Multisensor Poisson Multi-Bernoulli Filter for Joint Target–Sensor State Tracking
| Autor: | Markus Frohle, Karl Granstrom, Christopher Lindberg, Henk Wymeersch |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Control and Optimization
Computer science Frame (networking) Filter (signal processing) Simultaneous localization and mapping Poisson distribution Tracking (particle physics) Computer Science::Robotics symbols.namesake Artificial Intelligence Control theory Automotive Engineering symbols State (computer science) Divergence (statistics) Joint (audio engineering) |
| Zdroj: | IEEE Transactions on Intelligent Vehicles. 4:609-621 |
| ISSN: | 2379-8904 2379-8858 |
| DOI: | 10.1109/tiv.2019.2938093 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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