Tracking and Kalman Filtering Made Easy

Brookner, Eli

doi:10.1002/0471224197

Cited by 318 publications

(221 citation statements)

References 37 publications

Supporting

Mentioning

213

Contrasting

Unclassified

Order By: Relevance

“…The key property of the EKF is that it is applied to neural network training, it leads to faster convergence property than the gradient-based algorithm.The EKF algorithm provides first-order approximations to optimal nonlinear estimation through the linearization of the nonlinear system. However, these approximations can include large errors in the true posterior mean and covariance of the transformed (Gaussian ) random svariable, which may lead to suboptimal performance and sometimes divergence of the filter [12].…”

Section: Kalman Filter Techniquementioning

confidence: 99%

Estimation Of Global Positioning System Measurement Errors For GAGAN Applications

Baba¹,

Rao²

2014

IOSRJECE

View full text Add to dashboard Cite

Abstract:The Global Positioning System (GPS) The instrumental biases exist as the signals at the two GPS frequencies experience different delays inside the satellite and receiver hardware. For estimation of the instrumental biases Extended Kalman filter(EKF) technique is adopted. The user equivalent range error (UERE) obtained due to all the error sources is of the order of few metres. After accounting for various errors, the estimation accuracy is significantly improved.The positional accuracy of the GPS Aided GEO Augmented Navigation (GAGAN) system is basically dependent on ranging errors and the satellite constellation geometry[1]. The lineof-sight ionospheric measurements derived from the Global Positioning System (GPS) observables are corrupted by the instrumental biases present in both the GPS satellites and the receivers. The instrumental bias and Total Electron Content (TEC) results (Hyderabad GAGAN station (78.47°E, 17.45°N)) obtained using theExtended Kalman filter technique are presented in this paper. In this work the main focus is towards development of an improved error free approach for aircraft navigation. The existing navigation solutions are based on iterative algorithms and

show abstract

Section: Kalman Filter Techniquementioning

confidence: 99%

Estimation Of Global Positioning System Measurement Errors For GAGAN Applications

Baba¹,

Rao²

2014

IOSRJECE

View full text Add to dashboard Cite

show abstract

“…In some cases, the algorithms to track mouse positions assume a constant speed movement model. This assumption is reasonable since that sample period is very small compared with the movement speeds, Brookner (1998), i.e., the sample period adopted was 20ms and the voluntary movement estimated occurs in a bandwidth lower than 2Hz (75% of the power spectral density, Raya et al (2011)). …”

Section: Adaptive Filtersmentioning

confidence: 99%

New Strategies of Mobility and Interaction for People with Cerebral Palsy

Raya¹,

Rocón²,

Ceres³

et al. 2012

Assistive Technologies

View full text Add to dashboard Cite

“…First, we build a background model to segment foreground objects. Then, the PTMS algorithm is used to achieve two goals, namely 1) predicting the position of the detected vehicles, by means of a KF [24] and 2) performing a time-consistent analysis (grouping) of the detected blobs, possibly merging and splitting detected blobs due to partial or complete occlusions. This process permits the identification of blobs composed of multiple vehicles that should not be used as input to the trajectory clustering algorithm, because the position estimation may be unreliable.…”

Section: Object Tracking With the Predictive Trajectory Merge-anmentioning

confidence: 99%

Detection and Classification of Highway Lanes Using Vehicle Motion Trajectories

Melo

Naftel

Bernardino

et al. 2006

IEEE Trans. Intell. Transport. Syst.

116

View full text Add to dashboard Cite

Abstract-Intelligent vision-based traffic surveillance systems are assuming an increasingly important role in highway monitoring and road management schemes. This paper describes a low-level object tracking system that produces accurate vehicle motion trajectories that can be further analyzed to detect lane centers and classify lane types. Accompanying techniques for indexing and retrieval of anomalous trajectories are also derived. The predictive trajectory merge-and-split algorithm is used to detect partial or complete occlusions during object motion and incorporates a Kalman filter that is used to perform vehicle tracking. The resulting motion trajectories are modeled using variable low-degree polynomials. A K-means clustering technique on the coefficient space can be used to obtain approximate lane centers. Estimation bias due to vehicle lane changes can be removed using robust estimation techniques based on Random Sample Consensus (RANSAC). Through the use of nonmetric distance functions and a simple directional indicator, highway lanes can be classified into one of the following categories: entry, exit, primary, or secondary. Experimental results are presented to show the real-time application of this approach to multiple views obtained by an uncalibrated pan-tilt-zoom traffic camera monitoring the junction of two busy intersecting highways.

show abstract

Tracking and Kalman Filtering Made Easy

Cited by 318 publications

References 37 publications

Estimation Of Global Positioning System Measurement Errors For GAGAN Applications

Estimation Of Global Positioning System Measurement Errors For GAGAN Applications

New Strategies of Mobility and Interaction for People with Cerebral Palsy

Detection and Classification of Highway Lanes Using Vehicle Motion Trajectories

Contact Info

Product

Resources

About