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In multisensor multitarget tracking systems, spatiotemporal bias compensation is essential for correct data fusion. However, waiting for the measurements of all targets from a sensor to estimate the biases in a batch processing scheme is computational prohibitive and infeasible in some real applications. In this paper, an online sequential spatiotemporal bias compensation method using multisensor multitarget measurements is presented. The spatiotemporal biases of the sensors are augmented into the state vector of each target to be estimated by the measurements of this target. When updating the estimates of a target, the spatiotemporal bias estimates of the previous target are used as linear pseudo-measurements. The correlation between the pseudo-measurement and the augmented state estimates of this target from the previous time step is analyzed. To handle the correlation problem, a novel estimator is derived under the minimum mean square error (MMSE) framework to obtain correct sequential estimation in this situation. The proposed method produces both target state and spatiotemporal bias estimates once the measurements of one target from a sensor are received, without requirement of waiting for all target measurements and calculation with high dimensional matrix. The posterior Cramer-Rao Lower Bound (PCRLB) for spatiotemporal bias and state estimation is presented. Simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks. [ABSTRACT FROM AUTHOR]