Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking

TL;DR

This paper introduces a multisensor Poisson multi-Bernoulli filter that jointly tracks vehicle and target states in a global coordinate frame, improving accuracy in automotive scenarios with uncertain sensor locations.

Contribution

The paper presents a low complexity joint vehicle-target tracking filter that incorporates multiple sensors with uncertain locations, enhancing multitarget tracking in automotive environments.

Findings

Improved target and vehicle state estimation accuracy.

Effective fusion of measurements from multiple sensors.

Demonstrated benefits in synthetic and real driving scenarios.

Abstract

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-feature 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.