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Distributed Invariant Extended Kalman Filter Using Lie Groups: Algorithm and Experiments
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AI
DOI：10.1109/TCST.2023.3290299.png)
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Abstract
En 中文
Distributed Kalman filters have been widely studied in vector space and have been applied to 2-D target state estimation using sensor networks. In this article, we introduce a novel distributed invariant extended Kalman filer (DIEKF) that exploits matrix Lie groups and is suitable to track the target's 6-DOF motion in a 3-D environment. The DIEKF is based on the proposed extended covariance intersection (CI) algorithm that guarantees consistency in matrix Lie groups. The DIEKF is fully distributed as each agent only uses the information from itself and the one-hop communication neighbors, and it is robust to a time-varying communication topology and changing blind agents. In addition to assuming a known target model, we study the case where the target's true motion is unknown. To evaluate the performance, first, we apply the algorithm in a camera network to track a target pose. Extensive Monte-Carlo simulations have been performed to analyze the performance. More importantly, the performance is further verified with real data collected by using a quadrotor with multiple ultra-wideband (UWB) anchor receivers. Overall, the proposed algorithm is more accurate and more consistent in comparison with our recent work on the quaternion-based distributed extended Kalman filter (QDEKF).
Keywords:
Distributed estimation
information fusion
invariant extended Kalman filtering
wireless sensor network
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