Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking
Journal article, 2019

In a typical multitarget tracking (MTT) scenario, the sensor state is either assumed known, or tracking is performed in the sensor's (relative) coordinate frame. This assumption does not hold when the sensor, e.g., an automotive radar, is mounted on a vehicle, and the target state should be represented in a global (absolute) coordinate frame. Then it is important to consider the uncertain location of the vehicle on which the sensor is mounted for MTT. In this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT filter, which jointly tracks the uncertain vehicle state and target states. Measurements collected by different sensors mounted on multiple vehicles with varying location uncertainty are incorporated sequentially based on the arrival of new sensor measurements. In doing so, targets observed from a sensor mounted on a well-localized vehicle reduce the state uncertainty of other poorly localized vehicles, provided that a common non-empty subset of targets is observed. A low complexity filter is obtained by approximations of the joint sensor-target state density minimizing the Kullback-Leibler divergence (KLD). Results from synthetic as well as experimental measurement data, collected in a vehicle driving scenario, demonstrate the performance benefits of joint vehicle-target state tracking.


Markus Fröhle

Zenuity AB



Karl Granström

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

IEEE Transactions on Intelligent Vehicles

23798858 (eISSN)

Vol. 4 4 609-621 8818354

Subject Categories

Control Engineering

Signal Processing



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