Tracking of Moving Objects Based on Extended Kalman Filter

2020 ◽  
Author(s):  
Tarik Omeragic ◽  
Jasmin Velagic
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Moving Objects

scholarly journals Tracking Objects on Detector Response using Extended Kalman Filter

2020 ◽  
Vol 9 (4) ◽  
pp. 1614-1620
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Moving Objects ◽  
Video Frame ◽  
Detector Response ◽  
Data Set ◽  
Detection And Tracking ◽  
Cumulative Summation ◽  
Tracking Objects

Video analytics plays a very important role in identification or detection and tracking of objects, this intern find application in many fields and domains. Novel learning methods or techniques built on Neural Networks requires larger dataset for training the results, the output obtained depends on how well the training is done. The proposed method of Weighted Cumulative Summation (WCS) is an approach based on background modelling to segment the moving objects. This method adapts and tunes the background variations instantaneously as the video frame arrives. The segmentation obtained is compared with other basic methods. The result obtained infers improvements in segmentation and in removal of ghost effect in the video. Extended Kalman Filter (EKF) is used to track the detector response. The responses of the detection from WCS are provided as input to EKF to track the moving object. The results are tabulated and represented in the form of graphs for analysis. The results are compared with three different video datasets and the results are noticeably good. The methods WCS can be used in the applications were data set is not available.

Object Identification and Tracking via Noise Updated Iterative Extended Kalman Filter

Volume 1 ◽  
2004 ◽  
Author(s):  
A. Bondarenko ◽  
Y. Halevi ◽  
M. Shpitalni
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Moving Objects ◽  
Mathematical Formulation ◽  
Substantial Improvement ◽  
Object Identification ◽  
State Variables ◽  
Unknown Parameters ◽  
Method Of Solution ◽  
Simultaneous Identification

The paper considers the problem of simultaneous identification and trajectory tracking of moving objects (either 2D or 3D) from moving sensors. The identification is parametric and is based on knowing the family that the object belongs to, e.g. ball, ellipsoid, box, etc. The mathematical formulation results in implicit measurements, i.e. an algebraic equation that includes both state variables and actual measurements. The method of solution is via Extended Kalman Filter where the unknown parameters are regarded as additional state variables. Standard Extended Kalman Filter and Iterative Extended Kalman Filter yielded unsatisfactory results, mainly due to the nonlinearity of the measurements in both the state vector and the noise. A new algorithm, called Noise Updated Iterative Extended Kalman Filter is suggested. Its deviation from the standard iterative Kalman filter is in estimating the measurement noise at each iteration. The estimated noise is then used in the linearization stage to obtain a more accurate linear approximation. The method has been applied to the online identification and tracking problem, with substantial improvement in performance.

scholarly journals Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4802 ◽  
Author(s):  
Martin Schmidhammer ◽  
Christian Gentner ◽  
Benjamin Siebler ◽  
Stephan Sand
Keyword(s):  
Kalman Filter ◽  
Lower Bound ◽  
Extended Kalman Filter ◽  
Optimization Problem ◽  
Moving Objects ◽  
Multipath Propagation ◽  
Measurement Data ◽  
Position Estimation ◽  
Performance Bounds ◽  
Cramer Rao Lower Bound

This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scatterers is formulated as a nonlinear optimization problem. An iterative nonlinear least squares algorithm following Levenberg and Marquardt is used for solving the optimization problem initially, and an extended Kalman filter is used for estimating the scatterer location recursively over time. The corresponding performance bounds are derived for both the snapshot based position estimation and the nonlinear sequential Bayesian estimation with the classic and the posterior Cramér–Rao lower bound. Thereby, a comparison of simulation results to the posterior Cramér–Rao lower bound confirms the applicability of the extended Kalman filter. The proposed approach is applied to estimate the position of a walking pedestrian sequentially based on wideband measurement data in an outdoor scenario. The evaluation shows that the pedestrian can be localized throughout the scenario with an accuracy of 0 . 8 m at 90% confidence.

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