scholarly journals Underwater Bearing-Only Multitarget Tracking in Dense Clutter Environment Based on PMHT

2020 ◽  
Vol 38 (2) ◽  
pp. 359-365
Author(s):  
Xiaohua Li ◽  
Ya'an Li ◽  
Xiaofeng Lu ◽  
Chenxu Zhao ◽  
Jing Yu
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Data Association ◽  
Estimation Accuracy ◽  
Multitarget Tracking ◽  
Single Observation ◽  
Multiple Observations ◽  
Multiple Hypothesis Tracking ◽  
Kalman Filter Algorithm ◽  
Present Algorithm

Underwater bearing-only multitarget tracking in clutter environment is challenging because of the measurement nonlinearity, range unobservability, and data association uncertainty. In terms of the principle of expectation maximization, combining the extended Kalman filter (EKF) and unscented Kalman filter algorithm(UKF), a new bearing-only multi-sensor multitarget tracking via probabilistic multiple hypothesis tracking(PMHT) algorithm is proposed. The PMHT algorithm introduces an association variable to deal with the data association uncertainty problem between the measurements and the targets. Furthermore, the EKF-based PMHT for multi-sensor multitarget system is simplified, which obviate the need to "stack" the synthetic measurements and can reduce the computation cost. The estimation accuracy of the EKF based on PMHT approach and UKF based on PMHT approach in simulation experiments for underwater bearing-only cross-moving targets and closely spaced targets for the case of stationary multiple observations and maneuvering single observation under dense clutter environment is analyzed. The experimental results demonstrate that the present algorithm is very well in a highly clutter environment and its computational load is low, which confirms the effectiveness of the algorithm to the bearing-only multitarget tracking in dense clutter.

scholarly journals Two-Step Adaptive Augmented Unscented Kalman Filter for Roll Angles of Spinning Missiles Based on Magnetometer Measurements

2018 ◽  
Vol 51 (3-4) ◽  
pp. 73-82 ◽  
Author(s):  
Xiaolong Yan ◽  
Guoguang Chen ◽  
Xiaoli Tian
Keyword(s):  
Kalman Filter ◽  
State Vector ◽  
Unscented Kalman Filter ◽  
Sampling Rate ◽  
The State ◽  
Roll Angle ◽  
Estimation Accuracy ◽  
Measurement Circuit ◽  
Kalman Filter Algorithm ◽  
Stop Rule

It is critical to measure the roll angle of a spinning missile quickly and accurately. Magnetometers are commonly used to implement these measurements. At present, the estimation of roll angle parameters is usually performed with the unscented Kalman filter algorithm. In this paper, the two-step adaptive augmented unscented Kalman filter algorithm is proposed to calibrate the biaxial magnetometer and circuit measurements quickly, which allows accurate estimates of the missile roll angle. Unlike the existing algorithms, the state vector of the algorithm is based on the missile roll angle parameters and the error factors caused by the magnetometer and the measurement circuit errors. Next, a two-step fast fitting algorithm is used to fit the initial value. After satisfying the stop rule, the state vector of the filter is configured to estimate the roll angle parameters and the calibration parameters. This method is evaluated by running numerous simulations. In the experiment, the algorithm completes the calibration of the magnetometer and the measurement circuit 1 s after the missile launches, with a sampling rate of 1 ms and an output roll attitude angle with a 0.0015 rad precision. The conventional unscented Kalman filter algorithm requires more time to achieve such a high accuracy. The simulation results demonstrate that the proposed two-step augmented unscented Kalman filter outperforms the conventional unscented Kalman filter in its estimation accuracy and convergence characteristics.

State of Energy Estimation Based on AUKF for Lithium Battery Used on Pure Electric Vehicle

2012 ◽  
Vol 608-609 ◽  
pp. 1627-1630
Author(s):  
Hong Wei Liu ◽  
Hai Feng Wang ◽  
Chong Guo
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Unscented Kalman Filter ◽  
The State ◽  
Estimation Accuracy ◽  
Battery Management System ◽  
Battery Management ◽  
Process Noise ◽  
Energy Estimation ◽  
Kalman Filter Algorithm

State of Energy can be used to predict the driving mileage of electric vehicles, design the control strategy of vehicle energy distribution, and improve the safety of electric vehicle. Accurate estimaion of state of energy is one of the key technologies in the study on battery management system of electric vehicle. In this paper, the State of Energy is estimated by using Unscented Kalman Filter, while the process noise and measurement noise is adjusted by using the Sage-Husa adaptive algorithm, as a result the estimation accuracy is improved. The result shows that the State of Energy estimation by using Adaptive Unscented Kalman Filter algorithm is satisfactory to electric vehicle.

scholarly journals Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization

Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 24595-24614 ◽  
Author(s):  
Guoliang Chen ◽  
Xiaolin Meng ◽  
Yunjia Wang ◽  
Yanzhe Zhang ◽  
Peng Tian ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Indoor Localization ◽  
Unscented Kalman Filter ◽  
Kalman Filter Algorithm

Iterated Cubature Kalman Filter for State Estimation of Maneuver Reentry Vehicle

2012 ◽  
Vol 466-467 ◽  
pp. 1329-1333
Author(s):  
Jing Mu ◽  
Chang Yuan Wang
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
High Accuracy ◽  
Estimation Accuracy ◽  
Reentry Vehicle ◽  
Vehicle Simulation ◽  
Cubature Kalman Filter ◽  
Simulation Results ◽  
Iterate Process

We present the new filters named iterated cubature Kalman filter (ICKF). The ICKF is implemented easily and involves the iterate process for fully exploiting the latest measurement in the measurement update so as to achieve the high accuracy of state estimation We apply the ICKF to state estimation for maneuver reentry vehicle. Simulation results indicate ICKF outperforms over the unscented Kalman filter and square root cubature Kalman filter in state estimation accuracy.

scholarly journals An Improved Unscented Kalman Filter Algorithm for Radar Azimuth Mutation

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Dazhang You ◽  
Pan Liu ◽  
Wei Shang ◽  
Yepeng Zhang ◽  
Yawei Kang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Singular Value Decomposition ◽  
Unscented Kalman Filter ◽  
Tracking System ◽  
Sudden Change ◽  
Singular Value ◽  
Tracking Accuracy ◽  
Kalman Filter Algorithm ◽  
Value Decomposition ◽  
Accuracy And Stability

An improved UKF (Unscented Kalman Filter) algorithm is proposed to solve the problem of radar azimuth mutation. Since the radar azimuth angle will restart to count after each revolution of the radar, and when the aircraft just passes the abrupt angle change, the radar observation measurement will have a sudden change, which has serious consequences and is solved by the proposed novel UKF based on SVD. In order to improve the tracking accuracy and stability of the radar tracking system further, the SVD-MUKF (Singular Value Decomposition-based Memory Unscented Kalman Filter) based on multiple memory fading is constructed. Furthermore, several simulation results show that the SVD-MUKF algorithm proposed in this paper is better than the SVD-UKF (Singular Value Decomposition of Unscented Kalman Filter) algorithm and classical UKF algorithm in accuracy and stability. Last but not the least, the SVD-MUKF can achieve stable tracking of targets even in the case of angle mutation.

scholarly journals Online Estimation of Li-ion Battery SOC for Electric Vehicles Based on An Improved AEKF

2019 ◽  
Vol 118 ◽  
pp. 02025 ◽  
Author(s):  
Kaihui Feng ◽  
Bibin Huang ◽  
Qionghui Li ◽  
Hu Yan
Keyword(s):  
Kalman Filter ◽  
Electric Vehicles ◽  
Equivalent Circuit Model ◽  
Estimation Accuracy ◽  
Extend Kalman Filter ◽  
Online Estimation ◽  
Soc Estimation ◽  
Kalman Filter Algorithm ◽  
Li Ion ◽  
Influence Of Noise

The purpose of this paper is to discuss how to eliminate the influence of noise time -varying characteristics on the accuracy of SOC estimation. Based on the matlab/simulink platform, the Thevenin equivalent circuit model of the battery is built, and an improved Adaptive Extend Kalman Filter (AEKF) is designed, which is compared with the Extend Kalman filter algorithm (EKF).The simulation results are shown that the improved AEKF algorithm results in effective online estimation SOC and the estimation accuracy is higher than the EKF algorithm.

Application of Adaptive Kalman Filter Technique in Initial Alignment of Single-Axial Rotation Strap-Down Inertial Navigation System

2012 ◽  
Vol 466-467 ◽  
pp. 617-621
Author(s):  
Song Tian Shang ◽  
Wen Shao Gao
Keyword(s):  
Kalman Filter ◽  
Axial Rotation ◽  
Estimation Accuracy ◽  
Initial Alignment ◽  
Adaptive Kalman Filter ◽  
Rotation System ◽  
Kalman Filter Algorithm ◽  
Axis Rotation ◽  
Single Axis Rotation ◽  
Better Than

In order to improve the accuracy of initial alignment which determines the accuracy of navigation, a Sage-Husa adaptive kalman filter algorithm is applied to SINS initial alignment of single-axis rotation system. The simulation result further shows that in the case of inaccurate statistical property of noise, the estimation accuracy of Sage-Husa adaptive kalman filter is better than the conventional kalman filter.

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