Estimating the shape of targets with a PHD filter

Lundquist, Christian
Granström, Karl
Orguner, Umut
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.


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Citation Formats
C. Lundquist, K. Granström, and U. Orguner, “Estimating the shape of targets with a PHD filter,” 2011, Accessed: 00, 2021. [Online]. Available: