Kalman Filtering With Adaptive Step Size Using a Covariance-Based Criterion
- 2 March 2021
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement
- Vol. 70, 1-10
- https://doi.org/10.1109/tim.2021.3063191
Abstract
In Kalman filtering (KF), a tradeoff exists when selecting the filter step size. Generally, a smaller step size improves the estimation accuracy, yet with the cost of a high computational load. To mitigate this tradeoff influence on performance, a criterion that acts as a guideline for a reasonable choice of the step size is proposed. This criterion is based on the predictor–corrector error covariance matrices of the discrete KF. In addition, this criterion is elaborated to an adaptive algorithm, for the case of the time-varying measurement noise covariance. Two simulation examples and a field experiment using a quadcopter are presented and analyzed to show the benefits of the proposed approach.Keywords
This publication has 17 references indexed in Scilit:
- A Novel Kalman Filter for Human Motion Tracking With an Inertial-Based Dynamic InclinometerIEEE Transactions on Biomedical Engineering, 2015
- Reducing Low-Cost INS Error Accumulation in Distance Estimation Using Self-ResettingIEEE Transactions on Instrumentation and Measurement, 2013
- Assessment of Aided-INS PerformanceJournal of Navigation, 2011
- A Novel Approach for Modeling Land Vehicle Kinematics to Improve GPS Performance Under Urban Environment ConditionsIEEE Transactions on Intelligent Transportation Systems, 2011
- Improving Estimation of Vehicle's Trajectory Using the Latest Global Positioning System With Kalman FilteringIEEE Transactions on Instrumentation and Measurement, 2011
- Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and SimulationsIEEE Transactions on Signal Processing, 2010
- The Ornstein–Uhlenbeck process as a model of a low pass filtered white noiseMetrologia, 2008
- Energy-Efficient Distributed Adaptive Multisensor Scheduling for Target Tracking in Wireless Sensor NetworksIEEE Transactions on Instrumentation and Measurement, 2008
- Adaptive sampling for sensor networksPublished by Association for Computing Machinery (ACM) ,2004
- A New Approach to Linear Filtering and Prediction ProblemsJournal of Basic Engineering, 1960