), and the more recently developed technique Variational Bayesian Filter (VF). The original data was issued from experiments carried out on silty soil in Belgium with a wheat crop during two consecutive years, the seasons 2008-09 and 2009-10.

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