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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kaplan, L.M. Blair, W.D. Bar-Shalom, Y. |
| Copyright Year | 2006 |
| Description | Author affiliation: Army Res. Lab., Adelphi, MD (Kaplan, L.M.) |
| Abstract | Recent work has developed maximum likelihood (ML) methods for track-to-track data association and fusion in a multisensor, i.e., more than two sensor, environment. In order to conserve bandwidth, only the state estimates and corresponding covariance matrices are shared amongst the nodes. The fusion engine uses this track information to determine which tracks associate to the same target and then computes a fused track to improve the accuracy of the state estimates. The simplest class of ML methods assumes that the track errors from different sensors are uncorrelated. The more computationally demanding ML methods incorporate the cross-correlations that are due to the common process noise in the kinematic model of the target. In order to account for track correlations in practice, the cross-covariance matrices must be approximated from the single sensor covariance matrices. This paper introduces new methods to approximate the cross-covariance matrices, and these approximations lead to a third class of association and estimation methods. The paper then uses simulations to assess the performance of the different association and estimation techniques. The simulations include results when the sensor tracks are produced by either a Kalman filter or an interacting multiple model (IMM) filter |
| Sponsorship | Aerospace and Electron. Syst. Soc |
| File Size | 3393330 |
| File Format | |
| ISBN | 078039545X |
| DOI | 10.1109/AERO.2006.1655913 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-03-04 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Target tracking Sensor fusion State estimation Maximum likelihood estimation Covariance matrix Bandwidth Testing Time measurement Computer architecture Engines target tracking Track-to-track association likelihood ratio tests sensor fusion data association |
| Content Type | Text |
| Resource Type | Article |
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