Variational Bayesian Expectation Maximization for Radar Map Estimation
Journal article, 2016

For self-localization, a detailed and reliable map of the environment can be used to relate sensor data to static features with known locations. This paper presents a method for construction of detailed radar maps that describe the expected intensity of detections. Specifically, the measurements are modelled by an inhomogeneous Poisson process with a spatial intensity function given by the sum of a constant clutter level and an unnormalized Gaussian mixture. A substantial difficulty with radar mapping is the presence of data association uncertainties, i.e., the unknown associations between measurements and landmarks. In this paper, the association variables are introduced as hidden variables in a variational Bayesian expectation maximization (VBEM) framework, resulting in a computationally efficient mapping algorithm that enables a joint estimation of the number of landmarks and their parameters.

radar

iterative methods

Estimation theory

Author

Malin Lundgren

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

Lennart Svensson

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

Lars Hammarstrand

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

IEEE Transactions on Signal Processing

1053-587X (ISSN)

Vol. 64 6 1391-1404 7313017

COPPLAR CampusShuttle cooperative perception & planning platform

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

Subject Categories

Signal Processing

DOI

10.1109/TSP.2015.2496287

More information

Latest update

4/11/2019