Track-to-track fusion is widely used by largescale distributed surveillance systems to integrate multisensor tracking data. A critical step prior to track-totrack fusion is track-to-track assignment. However, when ground targets are tracked by airborne sensors, the target tracks contain not only tracking sensor errors but also navigation errors of the sensor platforms. If the errors are not compensated for properly, biased tracks create false assignments and lead to erroneously fused tracks. In this paper, track biases produced by individual trackers are characterized in terms of translation and rotation (and to a lesser extent by scaling) with respect to the common reference frame in which the track-to-track fusion will take place. Opportunistic information about the roads on which ground targets are moving is explored to estimate the track biases, akin to a system calibration, which can be used not only to remove biases from past and present tracks but also to provide corrections for future estimates. The proposed bias estimate method is based on binary image matching to estimate rotation and translation. A two-dimensional fast Fourier transform (2D FFT) is used to implement 2D search and correlation efficiently. Simulation results are presented to illustrate the proposed opportunistic road information based bias estimation (ORIBE) method and its performance as a function of target track accuracy and spatial resolution in forming track and road map images.

Multi-sensor management for data fusion in target tracking concerns issues of sensor assignment and scheduling by managing or coordinating the use of multiple sensor resources. Since a centralized sensor management technique has a crucial limitation in that the failure of the central node would cause whole system failure, a decentralized sensor management (DSM) scheme is increasingly important in modern multi-sensor systems. DSM is afforded in modern systems through increased bandwidth, wireless communication, and enhanced power. However, protocols for system control are needed to management device access. As game theory offers learning models for distributed allocations of surveillance resources and provides mechanisms to handle the uncertainty of surveillance area, we propose an agentbased negotiable game theoretic approach for decentralized sensor management (ANGADS). With the decentralized sensor management scheme, sensor assignment occurs locally, and there is no central node and thus reduces the risk of whole-system failure. Simulation results for a multi-sensor target-tracking scenario demonstrate the applicability of the proposed approach.

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