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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lundquist, C. Granstrom, K. Orguner, U. |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Electrical Engineering, Linköping University, 583 33 Linköping, Sweden (Lundquist, C.; Granstrom, K.; Orguner, U.) |
| Abstract | This paper presents a framework for tracking extended targets which give rise to a structured set of measurements per each scan. The concept of a measurement generating point (MGP) which is defined on the boundary of each target is introduced. The tracking framework contains an hybrid state space where MGP:s and the measurements are modeled by random finite sets and target states by random vectors. The target states are assumed to be partitioned into linear and nonlinear components and a Rao-Blackwellized particle filter is used for their estimation. For each state particle, a probability hypothesis density (PHD) filter is utilized for estimating the conditional set of MGP:s given the target states. The PHD kept for each particle serves as a useful means to represent information in the set of measurements about the target states. The early results obtained show promising performance with stable target following capability and reasonable shape estimates. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 613440 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781457702679 |
| e-ISBN | 9780982443828 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-07-05 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Intl Society of Info Fusion |
| Subject Keyword | Sensors Target tracking Particle measurements Atmospheric measurements Shape Approximation methods Mathematical model Rao-Blackwellized particle filter Tracking data association particle filter Kalman filter estimation PHD filter extended target |
| Content Type | Text |
| Resource Type | Article |
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