Measurement Noise Mitigation Using Cascaded Unscented Kalman Filter

. For effectively using flight test data to extract drag coefficient, an optimal observer based on parameter estimation technique is proposed. The point mass dynamic equation is used to form the Unscented Kalman Filter (UKF) and the smoother (URTSS) for the estimation of a projectile’s flight states. The projectile flight states are then solved and utilized to extract the drag coefficient information using the observer techniques. The simulation verifies the feasibility of the method: with measurement noise, the accurate drag coefficient is obtained by using the smoother.

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A normal‐gamma‐based adaptive dual unscented Kalman filter for battery parameters and state‐of‐charge estimation with heavy‐tailed measurement noise

International Journal of Energy Research ◽

10.1002/er.5042 ◽

2020 ◽

Vol 44 (5) ◽

pp. 3510-3525 ◽

Cited By ~ 2

Author(s):

Jing Hou ◽

Yan Yang ◽

Tian Gao

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

State Of Charge ◽

Measurement Noise ◽

Heavy Tailed ◽

State Of Charge Estimation

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Passive Location of Angle Sensors Using Extend Square-Root Cubature Kalman Filter

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.143-144.577 ◽

2011 ◽

Vol 143-144 ◽

pp. 577-581 ◽

Cited By ~ 1

Author(s):

Yang Zhang ◽

Guo Sheng Rui ◽

Jun Miao

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

Measurement Noise ◽

Filter Method ◽

Square Root ◽

Passive Location ◽

Mode Decomposition ◽

Measurement Noise Covariance ◽

Cubature Kalman Filter ◽

Noise Covariance

A new nonlinear filter method Cubature Kalman Filter (CKF) is improved for passive location with moving angle-measured sensors’ measurements.Firstly,it used Empirical Mode Decomposition (EMD) algorithm to estimate measurement noise covariance; And then the covariance of the procession noise and measurement noise is brought into the circle; Meanwhile,CKF is improved by the way of square root to keep its stability and positivity,and the results of track by Extend SCKF are compared with the results by Unscented Kalman Filter (UKF) in the text;By the tracking results to the velocity of the target, Extend SCKF algorithm can not only track the target with unknown measurement noise but also improve the passive position precision remarkably as the same difficulty as UKF.

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Line-of-Sight Angle Rate Reconstruction for Phased Array Strapdown Seeker

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.645.196 ◽

2013 ◽

Vol 645 ◽

pp. 196-201

Author(s):

Ying Liu ◽

Wei Feng Tian ◽

Jian Kang Zhao ◽

Shi Qing Zhu ◽

Ge Wen Yang

Keyword(s):

Kalman Filter ◽

Phased Array ◽

Unscented Kalman Filter ◽

Measurement Noise ◽

Line Of Sight ◽

Measurement Information ◽

Reconstruction Method ◽

Proportional Navigation ◽

Filtering Algorithm ◽

Simulation Results

The phased array strapdown radar seeker’s detecting information is coupled with missile attitude information. Hence, the measurement information can not be used for proportional navigation directly. The method of reconstructing inertial line of sight (LOS) rate in phased array strapdown seeker is presented using the missile-target relative motion geometric and filtering algorithm. Considering measurement noise and nonlinearity of the phased array strapdown radar guidance systems, the principle of unscented kalman filter (UKF) is introduced to estimate LOS rate. The simulation results show that the reconstruction method is correct and the extraction of LOS rate is effective.

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Adaptive Unscented Kalman Filter for Target Tacking with Time-Varying Noise Covariance Based on Multi-Sensor Information Fusion

Sensors ◽

10.3390/s21175808 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5808

Author(s):

Dapeng Wang ◽

Hai Zhang ◽

Baoshuang Ge

Keyword(s):

Kalman Filter ◽

Information Fusion ◽

Stochastic Systems ◽

Unscented Kalman Filter ◽

Minimum Variance ◽

Measurement Noise ◽

Time Varying ◽

Measurement Noise Covariance ◽

Noise Covariance ◽

Sensor Information

In this paper, an innovative optimal information fusion methodology based on adaptive and robust unscented Kalman filter (UKF) for multi-sensor nonlinear stochastic systems is proposed. Based on the linear minimum variance criterion, this multi-sensor information fusion method has a two-layer architecture: at the first layer, a new adaptive UKF scheme for the time-varying noise covariance is developed and serves as a local filter to improve the adaptability together with the estimated measurement noise covariance by applying the redundant measurement noise covariance estimation, which is isolated from the state estimation; the second layer is the fusion structure to calculate the optimal matrix weights and gives the final optimal state estimations. Based on the hypothesis testing theory with the Mahalanobis distance, the new adaptive UKF scheme utilizes both the innovation and the residual sequences to adapt the process noise covariance timely. The results of the target tracking simulations indicate that the proposed method is effective under the condition of time-varying process-error and measurement noise covariance.

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Huber-Based Adaptive Unscented Kalman Filter with Non-Gaussian Measurement Noise

Circuits Systems and Signal Processing ◽

10.1007/s00034-017-0736-x ◽

2018 ◽

Vol 37 (9) ◽

pp. 3842-3861 ◽

Cited By ~ 7

Author(s):

Bing Zhu ◽

Lubin Chang ◽

Jiangning Xu ◽

Feng Zha ◽

Jingshu Li

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

Measurement Noise ◽

Adaptive Unscented Kalman Filter ◽

Non Gaussian

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Navigation Performance Enhancement Using IMM Filtering for Time Varying Satellite Signal Quality

E3S Web of Conferences ◽

10.1051/e3sconf/20199402004 ◽

2019 ◽

Vol 94 ◽

pp. 02004

Author(s):

Dah-Jing Jwo ◽

Shu-Ming Chang ◽

Jen-Hsien Lai

Keyword(s):

Fuzzy Logic ◽

Kalman Filter ◽

Unscented Kalman Filter ◽

Measurement Noise ◽

Statistical Characteristics ◽

Signal Quality ◽

Time Varying ◽

Multiple Model ◽

Gps Navigation ◽

Satellite Signal

A novel scheme using fuzzy logic based interacting multiple model (IMM) unscented Kalman filter (UKF) is employed in which the Fuzzy Logic Adaptive System (FLAS) is utilized to address uncertainty of measurement noise, especially for the outlier types of multipath errors for the Global Positioning System (GPS) navigation processing. Multipath is known to be one of the dominant error sources, and multipath mitigation is crucial for improvement of the positioning accuracy. It is not an easy task to establish precise statistical characteristics of measurement noise in practical engineering applications. Based on the filter structural adaptation, the IMM nonlinear filtering provides an alternative for designing the adaptive filter in the GPS navigation processing for time varying satellite signal quality. The uncertainty of the noise can be described by a set of switching models using the multiple model estimation. An UKF employs a set of sigma points by deterministic sampling, which avoids the error caused by linearization as in an extended Kalman filter (EKF). For enhancing further system flexibility, the fuzzy logic system is introduced. The use of IMM with FLAS enables tuning of appropriate values for the measurement noise covariance so as to obtain improved estimation accuracy. Performance assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing.

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Centralized Fusion of Unscented Kalman Filter Based on Huber Robust Method for Nonlinear Moving Target Tracking

Mathematical Problems in Engineering ◽

10.1155/2015/291913 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Jue Huang ◽

Bing Yan ◽

Shouwei Hu

Keyword(s):

Kalman Filter ◽

Kalman Filters ◽

Robust Statistics ◽

Unscented Kalman Filter ◽

Measurement Noise ◽

Moving Target ◽

Robust Method ◽

Noise Variance ◽

Accurate Performance ◽

Simulation Results

We propose a robust method for tracking nonlinear target with the fusion unscented Kalman filter (FUKF). We noticed that when some outliers exist in the measurements of the sensors, they cannot track the target accurately by using the standard Kalman filters. The robust statistics theory is used in this paper to solve this problem. The measurement noise variance which is at the time of the outlier is restructured through minimizing the designed cost function. Then, the standard fusion unscented Kalman filter is used to track the target in order to avoid the bias brought by the linear approximation. Compared to the traditional tracking method and Huber robust method (HFUKF), this method has a more accurate performance and can track the target efficiently while the outliers exist. Last, simulation examples in three different conditions are given and the simulation results show the advantages of the proposed method over the fusion unscented Kalman filter (FUKF) and the Huber robust method (HFUKF).

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An improved unscented Kalman filter based dynamic state estimation algorithm for electric distribution Systems

COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering ◽

10.1108/compel-12-2016-0586 ◽

2017 ◽

Vol 36 (4) ◽

pp. 1220-1236 ◽

Cited By ~ 2

Author(s):

Fiaz Ahmad ◽

Kabir Muhammad Abdul Rashid ◽

Akhtar Rasool ◽

Esref Emre Ozsoy ◽

Asif Sabanoviç ◽

...

Keyword(s):

Kalman Filter ◽

State Estimation ◽

Radial Distribution ◽

Distribution Systems ◽

Unscented Kalman Filter ◽

Distribution Networks ◽

Measurement Noise ◽

Content Type ◽

Load Variations ◽

Bad Data

Purpose To propose an improved algorithm for the state estimation of distribution networks based on the unscented Kalman filter (IUKF). The performance comparison of unscented Kalman filter (UKF) and newly developed algorithm, termed Improved unscented Kalman Filter (IUKF) for IEEE-30, 33 and 69-bus radial distribution networks for load variations and bad data for two measurement noise scenarios, i.e. 30 and 50 per cent are shown. Design/methodology/approach State estimation (SE) plays an instrumental role in realizing smart grid features like distribution automation (DA), enhanced distribution generation (DG) penetration and demand response (DR). Implementation of DA requires robust, accurate and computationally efficient dynamic SE techniques that can capture the fast changing dynamics of distribution systems more effectively. In this paper, the UKF is improved by changing the way the state covariance matrix is calculated, to enhance its robustness and accuracy under noisy measurement conditions. UKF and proposed IUKF are compared under the cummulative effect of load variations and bad data based on various statistical metrics such as Maximum Absolute Deviation (MAD), Maximum Absolute Per cent Error (MAPE), Root Mean Square Error (RMSE) and Overall Performance Index (J) for three radial distribution networks. All the simulations are performed in MATLAB 2014b environment running on an hp core i5 laptop with 4GB memory and 2.6 GHz processor. Findings An Improved Unscented Kalman Filter Algorithm (IUKF) is developed for distribution network state estimation. The developed IUKF is used to predict network states (voltage magnitude and angle at all buses) and measurements (source voltage magnitude, line power flows and bus injections) in the presence of load variations and bad data. The statistical performance of the coventional UKF and the proposed IUKF is carried out for a variety of simulation scenarios for IEEE-30, 33 and 69 bus radial distribution systems. The IUKF demonstrated superiority in terms of: RMSE; MAD; MAPE; and overall performance index J for two measurement noise scenarios (30 and 50 per cent). Moreover, it is shown that for a measurement noise of 50 per cent and above, UKF fails while IUKF performs. Originality/value UKF shows degraded performance under high measurement noise and fails in some cases. The proposed IUKF is shown to outperform the UKF in all the simulated scenarios. Moreover, this work is novel and has justified improvement in the robustness of the conventional UKF algorithm.

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