Joint Data Association, Spatiotemporal Bias Compensation and Fusion for Multisensor Multitarget Tracking

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Abstract
Spatiotemporal bias compensation and data association are two essential prerequisites for correct data fusion. Neglect of either in multisensor systems prevents the algorithms from successful application. In this paper, a joint data association, bias compensation and fusion method is presented for multitarget tracking in asynchronous multisensor systems with spatiotemporal biases. The time stamp interval is used to model the state transition, while the difference between the measurement and time stamp interval is regarded as temporal bias and augmented in the state vector as well as spatial bias. The measurements are then formulated as a function of both target states and spatiotemporal biases, where the unknown temporal bias is used to align measurements and target states. Based on the formulation, the joint data association and bias compensation problem is converted into the classical data association and filtering problem in a unified Bayesian framework, without requirement of iterative optimization procedure between data association and bias estimation. The data association is formulated as a 2-D assignment problem and is solved using the generalized Auction algorithm. For each track-measurement pair in association results, the unscented Kalman filter is used to handle the measurement to produce estimates of target states and spatiotemporal bias simultaneously. Furthermore, a bias fusion approach with feedback is presented to fuse the spatiotemporal bias estimates from multiple targets to improve the bias compensation performance and solve the correlation problems therein. Simulation results demonstrate the effectiveness of the proposed method.
Funding Information
  • National Natural Science Foundation of China (61671181)
  • Heilongjiang Outstanding Youth Science Fund (JQ2022F002)

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