scholarly journals EKF- and UKF-Based Estimators for Radar System

2021 ◽  
Vol 1 ◽  
Author(s):  
U. K. Singh ◽  
A. K. Singh ◽  
V. Bhatia ◽  
A. K. Mishra
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
Doppler Shift ◽  
Unscented Kalman Filter ◽  
Low Complexity ◽  
Least Mean Square ◽  
Estimation Errors ◽  
Incorrect Choice ◽  
Nonlinear Estimators ◽  
Kernel Least Mean Square

In radar, the measurements (like the range and radial velocity) are determined from the time delay and Doppler shift. Since the time delay and Doppler shift are estimated from the phase of the received echo, the concerned estimation problem is nonlinear. Consequently, the conventional estimator based on the fast Fourier transform (FFT) is prone to yield high estimation errors. Recently, nonlinear estimators based on kernel least mean square (KLMS) are introduced and found to outperform the conventional estimator. However, estimators based on KLMS are susceptible to incorrect choice of various system parameters. Thus, to mitigate the limitation of existing estimators, in this paper, two efficient low-complexity nonlinear estimators, namely, the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), are proposed. The EKF is advantageous due to its implementation simplicity; however, it suffers from the poor representation of the nonlinear functions by the first-order linearization, whereas UKF outperforms the EKF and offers better stability due to exact consideration of the system nonlinearity. Simulation results reveal improved accuracy achieved by the proposed EKF- and UKF-based estimators.

Unscented Kalman Filter for INS/GNSS Data Fusion with Time Delay

2021 ◽  
Author(s):  
Kanishke Gamagedara ◽  
Taeyoung Lee ◽  
Murray R. Snyder
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
Data Fusion ◽  
Unscented Kalman Filter ◽  
Gnss Data

Stereovision-based relative states and inertia parameter estimation of noncooperative spacecraft

2018 ◽  
Vol 233 (7) ◽  
pp. 2489-2502 ◽  
Author(s):  
Xun Wang ◽  
Zhaokui Wang ◽  
Yulin Zhang
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Target Surface ◽  
State Equation ◽  
Moment Of Inertia ◽  
Transition Rule ◽  
Estimation Errors ◽  
Fixed Frame ◽  
Adaptive Unscented Kalman Filter ◽  
Relative States

Autonomous proximity operations have recently become appealing as space missions. In particular, the estimation of the relative states and inertia properties of a noncooperative spacecraft is an important but challenging problem, because there might be poor priori information about the target. Using only stereovision measurements, this study developed an adaptive unscented Kalman filter to estimate the relative states and moment-of-inertia ratios of a noncooperative spacecraft. Because the accuracy of the initial relative states has an effect on the estimation convergence performance, attention was also given to their determination. The target’s body-fixed frame was defined in parallel to the chaser’s initial body-fixed frame, and then the initial relative attitude was known. After formulating kinematic constraint equations between the relative states and multiple points on the target surface, particle swarm optimization was utilized to determine the initial relative angular velocity. The initial relative position was also determined under the assumption that the initial relative translational velocity was known. To estimate the relative states and moment-of-inertia ratios using the adaptive unscented Kalman filter, the relative attitude dynamic model was reformulated by designing a novel transition rule with five moment-of-inertia ratios, described in the defined target’s body-fixed frame. The moment-of-inertia ratios were added to the state space, and a new state equation with variant process noise covariance matrix Q was formulated. The measurement updating errors of the relative states were utilized to adaptively modify Q so that the filter could estimate the relative states and moment-of-inertia ratios in two stages. Numerical simulations of the adaptive unscented Kalman filter with unknown moment-of-inertia ratios and the standard unscented Kalman filter with known moment-of-inertia ratios were conducted to illustrate the performance of the adaptive unscented Kalman filter. The obtained results showed the satisfactory convergence of the estimation errors of both the relative states and moment-of-inertia ratios with high accuracy.

scholarly journals Ground Reaction Force Estimation in Prosthetic Legs With Nonlinear Kalman Filtering Methods

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Seyed Fakoorian ◽  
Vahid Azimi ◽  
Mahmoud Moosavi ◽  
Hanz Richter ◽  
Dan Simon
Keyword(s):  
Kalman Filter ◽  
Continuous Time ◽  
Lyapunov Functions ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Reaction Force ◽  
Force Estimation ◽  
Joint Angles ◽  
Estimation Errors ◽  
Reaction Forces

A method to estimate ground reaction forces (GRFs) in a robot/prosthesis system is presented. The system includes a robot that emulates human hip and thigh motion, along with a powered (active) transfemoral prosthetic leg. We design a continuous-time extended Kalman filter (EKF) and a continuous-time unscented Kalman filter (UKF) to estimate not only the states of the robot/prosthesis system but also the GRFs that act on the foot. It is proven using stochastic Lyapunov functions that the estimation error of the EKF is exponentially bounded if the initial estimation errors and the disturbances are sufficiently small. The performance of the estimators in normal walk, fast walk, and slow walk is studied, when we use four sensors (hip displacement, thigh, knee, and ankle angles), three sensors (thigh, knee, and ankle angles), and two sensors (knee and ankle angles). Simulation results show that when using four sensors, the average root-mean-square (RMS) estimation error of the EKF is 0.0020 rad for the joint angles and 11.85 N for the GRFs. The respective numbers for the UKF are 0.0016 rad and 7.98 N, which are 20% and 33% lower than those of the EKF.

scholarly journals Underwater Acoustic Time Delay Estimation Based on Envelope Differences of Correlation Functions

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1259 ◽  
Author(s):  
Guodong Li ◽  
Jinsong Wu ◽  
Taolin Tang ◽  
Zhixin Chen ◽  
Jun Chen ◽  
...  
Keyword(s):  
Time Delay ◽  
Correlation Functions ◽  
Estimation Method ◽  
Digital Signal ◽  
Low Complexity ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Estimation Errors ◽  
Underwater Acoustic ◽  
Amplitude Fluctuations

This paper proposes underwater acoustic time delay estimation based on the envelope differences of correlation functions (EDCF), which mitigates the delay estimation errors introduced by the amplitude fluctuations of the correlation function envelopes in the traditional correlation methods (CM). The performance of the proposed delay estimation method under different time values was analyzed, and the optimal difference time values are given. To overcome the influences of digital signal sampling intervals on time delay estimation, a digital time delay estimation approach with low complexity and high accuracy is proposed. The performance of the proposed time delay estimation was analyzed in underwater multipath channels. Finally, the accuracy of the delay estimation using this proposed method was demonstrated by experiments.

The Unscented Kalman Filter Estimators for High Dynamic Doppler Shift Environments

2012 ◽  
Vol 532-533 ◽  
pp. 1487-1491
Author(s):  
Kun Zhao ◽  
Ke Gang Pan ◽  
Ai Jun Liu ◽  
Dao Xing Guo
Keyword(s):  
Kalman Filter ◽  
Nonlinear Systems ◽  
Doppler Shift ◽  
Unscented Kalman Filter ◽  
The State ◽  
Extend Kalman Filter ◽  
Unscented Transformation ◽  
High Dynamic ◽  
Simulation Results ◽  
Dynamic Trajectory

The Extend Kalman Filter (EKF) is widely used in the tracking of high dynamic Doppler shift trajectories, but it has some flows when it is used to estimate the state of nonlinear systems. In this paper, we apply the Unscented Transformation (UT) based Unscented Kalman Filter (UKF) to the state estimation in the high dynamic Doppler environments. Two versions of the UKF estimators, augmented UKF estimator and nonaugemented UKF estimator are designed. To compare the performance of them, they are applied to tracking a common high dynamic trajectory, and simulation results declare that given different conditions, the performance of the estimators will be different.

scholarly journals Vehicle Driving State Estimation Using an Improved Adaptive Unscented Kalman Filter

2021 ◽  
Author(s):  
Hui Pang ◽  
Peng Wang ◽  
Zijun Xu ◽  
Gang Wang
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Estimation Errors ◽  
Adaptive Unscented Kalman Filter ◽  
Adjustment Strategy ◽  
Noise Covariance ◽  
Adaptive Noise ◽  
Three Degree Of Freedom

Abstract This paper proposes an improved adaptive unscented Kalman filter (iAUKF)-based vehicle driving state estimation method. A three-degree-of-freedom vehicle dynamics model is first established, then the varying principles of estimation errors for vehicle driving states using constant process and measurement noises in the standard unscented Kalman filter (UKF) are compared and analyzed. Next, a new type of normalized innovation square-based adaptive noise covariance adjustment strategy is designed and incorporated into the UKF to derive our expected vehicle driving state estimation method. Finally, a comparative simulation investigation using CarSim and MATLAB/Simulink is conducted to validate the effectiveness of the proposed method, and the results show that our proposed iAUKF-based estimation method has higher accuracy and stronger robustness against the standard UKF algorithm.

A novel metaheuristic model-based approach for accurate online broken bar fault diagnosis in induction motor using unscented Kalman filter and ant lion optimizer

2019 ◽  
Vol 42 (8) ◽  
pp. 1537-1546 ◽  
Author(s):  
Marouane Rayyam ◽  
Malika Zazi
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Unscented Kalman Filter ◽  
Superior Performance ◽  
Simultaneous Estimation ◽  
Estimation Errors ◽  
Model Based ◽  
Ant Lion Optimizer ◽  
Detection And Identification ◽  
Ant Lion

This paper introduces a novel metaheuristic model-based scheme for fault monitoring in squirrel cage induction motors (SCIMs). This method relies on the combination of the ant lion optimizer (ALO) and the unscented Kalman filter (UKF) to detect and quantify the number of broken bars. Contrary to the UKF-based fault diagnosis, the improved ALO-UKF algorithm tunes optimally and automatically the noise covariance matrices Q and R, which reduces the estimation errors, and then obtains an effective and accurate fault diagnosis. Firstly, a mathematical model of the fault under study has been developed based on rotor parameter value as signature. Secondly, a sixth order ALO-UKF algorithm has been synthesized for simultaneous estimation of rotor resistance and speed. Several broken bar fault conditions have been simulated. Simulation results show the effectiveness and robustness of the proposed ALO-UKF scheme in broken bar detection and identification, and exhibit a more superior performance than the simple-UKF and EKF algorithms in term of stability, accuracy and response time.

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