Poisson Multi-Bernoulli Radar Mapping Using Gibbs Sampling
Preprint, 2016

This paper addresses the radar mapping problem. Using a conjugate prior form, we derive the exact theoretical batch multi-object posterior density of the map given a set of measurements. The landmarks in the map are modeled as extended objects, and the measurements are described as a Poisson process, conditioned on the map. We use a Poisson process prior on the map and prove that the posterior distribution is a hybrid Poisson, multi-Bernoulli mixture distribution. We devise a Gibbs sampling algorithm to sample from the batch multi-object posterior. The proposed method can handle uncertainties in the data associations and the cardinality of the set of landmarks, and is parallelizable, making it suitable for large-scale problems. The performance of the proposed method is evaluated on synthetic data and is shown to outperform an state-of-the-art method.

Author

Maryam Fatemi

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Karl Granström

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Lennart Svensson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Francisco J. R. Ruiz

Lars Hammarstrand

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

COPPLAR CampusShuttle cooperative perception & planning platform

VINNOVA, 2016-01-01 -- 2018-12-31.

Areas of Advance

Transport

Subject Categories

Signal Processing

More information

Latest update

4/11/2019