Abstract
In multi-sensor fusion (MSF), the integration of multi-sensor observation data with different observation errors to achieve more accurate positioning of the target has always been a research focus. In this study, a modified ensemble Kalman filter (EnKF) is presented to substitute the traditional Kalman filter (KF) in the multiple hypotheses tracking (MHT) to deal with the high nonlinearity that always shows up in multiple target tracking (MTT) problems. In addition, the multi-source observation data fusion is also realized by using the modified EnKF, which enables the low-precision observation data to be corrected by high-precision observation data, and the accuracy of the corrected data can be calibrated by the statistical information provided by the EnKF. Numerical studies are given to demonstrate the effectiveness of our proposed method and the results show that the MHT-EnKF method can achieve remarkable enhancement in dealing with nonlinear movement variation and positioning accuracy for MTT problems in MSF scenario.
Highlights
In multi-sensor surveillance systems like radar-based tracking, sonar-based tracking, and video-based tracking, multiple target tracking (MTT) is a vital problem that always arises
This study proved that the ensemble Kalman filter (EnKF) method is superior to the EKF method for the target tracking result
We proposed a modified EnKF to substitute the Kalman filter (KF) in multiple hypotheses tracking (MHT) to deal with the nonlinearity in the MTT and MFS scenarios
Summary
In multi-sensor surveillance systems like radar-based tracking, sonar-based tracking, and video-based tracking, multiple target tracking (MTT) is a vital problem that always arises. Zhang et al [22] established an improved EnKF model to solve an asynchronous data fusion of a target tracking nonlinearity problem with the coexistence of velocity and acceleration. These studies have not extended the EnKF approach to MTT scenarios. We proposed a modified EnKF to substitute the KF in MHT to deal with the nonlinearity in the MTT and MFS scenarios This allows us to consider the coupling of acceleration and velocity with nonlinear kinematic equations in each time interval.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
