Decentralized Gaussian Filters for Cooperative Self-localization and Multi-target Tracking
| Autor: | Pranay Sharma, Augustin-Alexandru Saucan, Pramod K. Varshney, Donald J. Bucci |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Signal Processing (eess.SP)
Computer science Gaussian Association (object-oriented programming) 020206 networking & telecommunications Correlation and dependence 02 engineering and technology Tracking (particle physics) Belief propagation symbols.namesake Signal Processing 0202 electrical engineering electronic engineering information engineering symbols FOS: Electrical engineering electronic engineering information engineering Clutter Measurement uncertainty Electrical and Electronic Engineering Electrical Engineering and Systems Science - Signal Processing Algorithm Gibbs sampling |
| Popis: | Scalable and decentralized algorithms for Cooperative Self-localization (CS) of agents, and Multi-Target Tracking (MTT) are important in many applications. In this work, we address the problem of Simultaneous Cooperative Self-localization and Multi-Target Tracking (SCS-MTT) under target data association uncertainty, i.e., the associations between measurements and target tracks are unknown. Existing CS and tracking algorithms either make the assumption of no data association uncertainty or employ a hard-decision rule for measurement-to-target associations. We propose a novel decentralized SCS-MTT method for an unknown and time-varying number of targets under association uncertainty. Marginal posterior densities for agents and targets are obtained by an efficient belief propagation (BP) based scheme while data association is handled by marginalizing over all target-to-measurement association probabilities. Decentralized single Gaussian and Gaussian mixture implementations are provided based on average consensus schemes, which require communication only with one-hop neighbors. An additional novelty is a decentralized Gibbs mechanism for efficient evaluation of the product of Gaussian mixtures. Numerical experiments show the improved CS and MTT performance compared to the conventional approach of separate localization and target tracking. 16 pages, 7 figures |
| Databáze: | OpenAIRE |
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