scholarly journals Adaptive Consensus-Based Unscented Information Filter for Tracking Target with Maneuver and Colored Noise

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3069 ◽  
Author(s):  
Li ◽  
Wang ◽  
Zheng
Keyword(s):  
State Estimation ◽  
Situation Awareness ◽  
Information Matrix ◽  
Estimation Method ◽  
Measurement Noise ◽  
Distributed State Estimation ◽  
Information Filter ◽  
Fading Factor ◽  
Stability And Accuracy ◽  
The Impact

Distributed state estimation plays a key role in space situation awareness via a sensor network. This paper proposes two adaptive consensus-based unscented information filters for tracking target with maneuver and colored measurement noise. The proposed filters can fulfill the distributed estimation for non-linear systems with the aid of a consensus strategy, and can reduce the impact of colored measurement noise by employing the state augmentation and measurement differencing methods. In addition, a fading factor that shrinks the predicted information state and information matrix can suppress the impact of dynamical model error induced by target maneuvers. The performances of the proposed algorithms are investigated by considering a target tracking problem using a space-based radar network. This shows that the proposed algorithms outperform the traditional consensus-based distributed state estimation method in aspects of tracking stability and accuracy.

scholarly journals A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3809 ◽  
Author(s):  
Yushi Hao ◽  
Aigong Xu ◽  
Xin Sui ◽  
Yulei Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Navigation System ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Measurement Noise ◽  
Model Errors ◽  
State Process ◽  
Stability And Accuracy ◽  
The Impact

Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers.

Robust high-degree cubature information filter and its application to trajectory estimation for ballistic missile

2017 ◽  
Vol 232 (12) ◽  
pp. 2364-2377 ◽  
Author(s):  
Xiaogang Wang ◽  
Wutao Qin ◽  
Naigang Cui ◽  
Yu Wang
Keyword(s):  
State Estimation ◽  
Estimation Algorithm ◽  
Ballistic Missile ◽  
Measurement Noise ◽  
Estimation Accuracy ◽  
Trajectory Estimation ◽  
Information Filter ◽  
Non Gaussian ◽  
Time Update ◽  
High Degree

This paper presents a new recursive filter algorithm, the robust high-degree cubature information filter, which can provide reliable state estimation in the presence of non-Gaussian measurement noise. The novel algorithm is developed in the framework of the conventional information filter. The fifth-degree Cubature rule is utilized to improve the estimation accuracy and numerical stability during the time update, while the Huber technique is adopted in the measurements update stage. As the Huber technique is a combined minimum l1 and l2 norm estimation algorithm, the proposed algorithm could exhibit robustness to the non-Gaussian measurement noise, especially the glint noise. In addition, Monte Carlo simulation and the trajectory estimation for ballistic missile experiments demonstrate that the robust high-degree cubature information filter can provide improved state estimation performance over extended information filter and high-degree cubature information filter.

scholarly journals State Estimation for Sampled-Data Descriptor Nonlinear System: A Strong Tracking Unscented Kalman Filter Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tiantian Liang ◽  
Mao Wang ◽  
Zhenhua Zhou
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Descriptor System ◽  
Sampled Data ◽  
Filtering Method ◽  
System A ◽  
Filter Approach ◽  
Fading Factor

This paper proposes a state estimation method for a sampled-data descriptor system by the Kalman filtering method. The sampled-data descriptor system is firstly discretized to obtain a discrete-time nonsingular model. Based on the discretized nonsingular system, a strong tracking unscented Kalman filter (STUKF) algorithm is designed for the state estimation. Then, a defined suboptimal fading factor is proposed and added to the prediction covariance for decreasing the weight of the prior knowledge on the conventional UKF filtering solution. Finally, a simulation example is given to show the effectiveness of the proposed method.

Research of Robust State Estimation Method and Program Implementation Considering Large-Scale Wind Power Integration

2014 ◽  
Vol 672-674 ◽  
pp. 361-366
Author(s):  
Ya Di Luo ◽  
Jing Li ◽  
Zi Ming Guo ◽  
Gui Rong Shi ◽  
Dong Sheng Wang ◽  
...  
Keyword(s):  
State Estimation ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Estimation Method ◽  
Wind Farms ◽  
The State ◽  
Wind Power Integration ◽  
The Impact ◽  
Bad Data

According to the characteristics of the wind farm measuration and the impact of bad data on the state estimation, this paper introduces the reference value of measurement type and the bad data reference factor into the weight function, and then presents the calculation method of state estimation method for solving residual contamination problem caused by large-scale wind power integration. In order to improve the software computing speed and the data section real-time performance of robust state estimation, using parallel algorithms to do Givens transformation. Finally, the simulation tests of a regional power grid to prove that the proposed method can effectively identify telemetry bad data of wind farms eliminate residual pollution caused by it, which improve the speed and accuracy of the State Estimation.

scholarly journals Hybrid Adaptive Cubature Kalman Filter with Unknown Variance of Measurement Noise

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4335 ◽  
Author(s):  
Yuepeng Shi ◽  
Xianfeng Tang ◽  
Xiaoliang Feng ◽  
Dingjun Bian ◽  
Xizhao Zhou
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Estimation Method ◽  
Measurement Noise ◽  
Variance Estimators ◽  
Unknown Variance ◽  
Weighted Fusion ◽  
Information Filter ◽  
Cubature Kalman Filter ◽  
The Stability

This paper is concerned with the filtering problem caused by the inaccuracy variance of measurement noise in real nonlinear systems. A novel weighted fusion estimation method of multiple different variance estimators is presented to estimate the variance of the measurement noise. On this basis, a hybrid adaptive cubature Kalman filtering structure is proposed. Furthermore, the information filter of the hybrid adaptive cubature Kalman filter is also studied, and the stability and filtering accuracy of the filter are theoretically discussed. The final simulation examples verify the validity and effectiveness of the hybrid adaptive cubature Kalman filtering methods proposed in this paper.

An Adaptive Vector Tracking Scheme for High-Orbit Degraded GNSS Signal

2020 ◽  
pp. 1-20
Author(s):  
Chenyang Jiao ◽  
Xinlong Wang ◽  
Dun Wang ◽  
Qunsheng Li ◽  
Jinpeng Zhang ◽  
...  
Keyword(s):  
Linear Models ◽  
Estimation Method ◽  
Satellite System ◽  
Measurement Noise ◽  
Noise Power ◽  
Time Varying ◽  
Vector Tracking ◽  
Gnss Receivers ◽  
High Orbit ◽  
The Impact

Global navigation satellite system (GNSS) receivers meet numerous challenges in a high-orbit environment, including weak and discontinuous signal, and time-varying strength. To resolve these issues and enhance reliability, an innovative adaptive vector tracking loop (VTL) scheme is proposed. Non-linear models of the VTL filter are established to calculate code phase and carrier frequency errors accurately. Based on this, a deep analysis has been developed on the measurement noise. To reduce the impact of the interdependent noises among channels in VTL, an adaptive VTL algorithm assisted by the variational Bayesian (VB) learning network is proposed to estimate the measurement noise and maintain the error convergence in the time-varying noise or signal outage conditions. Further, the implementation steps of the adaptive algorithm have been designed in detail. In particular, the carrier-to-noise power ratio (C/N0) estimation method is further employed to update the a prior probability density in case of change of tracking satellite. The simulation results indicate that the proposed VTL scheme with VB algorithm is a promising method to improve the accuracy and reliability of GNSS receivers significantly under a high-orbit degraded signal environment.

scholarly journals Robust State Estimation for a Nonlinear Hybrid Model of the Alternating Activated Sludge Process Using Filtered High-Gain Observers

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Afef Boudagga ◽  
Habib Dimassi ◽  
Salim Hadj-Said ◽  
Faouzi M’Sahli
Keyword(s):  
Activated Sludge ◽  
State Estimation ◽  
Estimation Method ◽  
High Gain ◽  
Observer Design ◽  
Measurement Noise ◽  
Observation Error ◽  
Activated Sludge Process ◽  
High Gain Observer ◽  
Nonlinear Hybrid

In this paper, a robust state estimation method based on a filtered high-gain observer is developed for the alternating activated sludge process (AASP) considered as a nonlinear hybrid system. Indeed, we assume that the biodegradable substrate and the ammonia concentrations in the AASP model are unmeasured due to the high cost of their sensors whose maintenance is also very expensive. The observer design is based on the association of the classical high-gain observer and the idea of the application of linear filters on the observation error to deal with measurement noise. It is shown through a Lyapunov analysis that the designed observer ensures the estimation of the unmeasured states (the biodegradable substrate and the ammonia concentrations) based on the measured dissolved oxygen and nitrate concentrations subject to noise. A comparison with the classical high-gain observer is performed via numerical simulations in order to show the robustness of the suggested estimation approach against Gaussian measurement noise.

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