scholarly journals Underwater 3D Doppler-Angle Target Tracking with Signal Time Delay

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3869
Author(s):  
Jun Su ◽  
Yaan Li ◽  
Wasiq Ali ◽  
Xiaohua Li ◽  
Jing Yu
Keyword(s):  
Time Delay ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Signal Propagation ◽  
The State ◽  
Estimation Accuracy ◽  
Target Range ◽  
Implicit Equations ◽  
Passive Acoustic ◽  
Signal Time

The traditional target tracking is a process of estimating the state of a moving target using measurement information obtained by sensors. However, underwater passive acoustic target tracking will confront further challenges, among which the system incomplete observability and time delay caused by the signal propagation create a great impact on tracking performance. Passive acoustic sensors cannot accurately obtain the target range information. The introduction of Doppler frequency measurement can improve the system observability performance; signal time delay cannot be ignored in underwater environments. It varies with time, which has a continuous negative impact on the tracking accuracy. In this paper, the Gauss–Helmert model is introduced to solve this problem by expanding the unknown signal emission time as an unknown variable to the state. This model allows the existence of the previous state and current state at the same time, while handling the implicit equations. To improve the algorithm accuracy, this paper further takes advantage of the estimated state and covariance for the second stage iteration and propose the Gauss–Helmert iterated Unscented Kalman filter under a three-dimensional environment. The simulation shows that the proposed method in this paper shows superior estimation accuracy and more stable performance compared with other filtering algorithms in underwater environments.

scholarly journals A model-free hull deformation measurement method with time delay compensation

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881069 ◽  
Author(s):  
Ying He ◽  
Xiafu Peng ◽  
Xiaoli Zhang ◽  
Xiaoqiang Hu
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Deformation Measurement ◽  
Estimation Accuracy ◽  
Deformation Model ◽  
Delay Compensation ◽  
The Neural Network ◽  
Time Delay Compensation

Estimation and compensation for hull deformation is an indispensable step for the ship to establish a unified space attitude. The existing hull deformation measurement methods are dependent on the pre-established deformation model, and an inaccurate deformation model will reduce the deformation estimation accuracy. To solve this problem, a hull deformation estimation method without deformation model is proposed in this article, which utilizes the neural network to fit the hull deformation. To train the neural network online, connection weights of the neural network are regarded as system state variables which can be estimated by the Unscented Kalman Filter. Simultaneously, considering the time delay problem of inertial data, a time delay compensation method based on the quaternion attitude matrix is proposed. The simulation results show that the proposed method can obtain high estimation accuracy without any deformation model even when the inertial data are asynchronous.

scholarly journals Pseudorange error analysis for precise indoor positioning system

2017 ◽  
Vol 68 (3) ◽  
pp. 206-211 ◽  
Author(s):  
Marek Pola ◽  
Pavel Bezoušek
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Estimation Method ◽  
Correlation Method ◽  
Signal Propagation ◽  
Ultra Wideband ◽  
Estimation Accuracy ◽  
Time Of Arrival ◽  
Position Measurement ◽  
Direct Path ◽  
Signal Time

Abstract There is a currently developed system of a transmitter indoor localization intended for fire fighters or members of rescue corps. In this system the transmitter of an ultra-wideband orthogonal frequency-division multiplexing signal position is determined by the time difference of arrival method. The position measurement accuracy highly depends on the directpath signal time of arrival estimation accuracy which is degraded by severe multipath in complicated environments such as buildings. The aim of this article is to assess errors in the direct-path signal time of arrival determination caused by multipath signal propagation and noise. Two methods of the direct-path signal time of arrival estimation are compared here: the cross correlation method and the spectral estimation method.

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 A Novel Adaptive State of Charge Iterative Calculation Method by Using the Improved Unscented Kalman Filtering Algorithm for the Lithium Ion Battery Packs

2018 ◽  
Author(s):  
Shun-Li Wang ◽  
Carlos Fernandez ◽  
Chun-Mei Yu ◽  
Chuan-Yun Zou ◽  
James Coffie-Ken
Keyword(s):  
Lithium Ion Battery ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Battery Management ◽  
Iterative Calculation ◽  
Calculation Process ◽  
State Of Charge Estimation

The state of charge estimation is an important part of the battery management system, the estimation accuracy of which seriously affects the working performance of the lithium ion battery pack. The unscented Kalman filter algorithm has been developed and applied to the iterative calculation process. When it is used to estimate the SOC value, there is a rounding error in the numerical calculation. When the sigma point is sampled in the next round, an imaginary number appears, resulting in the estimation failure. In order to improve the estimation accuracy, an improved adaptive square root - unscented Kalman filter method is introduced which combines the QR decomposition in the calculation process. Meanwhile, an adaptive noise covariance matching method is implied. Experiments show that the proposed method can guarantee the semi-positive and numerical stability of the state covariance, and the estimation accuracy can reach the third-order precision. The error remains about 1.60% under the condition of drastic voltage and current changes. The conclusion of this experiment can provide a theoretical basis of the state of charge estimation in the battery management of the lithium ion battery pack.

A Method of Multi-Antenna Signal Time-Delay Alignment Algorithm for Cyclostationary Signals

2014 ◽  
Vol 536-537 ◽  
pp. 3-8
Author(s):  
Xiang Wen Yao ◽  
Yun Peng Hu ◽  
Zhi Xiang Shen ◽  
Cai Yao Shen
Keyword(s):  
Time Delay ◽  
Estimation Error ◽  
Reference Signal ◽  
Estimation Accuracy ◽  
Alignment Algorithm ◽  
Deep Space Network ◽  
Communication Signal ◽  
Alignment Technique ◽  
Cyclostationary Signals ◽  
Signal Time

In the signal combining system of deep space network, the estimation error of time-delay between signals will reduce the effectiveness. The time-delay alignment technique based on combined output signal as the reference (CC-SUMPLE algorithm) makes use of the mutual information offered by multi-antenna and improves the alignment performance. However, it only takes ordinary cross-correlation into consideration rather than the cyclostationary of digital communication signal during calculating time-delay in the iterative process. As to this problem, this paper proposes multi-antenna signal time-delay alignment algorithm based on cyclostationary of communication signal (MCCC-SUMPLE algorithm) which reconstructs the combined reference signal and takes advantage of multi-cycle frequencies. The simulation results show that the proposed algorithm will improve the estimation accuracy and time-delay alignment performance compared with CC-SUMPLE algorithm.

scholarly journals Online Parameter Identification and Joint Estimation of the State of Charge and the State of Health of Lithium-Ion Batteries Considering the Degree of Polarization

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2939 ◽  
Author(s):  
Bizhong Xia ◽  
Guanghao Chen ◽  
Jie Zhou ◽  
Yadi Yang ◽  
Rui Huang ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
The State ◽  
Operating Conditions ◽  
State Of Charge ◽  
Joint Estimation ◽  
Estimation Accuracy ◽  
Degree Of Polarization ◽  
State Of Health ◽  
Battery Capacity

The state of charge (SOC) and the state of health (SOH) are the two most important indexes of batteries. However, they are not measurable with transducers and must be estimated with mathematical algorithms. A precise model and accurate available battery capacity are crucial to the estimation results. An improved speed adaptive velocity particle swarm optimization algorithm (SAVPSO) based on the Thevenin model is used for online parameter identification, which is used with an unscented Kalman filter (UKF) to estimate the SOC. In order to achieve the cyclic update of the SOH, the concept of degree of polarization (DOP) is proposed. The cyclic update of available capacity is thus obtainable to conversely promote the estimation accuracy of the SOC. The estimation experiments in the whole aging process of batteries show that the proposed method can enhance the SOC estimation accuracy in the full battery life cycle with the cyclic update of the SOH, even in cases of operating aged batteries and under complex operating conditions.

scholarly journals New delay-dependent observer-based control for uncertain stochastic time-delay systems

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xiu-feng Miao ◽  
Long-suo Li
Keyword(s):  
Time Delay ◽  
The State ◽  
Delay Systems ◽  
Linear Matrix ◽  
Time Delay Systems ◽  
Parameter Uncertainties ◽  
Noise Disturbances ◽  
System States ◽  
Delay Dependent ◽  
Stochastic Time

AbstractThis paper considers the problem of estimating the state vector of uncertain stochastic time-delay systems, while the system states are unmeasured. The system under study involves parameter uncertainties, noise disturbances and time delay, and they are dependent on the state. Based on the Lyapunov–Krasovskii functional approach, we present a delay-dependent condition for the existence of a state observer in terms of a linear matrix inequality. A numerical example is exploited to show the validity of the results obtained.

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