scholarly journals A New Pseudolinear Filter for Bearings-Only Tracking without Requirement of Bias Compensation

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5444
Author(s):  
Shizhe Bu ◽  
Aiqiang Meng ◽  
Gongjian Zhou
Keyword(s):  
Computational Complexity ◽  
State Estimation ◽  
Minimum Mean Square Error ◽  
State Space Model ◽  
Estimation Accuracy ◽  
Target State ◽  
Correlation Problem ◽  
Cross Covariance ◽  
Stable Performance ◽  
Bias Compensation

In bearings-only tracking systems, the pseudolinear Kalman filter (PLKF) has advantages in stability and computational complexity, but suffers from correlation problems. Existing solutions require bias compensation to reduce the correlation between the pseudomeasurement matrix and pseudolinear noise, but incomplete compensation may cause a loss of estimation accuracy. In this paper, a new pseudolinear filter is proposed under the minimum mean square error (MMSE) framework without requirement of bias compensation. The pseudolinear state-space model of bearings-only tracking is first developed. The correlation between the pseudomeasurement matrix and pseudolinear noise is thoroughly analyzed. By splitting the bearing noise term from the pseudomeasurement matrix and performing some algebraic manipulations, their cross-covariance can be calculated and incorporated into the filtering process to account for their effects on estimation. The target state estimation and its associated covariance can then be updated according to the MMSE update equation. The new pseudolinear filter has a stable performance and low computational complexity and handles the correlation problem implicitly under a unified MMSE framework, thus avoiding the severe bias problem of the PLKF. The posterior Cramer–Rao Lower Bound (PCRLB) for target state estimation is presented. Simulations are conducted to demonstrate the effectiveness of the proposed method.

A Strong Tracking Cubature Kalman Filter for Nonlinear Estimation

2013 ◽  
Vol 313-314 ◽  
pp. 1115-1119
Author(s):  
Yong Qi Wang ◽  
Feng Yang ◽  
Yan Liang ◽  
Quan Pan
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Scaling Factor ◽  
Estimation Accuracy ◽  
Nonlinear State Estimation ◽  
Cubature Kalman Filter ◽  
Novel Method ◽  
Simulation Results ◽  
Tracking Filter ◽  
Nonlinear State

In this paper, a novel method based on cubature Kalman filter (CKF) and strong tracking filter (STF) has been proposed for nonlinear state estimation problem. The proposed method is named as strong tracking cubature Kalman filter (STCKF). In the STCKF, a scaling factor derived from STF is added and it can be tuned online to adjust the filtering gain accordingly. Simulation results indicate STCKF outperforms over EKF and CKF in state estimation accuracy.

scholarly journals A Novel Comprehensive Scheme for Vehicle State Estimation Using Dual Extended H-Infinity Kalman Filter

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1526
Author(s):  
Fengjiao Zhang ◽  
Yan Wang ◽  
Jingyu Hu ◽  
Guodong Yin ◽  
Song Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Estimation Accuracy ◽  
Vehicle Speed ◽  
Sideslip Angle ◽  
H Infinity ◽  
Yaw Rate ◽  
Cost Constraints ◽  
Active Safety Systems ◽  
Vehicle State Estimation

The performance of vehicle active safety systems relies on accurate vehicle state information. Estimation of vehicle state based on onboard sensors has been popular in research due to technical and cost constraints. Although many experts and scholars have made a lot of research efforts for vehicle state estimation, studies that simultaneously consider the effects of noise uncertainty and model parameter perturbation have rarely been reported. In this paper, a comprehensive scheme using dual Extended H-infinity Kalman Filter (EH∞KF) is proposed to estimate vehicle speed, yaw rate, and sideslip angle. A three-degree-of-freedom vehicle dynamics model is first established. Based on the model, the first EH∞KF estimator is used to identify the mass of the vehicle. Simultaneously, the second EH∞KF estimator uses the result of the first estimator to predict the vehicle speed, yaw rate, and sideslip angle. Finally, simulation tests are carried out to demonstrate the effectiveness of the proposed method. The test results indicate that the proposed method has higher estimation accuracy than the extended Kalman filter.

scholarly journals Three State Estimation Fusion Methods Based on the Characteristic Function Filtering

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1440
Author(s):  
Yiran Yuan ◽  
Chenglin Wen ◽  
Yiting Qiu ◽  
Xiaohui Sun
Keyword(s):  
Characteristic Function ◽  
State Estimation ◽  
Packet Loss ◽  
Estimation Accuracy ◽  
Measurement Systems ◽  
Fusion Methods ◽  
Estimation Fusion ◽  
Parallel Filter ◽  
Sequential Filter ◽  
Better Than

There are three state estimation fusion methods for a class of strong nonlinear measurement systems, based on the characteristic function filter, namely the centralized filter, parallel filter, and sequential filter. Under ideal communication conditions, the centralized filter can obtain the best state estimation accuracy, and the parallel filter can simplify centralized calculation complexity and improve feasibility; in addition, the performance of the sequential filter is very close to that of the centralized filter and far better than that of the parallel filter. However, the sequential filter can tolerate non-ideal conditions, such as delay and packet loss, and the first two filters cannot operate normally online for delay and will be invalid for packet loss. The performance of the three designed fusion filters is illustrated by three typical cases, which are all better than that of the most popular Extended Kalman Filter (EKF) performance.

Intelligent localisation of multiple non-linear loads considering impact of harmonic state estimation accuracy

2017 ◽  
Vol 11 (8) ◽  
pp. 1943-1953 ◽  
Author(s):  
Amir Moradifar ◽  
Asghar Akbari Foroud ◽  
Khalil Gorgani Firouzjah
Keyword(s):  
State Estimation ◽  
Estimation Accuracy ◽  
Non Linear

The Fast Diagonal-Matrix-Weight IMM Algorithm for Target Tracking

2012 ◽  
Vol 461 ◽  
pp. 132-137
Author(s):  
Yang Fu ◽  
Ming Wei ◽  
Hai Chuan Zhang ◽  
Liang Gao
Keyword(s):  
Stochastic Process ◽  
Computational Complexity ◽  
Target Tracking ◽  
Real Time ◽  
Probability Density Functions ◽  
Diagonal Matrix ◽  
Estimation Accuracy ◽  
Density Functions ◽  
Alternative Algorithm ◽  
Real Time Application

The diagonal-matrix-weight IMM (DIMM) algorithm can solve the IMM algorithm confusions of probability density functions (PDFs) and probability masses of stochastic process. Combingandfilter，the Fast-IMM algorithm has a better performance both in accuracy and reducing computational complexity. In order to improve the estimation accuracy and computational complexity，we apply Fast-IMM method to DIMM algorithm. Therefore，A new method, Fast diagonal-matrix-weight IMM (fast-DIMM) algorithm, is proposed in this paper to heighten the real-time application of DIMM algorithm. Simulations indicate that the proposed fast-DIMM algorithm is a competitive alternative algorithm to the IMM algorithm in real time application

A Method of Distributed State Estimation in Dispatch Central Based on Ripe Data and Topology Model of Substation

2013 ◽  
Vol 732-733 ◽  
pp. 1283-1287
Author(s):  
Jun Liu ◽  
Da Wei Su ◽  
Ke Jun Qian ◽  
Fei Shi ◽  
Li Wen Wang
Keyword(s):  
State Estimation ◽  
Estimation Accuracy ◽  
Source Information ◽  
Consistency Check ◽  
Distributed State Estimation ◽  
And Topology ◽  
Level Data ◽  
Master System ◽  
Data Source ◽  
Topology Information

In this paper, a method of distributed state estimation in dispatch center based on ripe data and bus topology information of substation is proposed. Substation state estimation solves the reliability issues of basic data by means of processing the redundant information in substation side. By using a large amount of data source information of substation, state estimation can be performed efficiently in substation. On the basis of the unified data standard between master center and substation, uploading ripe data and calculation nodes topology structure are acquired in dispatch center level. Data rationality and consistency check of substation improve the state estimation accuracy of topology identification in dispatch master system. And also it provides reliable data for the online analysis software.

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.

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