# 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} }

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 Gaussianâ€¦Â

## 23 Citations

The Kernel-SME filter with false and missing measurements

- Computer Science2016 19th International Conference on Information Fusion (FUSION)
- 2016

This work shows how the Kernel-SME approach can systematically incorporate false and missing measurements and allows for deriving an efficient closed-form Gaussian filter based on the Kalman filter formulas.

The SME filter for multiple extended targets tracking

- Physics2017 20th International Conference on Information Fusion (Fusion)
- 2017

This paper presents an approach named symmetric measurement equation (SME) to track known number of multiple extended targets using SMEs which define new measurements through the sums of products of the original measurements.

Symmetrizing measurement equations for association-free multi-target tracking via point set distances

- MathematicsDefense + Security
- 2017

By employing a permutation-invariant and differentiable point set distance measure, a modified association-free multi-target measurement equation is derived, which maintains the target identities but is invariant to permutations in the unlabeled measurements.

Distributed spatio-temporal association and tracking of multiple targets using multiple sensors

- Engineering, Computer ScienceIEEE Transactions on Aerospace and Electronic Systems
- 2015

This novel sensors-to-targets association scheme is integrated with PF mechanisms to perform accurate tracking, and different from existing alternatives, the novel algorithm can efficiently track and associate targets with sensors even in noisy settings.

A likelihood-free particle filter for multi-obiect tracking

- Computer Science2017 20th International Conference on Information Fusion (Fusion)
- 2017

A particle filter for multi-object tracking that is based on the ideas of the Approximate Bayesian Computation (ABC) paradigm and selected the closest simulated measurements and their corresponding particles in state space, the posterior distribution is approximated.

Fading Unscentedâ€“Extended Kalman Filter for Multiple Targets Tracking with Symmetric Equations of Nonlinear Measurements

- Mathematics, Engineering
- 2016

A nonlinear stochastic model with unknown random bias is developed to provide a unified structure for the tracking systems with different types of symmetric measurement equations.

Association-free direct filtering of multi-target random finite sets with set distance measures

- Mathematics, Computer Science2015 18th International Conference on Information Fusion (Fusion)
- 2015

Particle-based random finite set densities are used for characterizing the RFS in a simple and natural way for association-free tracking of multiple targets without identities.

Data association â€” solution or avoidance: Evaluation of a filter based on RFS framework and factor graphs with SME

- Computer Science2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
- 2017

This paper compares and contrast the state estimation using state-of-the-art Random Finite Set (RFS) approach and using a Factor Graph solution with SMEs, which shows the performance of GLMB Filter degrades faster than Factor Graphs using SMEs when the error in the sensors increase.

Tracking Extended Objects in Noisy Point Clouds with Application in Telepresence Systems

- Computer Science
- 2016

An approach to track the parameters of arbitrary objects is proposed, which provides new solutions to the above challenges, and marks a significant advance to the state of the art.

The Smart Sampling Kalman Filter with Symmetric Samples

- Computer Science, MathematicsArXiv
- 2015

This paper extends the Smart Sampling Kalman Filter with a new point symmetric Gaussian sampling scheme, which improves the S2KF's estimation quality, but also reduces the time needed to compute the required optimal Gaussian samples drastically.

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