• Corpus ID: 2797757

The Kernel-SME filter for multiple target tracking

@article{Baum2013TheKF,
  title={The Kernel-SME filter for multiple target tracking},
  author={Marcus Baum and Uwe D. Hanebeck},
  journal={Proceedings of the 16th International Conference on Information Fusion},
  year={2013},
  pages={288-295}
}
  • M. Baum, U. Hanebeck
  • Published 24 December 2012
  • Computer Science
  • Proceedings of the 16th International Conference on Information Fusion
We present a novel method for tracking multiple targets, called Kernel-SME filter, that does not require an enumeration of measurement-to-target associations. This method is a further development of the symmetric measurement equation (SME) filter that removes the data association uncertainty of the original measurement equation with the help of a symmetric transformation. The key idea of the Kernel-SME filter is to define a symmetric transformation that maps the measurements to a Gaussian… 

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References

SHOWING 1-10 OF 31 REFERENCES
Optimal Gaussian filtering for polynomial systems applied to association-free multi-target tracking
TLDR
An efficient optimal Gaussian filter based on analytic moment calculation for discrete-time multi-dimensional polynomial systems corrupted with Gaussian noise is derived, and then applied to the poynomial system resulting from the SME filter.
Multidimensional SME filter for multitarget tracking
TLDR
An improved multi-dimensional SME tracking algorithm which agrees with Kamen's for one-dimensional scenarios and avoids the ghost target problem in higher dimensions is presented and a more efficient method for computing the noise covariance matrix of the SME coefficients is provided.
Multiple target tracking using products of position measurements
The continued development of the symmetric measurement equation (SME) filter for track maintenance in multiple target tracking (MTT) is considered, focusing on the case in which the SMEs are
Optimal point estimates for multi-target states based on kernel distances
TLDR
This paper shows how the calculation of point estimates for multi-target states that are optimal according to a kernel distance measure can be casted as an optimization problem and it turns out that it corresponds to the problem of reducing the Probability Hypothesis Density (PHD) function to a Dirac mixture density.
A Bayesian Approach to Multiple Target Detection and Tracking
TLDR
Simulation results, with measurements generated from real target trajectories, demonstrate the ability of the proposed procedure to simultaneously detect and track ten targets with a reasonable sample size.
Multiple Target Tracking based on Symmetric Measurement Equations
  • E. Kamen
  • Physics
    1989 American Control Conference
  • 1989
A new approach to track maintenance in multiple target tracking is presented in terms of measurements which are symmetric functions of target positions. The first-order version of the target state
Unscented Kalman Filters for Multiple Target Tracking With Symmetric Measurement Equations
TLDR
An SME/unscented Kalman filter pairing is shown to have improved performance versus previous approaches while possessing simpler implementation and equivalent computational complexity.
Multitarget Tracking of Distributed Targets Using Histogram-PMHT
TLDR
The expectation-maximization method is applied to derive a stable tracking algorithm that uses the entire display (image) as its input data, completely avoiding peak picking and other data compression steps required to produce traditional point measurements.
A Comparison of Detection Performance for Several Track-before-Detect Algorithms
TLDR
The ability of several different approaches to detect low amplitude targets by removing the detection algorithm and supplying the sensor data directly to the tracker is compared.
Multiple target tracking with symmetric measurement equations using unscented Kalman and particle filters
  • W. F. Leven, A. Lanterman
  • Engineering, Physics
    Thirty-Sixth Southeastern Symposium on System Theory, 2004. Proceedings of the
  • 2004
TLDR
The symmetric measurement equation approach to multiple target tracking is revisited using unscented Kalman and particle filters and the point is made that the performance of the SME approach is dependent on the interaction of the set of SME equations and the filter used.
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