Unscented Kalman Filter based two-stage estimation of spacecraft position and velocity with single station antenna tracking data

2015 ◽  
Author(s):  
Chingiz Hajiyev ◽  
Melih Ata
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Tracking Data ◽  
Two Stage ◽  
Single Station ◽  
Stage Estimation ◽  
Spacecraft Position

scholarly journals Aircraft Inertial Measurement Unit Fault Diagnosis Based on Optimal Two-Stage UKF

2018 ◽  
Vol 36 (5) ◽  
pp. 933-941
Author(s):  
Qizhi He ◽  
Weiguo Zhang ◽  
Degang Huang ◽  
Huakun Chen ◽  
Jinglong Liu
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Nonlinear System ◽  
Inertial Measurement Unit ◽  
Unscented Kalman Filter ◽  
Optimal Estimation ◽  
Measurement Unit ◽  
Two Stage ◽  
Inertial Measurement ◽  
System States

Optimal two stage Kalman filter (OTSKF) is able to obtain optimal estimation of system states and bias for linear system which contains random bias. Unscented Kalman filter (UKF) is a conventional nonlinear filtering method which utilizes Sigmas point sampling and unscented transformation technology realizes propagation of state means and covariances through nonlinear system. Aircraft is a typical complicate nonlinear system, this paper treats the faults of Inertial Measurement Unit (IMU) as random bias, established a filtering model which contains faults of IMU. Hybird the two stage filtering technique and UKF, this paper proposed an optimal two stage unscented Kalman filter (OTSUKF) algorithm which is suitable for fault diagnosis of IMU, realized optimal estimation of system states and faults identification of IMU via proposed innovative designing method of filtering model and the algorithm was validated that it is robust to wind disterbance via real flight data and it is also validated that proposed OTSUKF is optimal in the existance of wind disturbance via comparing with the existance iterated optimal two stage extended kalman filter (IOTSEKF) method.

scholarly journals Aircraft Inertial Measurement Unit Fault Diagnosis Based on Adaptive Two-Stage UKF

2020 ◽  
Vol 38 (4) ◽  
pp. 806-813
Author(s):  
Qizhi He ◽  
Weiguo Zhang ◽  
Xiaoxiong Liu ◽  
Weinan Li
Keyword(s):  
Kalman Filter ◽  
Random Walk ◽  
Fault Diagnosis ◽  
Inertial Measurement Unit ◽  
Unscented Kalman Filter ◽  
Random Walk Model ◽  
Measurement Unit ◽  
Actual Situation ◽  
Two Stage ◽  
Inertial Measurement

In the case of nonlinear systems with random bias, the Optimal Two-Stage Unscented Kalman Filter (OTSUKF) can obtain the optimal estimation of system state and bias. But it requires random bias to be accurately modeled, while it is always very difficult in actual situation because the aircraft is a typical nonlinear system. In this paper, the faults of the Inertial Measurement Unit (IMU) are treated as a random bias, and the random walk model is used to describe the fault. The accuracy of the random walk model depends on the degree of matching between the covariance of the random walk model and the actual situation. For the IMU fault diagnosis method based on OTSUKF, the covariance of the random walk model is assigned with a constant matrix, and the value of the matrix is initialized empirically. It is very difficult to select a matching matrix in practical applications. For this problem, in this paper, the covariance matrix of the random walk model is adaptively adjusted online based on the innovation covariance matching technique, and an adaptive Two-Stage Unscented Kalman Filter (ATSUKF) is proposed to solve the fault diagnosis problem of the IMU. The simulation experiment compares the IMU fault diagnosis performance of OTSUKF and ATSUKF, and verifies the effectiveness of the proposed adaptive method.

A Two-stage Parametric Identification of Strong Nonlinear Structural Systems with Incomplete Response Measurements

2016 ◽  
Vol 16 (04) ◽  
pp. 1640022 ◽  
Author(s):  
Lijun Liu ◽  
Ying Lei ◽  
Mingyu He
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Structural Parameters ◽  
Nonlinear Problems ◽  
Parametric Identification ◽  
Structural Systems ◽  
Two Stage ◽  
Incomplete Response ◽  
Nonlinear Structural ◽  
Structural Responses

Compared with the identification of linear structural parameters, it is more difficult to conduct parametric identification of strong nonlinear structural systems, especially when only incomplete structural responses are available. Although the extended Kalman filter (EKF) is useful for structural identification with partial measurements of structural responses and can be extended for the identification of nonlinear structures, EKF approximates nonlinear system through Taylor series expansion and is therefore not effective for the identification of strong nonlinear structural systems. Other approaches such as the unscented Kalman filter (UKF) have been proposed for the identification of strong nonlinear problems. Based on the fact that nonlinearities exist in local areas of structures, a straightforward two-stage identification approach is proposed in this paper for the identification of strong nonlinear structural parameters with incomplete response measurements. In the first stage, the locations of nonlinearities are identified based on the EKF for the identification of the equivalent linear structures. In the second stage, the UKF is utilized to identify the parameters of strong nonlinear structural systems. Therefore, the parametric identification of strong nonlinear structural parameters is simplified by the proposed approach. Several numerical examples with different nonlinear models and locations are used to validate the proposed approach.

scholarly journals Refined Two-Stage Programming Approach of Phase Unwrapping for Multi-Baseline SAR Interferograms Using the Unscented Kalman Filter

2019 ◽  
Vol 11 (2) ◽  
pp. 199 ◽  
Author(s):  
Yandong Gao ◽  
Shubi Zhang ◽  
Tao Li ◽  
Qianfu Chen ◽  
Xiang Zhang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Surface Deformation ◽  
Unscented Kalman Filter ◽  
Phase Unwrapping ◽  
Programming Approach ◽  
Interferometric Synthetic Aperture Radar ◽  
Two Stage ◽  
Second Stage ◽  
Digital Elevation ◽  
Single Baseline

Phase unwrapping (PU) represents a key step in the reconstruction of digital elevation models (DEMs) and the monitoring of surface deformation from interferometric synthetic aperture radar (InSAR) data. Compared with single-baseline (SB) PU, multi-baseline (MB) PU can resolve the phase discontinuities caused by noise and phase layover induced by terrain undulations. However, the MB PU performance is limited primarily by its poor robustness to measurement bias and noise. To address this problem, we propose a refined 2-D MB PU method based on the two-stage programming approach (TSPA). The proposed method uses the unscented Kalman filter (UKF) to improve the performance of the second stage of the original TSPA method. Specifically, the proposed method maintains the first stage of the TSPA to estimate the range and azimuth gradients between neighbouring pixels. Then, median filtering is slightly used to reduce the effects of the noise gradients on the estimated phase gradients. Finally, the UKF model is used to unwrap the interferometric phases using an efficient quality-guided strategy based on heap-sort. This paper is the first to integrate the UKF into the TSPA framework. The proposed method is validated using bistatic and monostatic MB InSAR datasets, and the experimental results show that the proposed method is effective for MB PU problems.

scholarly journals Estimation of Vehicle Dynamic Parameters Based on the Two-Stage Estimation Method

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3711
Author(s):  
Wenfei Li ◽  
Huiyun Li ◽  
Kun Xu ◽  
Zhejun Huang ◽  
Ke Li ◽  
...  
Keyword(s):  
Vehicle Dynamics ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Moment Of Inertia ◽  
Dynamic Parameters ◽  
Vehicle Dynamic ◽  
Vehicle Model ◽  
Two Stage ◽  
Second Stage ◽  
Stage Estimation

Vehicle dynamic parameters are of vital importance to establish feasible vehicle models which are used to provide active controls and automated driving control. However, most vehicle dynamics parameters are difficult to obtain directly. In this paper, a new method, which requires only conventional sensors, is proposed to estimate vehicle dynamic parameters. The influence of vehicle dynamic parameters on vehicle dynamics often involves coupling. To solve the problem of coupling, a two-stage estimation method, consisting of multiple-models and the Unscented Kalman Filter, is proposed in this paper. During the first stage, the longitudinal vehicle dynamics model is used. Through vehicle acceleration/deceleration, this model can be used to estimate the distance between the vehicle centroid and vehicle front, the height of vehicle centroid and tire longitudinal stiffness. The estimated parameter can be used in the second stage. During the second stage, a single-track with roll dynamics vehicle model is adopted. By making vehicle continuous steering, this vehicle model can be used to estimate tire cornering stiffness, the vehicle moment of inertia around the yaw axis and the moment of inertia around the longitudinal axis. The simulation results show that the proposed method is effective and vehicle dynamic parameters can be well estimated.

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