ScholarLens Back to search
2007•Radio Engineering of ChinaRequires access

Application of Adaptive Extended Kalman Filter in Passive Targets Tracking

Fang Ling-wei

Open publisher page 0 citations

Abstract

In this paper,we present an adaptive extended Kalman filter(AEKF) algorithm aiming at the issues such as divergence,slow convergence and low precision of filters in passive targets tracking.The algorithm can estimate the statistics features of the virtual state noise on-line and compensate the error caused by model’s non-linearization and reduce the error in observation for systems.Simulation results show that the algorithm improves the filtering convergence rate and the accuracy,and is better than the original extended Kalman filter(EKF).

About this research paper

What this paper is about

In this paper,we present an adaptive extended Kalman filter(AEKF) algorithm aiming at the issues such as divergence,slow convergence and low precision of filters in passive targets tracking.The algorithm can estimate the statistics features of the virtual state noise on-line and compensate the error caused by model’s non-linearization and reduce the error in observation for systems.Simulation results show that the algorithm improves the filtering convergence rate and the accuracy,and is better than the original extended Kalman filter(EKF).

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

In this paper,we present an adaptive extended Kalman filter(AEKF) algorithm aiming at the issues such as divergence,slow convergence and low precision of filters in passive targets tracking.The algorithm can estimate the statistics features of the virtual state noise on-line and compensate the error caused by model’s non-linearization and reduce the error in observation for systems.Simulation results show that the algorithm improves the filtering convergence rate and the accuracy,and is better than the original extended Kalman filter(EKF).

Key concepts: Invariant extended Kalman filter, Extended Kalman filter, Kalman filter, Control theory (sociology), Divergence (linguistics), Fast Kalman filter, Alpha beta filter, Computer science

Related papers

Related paper 1Related paper 2Related paper 3
Back to paper searchBrowse research topicsOriginal source
Application of Adaptive Extended Kalman Filter in Passive Targets Tracking — Research Paper | ScholarLens