Receding Nonlinear Kalman (RNK) Filter for Nonlinear Constrained State Estimation

Raghunathan Rengaswamy; Shankar Narasimhan; Vidyashankar Kuppuraj

doi:10.1016/b978-0-444-53711-9.50169-3

ScienceGate Book Chapters

BOOK-CHAPTER

Receding Nonlinear Kalman (RNK) Filter for Nonlinear Constrained State Estimation

Raghunathan Rengaswamy Shankar Narasimhan Vidyashankar Kuppuraj

Year: 2011 Computer-aided chemical engineering/Computer aided chemical engineering Pages: 844-848 Publisher: Elsevier BV

DOI: 10.1016/b978-0-444-53711-9.50169-3

Get Full-Text PDF Get Analytical Report

Keywords:

Extended Kalman filter Control theory (sociology) Kalman filter Nonlinear system Horizon Linearization Covariance Mathematics Computer science Artificial intelligence Control (management)

Metrics

Cited By

2.21

FWCI (Field Weighted Citation Impact)

Refs

0.88

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Fault Detection and Control Systems

Physical Sciences → Engineering → Control and Systems Engineering

Advanced Control Systems Optimization

Physical Sciences → Engineering → Control and Systems Engineering

Target Tracking and Data Fusion in Sensor Networks

Physical Sciences → Computer Science → Artificial Intelligence

Receding Nonlinear Kalman (RNK) Filter for Nonlinear Constrained State Estimation

Metrics

Citation History

Topics

Related Documents

Receding-Horizon Nonlinear Kalman (RNK) Filter for State Estimation

CONSTRAINED EXTENDED KALMAN FILTER FOR NONLINEAR STATE ESTIMATION

Simultaneous State and Parameter Estimation using Receding-horizon Nonlinear Kalman Filter

Simultaneous State and Parameter Estimation using Robust Receding-horizon Nonlinear Kalman Filter

Simultaneous and sequential state and parameter estimation using receding-horizon nonlinear Kalman filter