A multi‐tone central divided difference frequency tracker with adaptive process noise covariance tuning

2020 ◽  
Vol 34 (7) ◽  
pp. 877-900
Author(s):  
Alessandro Brumana ◽  
Luigi Piroddi
Keyword(s):  
Divided Difference ◽  
Difference Frequency ◽  
Process Noise ◽  
Adaptive Process ◽  
Noise Covariance

Flexible polarization demultiplexing method based on an adaptive process noise covariance Kalman filter

2018 ◽  
Vol 16 (6) ◽  
pp. 060601
Author(s):  
Jun Ge Jun Ge ◽  
Lianshan Yan Lianshan Yan ◽  
Anlin Yi Anlin Yi ◽  
Yan Pan Yan Pan ◽  
Lin Jiang Lin Jiang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Process Noise ◽  
Adaptive Process ◽  
Noise Covariance

Improving Adaptive Kalman Estimation in GPS/INS Integration

2007 ◽  
Vol 60 (3) ◽  
pp. 517-529 ◽  
Author(s):  
Weidong Ding ◽  
Jinling Wang ◽  
Chris Rizos ◽  
Doug Kinlyside
Keyword(s):  
A Priori ◽  
Optimal Solution ◽  
Window Size ◽  
Stochastic Modelling ◽  
Process Noise ◽  
Stringent Requirement ◽  
Adaptive Process ◽  
Covariance Matching ◽  
Noise Covariance ◽  
Optimal Window

The central task of GPS/INS integration is to effectively blend GPS and INS data together to generate an optimal solution. The present data fusion algorithms, which are mostly based on Kalman filtering (KF), have several limitations. One of those limitations is the stringent requirement on precise a priori knowledge of the system models and noise properties. Uncertainty in the covariance parameters of the process noise (Q) and the observation errors (R) may significantly degrade the filtering performance. The conventional way of determining Q and R relies on intensive analysis of empirical data. However, the noise levels may change in different applications. Over the past few decades adaptive KF algorithms have been intensively investigated with a view to reducing the influence of the Q and R definition errors. The covariance matching method has been shown to be one of the most promising techniques. This paper first investigates the utilization of an online stochastic modelling algorithm with regards to its parameter estimation stability, convergence, optimal window size, and the interaction between Q and R estimations. Then a new adaptive process noise scaling algorithm is proposed. Without artificial or empirical parameters being used, the proposed adaptive mechanism has demonstrated the capability of autonomously tuning the process noise covariance to the optimal magnitude, and hence improving the overall filtering performance.

Detection of power quality disturbances using an adaptive process noise covariance Kalman filter

2018 ◽  
Vol 76 ◽  
pp. 34-49 ◽  
Author(s):  
Yanhui Xi ◽  
Zewen Li ◽  
Xiangjun Zeng ◽  
Xin Tang ◽  
Qiao Liu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Power Quality ◽  
Process Noise ◽  
Adaptive Process ◽  
Noise Covariance ◽  
Power Quality Disturbances

scholarly journals Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1126
Author(s):  
Zhentao Hu ◽  
Linlin Yang ◽  
Yong Jin ◽  
Han Wang ◽  
Shibo Yang
Keyword(s):  
Real Time ◽  
Gaussian Mixture ◽  
Measurement Noise ◽  
Tracking Accuracy ◽  
Process Noise ◽  
Probability Hypothesis Density ◽  
Measurement Noise Covariance ◽  
Multi Target Tracking ◽  
Noise Covariance ◽  
Fading Factor

Assuming that the measurement and process noise covariances are known, the probability hypothesis density (PHD) filter is effective in real-time multi-target tracking; however, noise covariance is often unknown and time-varying for an actual scene. To solve this problem, a strong tracking PHD filter based on Variational Bayes (VB) approximation is proposed in this paper. The measurement noise covariance is described in the linear system by the inverse Wishart (IW) distribution. Then, the fading factor in the strong tracking principle uses the optimal measurement noise covariance at the previous moment to control the state prediction covariance in real-time. The Gaussian IW (GIW) joint distribution adopts the VB approximation to jointly return the measurement noise covariance and the target state covariance. The simulation results show that, compared with the traditional Gaussian mixture PHD (GM-PHD) and the VB-adaptive PHD, the proposed algorithm has higher tracking accuracy and stronger robustness in a more reasonable calculation time.

Online evaluation of the process noise covariance matrix for event‐based state estimators

2019 ◽  
Vol 120 (2) ◽  
pp. 195-208
Author(s):  
Miguel Martínez‐Rey ◽  
Carlos Santos ◽  
Rubén Nieto ◽  
Cristina Losada ◽  
Felipe Espinosa
Keyword(s):  
Covariance Matrix ◽  
Process Noise ◽  
Noise Covariance Matrix ◽  
Online Evaluation ◽  
State Estimators ◽  
Noise Covariance ◽  
Event Based

scholarly journals A System for Tracking an Autonomously Controlled Canine

2012 ◽  
Vol 65 (3) ◽  
pp. 427-444 ◽  
Author(s):  
Jeff Miller ◽  
George Flowers ◽  
David Bevly
Keyword(s):  
Fuzzy Logic Controller ◽  
Gps Receiver ◽  
Process Noise ◽  
Embedded Control ◽  
Noise Covariance Matrix ◽  
Adaptive Tuning ◽  
Noise Characteristics ◽  
Noise Covariance ◽  
Motion Characteristics ◽  
Tone Generation

This paper presents an approach for outdoor navigation of an autonomously guided canine using an embedded command module with vibration and tone generation capabilities and an embedded control suite comprised of a microprocessor, wireless radio, GPS receiver, and an Attitude and Heading Reference System. In order to determine the canine's motions, which inherently contain non-conventional noise characteristics, the sensor measurements were integrated using a specialized Extended Kalman Filter (EKF), equipped with a Fuzzy Logic controller for adaptive tuning of the Process Noise Covariance Matrix. This allowed for rejection of un-modelled canine motion characteristics which tend to corrupt accelerometer bias tracking in a standard EKF. The EKF solution provided an optimized estimate of the canine position and velocity and also proved to be effective in tracking the canine's position (within 7·5 m) and velocity (within 1·2 m/s) during simulated 10 second GPS outages.

scholarly journals Novel Process Noise Model for GNSS Kalman Filter Based on Sensitivity Analysis of Covariance with Poor Satellite Geometry

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6056
Author(s):  
Yoji Takayama ◽  
Takateru Urakubo ◽  
Hisashi Tamaki
Keyword(s):  
Kalman Filter ◽  
Estimation Error ◽  
Conventional Technique ◽  
Urban Environments ◽  
Analysis Of Covariance ◽  
Noise Model ◽  
Process Noise ◽  
Satellite Geometry ◽  
Noise Covariance ◽  
Filter Divergence

One of the great unsolved GNSS problems is inaccuracy in urban canyons due to Non-Line-Of-Sight (NLOS) signal reception. Owing to several studies about the NLOS signal rejection method, almost all NLOS signals can be excluded from the calculation of the position. However, such precise NLOS rejection would make satellite geometry poor, especially in dense urban environments. This paper points out, through numerical simulations and theoretical analysis, that poor satellite geometry leads to unintentional performance degradation of the Kalman filter with a conventional technique to prevent filter divergence. The conventional technique is to bump up process noise covariance, and causes unnecessary inflation of estimation-error covariance when satellite geometry is poor. We propose a novel choice of process noise covariance based on satellite geometry that can reduce such unnecessary inflation. Numerical and experimental results demonstrate that performance improvement can be achieved by the choice of process noise covariance even for a poor satellite geometry.

scholarly journals Adaptive Fifth-Degree Cubature Information Filter for Multi-Sensor Bearings-Only Tracking

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3241 ◽  
Author(s):  
Haonan Jiang ◽  
Yuanli Cai
Keyword(s):  
Information Filtering ◽  
Estimation Procedure ◽  
Estimation Accuracy ◽  
Selection Strategy ◽  
Bayesian Filtering ◽  
Process Noise ◽  
Covariance Matching ◽  
Information Filter ◽  
Cubature Kalman Filter ◽  
Noise Covariance

Standard Bayesian filtering algorithms only work well when the statistical properties of system noises are exactly known. However, this assumption is not always plausible in real target tracking applications. In this paper, we present a new estimation approach named adaptive fifth-degree cubature information filter (AFCIF) for multi-sensor bearings-only tracking (BOT) under the condition that the process noise follows zero-mean Gaussian distribution with unknown covariance. The novel algorithm is based on the fifth-degree cubature Kalman filter and it is constructed within the information filtering framework. With a sensor selection strategy developed using observability theory and a recursive process noise covariance estimation procedure derived using the covariance matching principle, the proposed filtering algorithm demonstrates better estimation accuracy and filtering stability. Simulation results validate the superiority of the AFCIF.

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