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- W2891666435 abstract "This paper describes a new approach for joint tracking and registration in multi-target multi-sensor surveillance in presence of sensor biases. Due to a mutual dependency on target state and sensor bias, measurements cause cross-couplings between target and bias estimates. Therefore, an all-in-one filter (e.g. extended Kalman filter) only is practical for a limited amount of sensors/targets without impairing real-time capability. Since Bayesian filtering historically is the method of choice in target tracking applications, bias estimation is attempted to be decoupled from the tracking filters for the sake of computational efficiency. Current approaches either solve the registration problem first and ignore remaining bias errors during the tracking phase, or make simplifying assumptions about cross-correlations in order to get the tracking filters decoupled from bias estimation. In contrast, we propose a joint estimator for both target states and sensor biases that preserves real-time capability and exhibits a decoupling with all cross-correlations considered. The basic idea is driven by graphical inference methods on factor graphs and entails a departure from the classical filtering framework. We introduce a multiple-model state transition factor to cope with non-cooperative targets. The benefits are threefold. First, conditional independence between target states given the mutual biases is exploited to attain high parallelism scalable in the number of targets. Second, operational delays are brought to a minimum as registration and tracking is performed simultaneously. Third, out-of-sequence measurements are easily integrable. The proposed algorithm is evaluated using a simulated scenario." @default.
- W2891666435 created "2018-09-27" @default.
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- W2891666435 date "2018-07-01" @default.
- W2891666435 modified "2023-09-26" @default.
- W2891666435 title "Joint Tracking and Registration in Multi-Target Multi-Sensor Surveillance Using Factor Graphs" @default.
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- W2891666435 doi "https://doi.org/10.23919/icif.2018.8455675" @default.
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