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The real‐time tracking process of dim targets in space is mainly achieved through the correlation and prediction of dots after the detection and calculation process. The on‐board calculation of the tracking needs to be completed in milliseconds, and it needs to reach the microsecond level at high frame rates. For real‐time tracking of dim targets in space, it is necessary to achieve universal tracking calculation acceleration in response to different space regions and complex backgrounds, which poses high requirements for engineering implementation architecture. This paper designs a Kalman filter calculation based on digital logic parallel acceleration architecture for real‐time solution of dim target tracking on‐board. A unified architecture of Vector Processing Element (VPE) was established for the calculation of Kalman filtering matrix, and an array computing structure based on VPE was designed to decompose the entire filtering process and form a parallel pipelined data stream. The prediction errors under different fixed point bit widths were analyzed and deduced, and the guidance methods for selecting the optimal bit width based on the statistical results were provided. The entire design was engineered based on Xilinx's XC7K325T, resulting in an energy efficiency improvement compared to previous designs. The single iteration calculation time does not exceed 0.7 microseconds, which can meet the current high frame rate target tracking requirements. The effectiveness of this design has been verified through simulation of random trajectory data, which is consistent with the theoretical calculation error. [ABSTRACT FROM AUTHOR]

Information ordering is a nontrivial task in multi-document summarization (MDS), which typically relies on the traditional vector space model (VSM) notorious for semantic deficiency. In this article, we propose a novel event-enriched VSM to alleviate the problem by building event semantics into sentence representations. The mediation of event information between sentence and term, especially in the news domain, has an intuitive appeal as well as technical advantage in common sentence-level operations such as sentence similarity computation. Inspired by the block-style writing by humans, we base the sentence ordering algorithm on sentence clustering. To accommodate the complexity introduced by event information, we adopt a soft-to-hard clustering strategy on the event and sentence levels, using expectation-maximization clustering and K-means, respectively. For the purpose of cluster-based sentence ordering, the event-enriched VSM enables us to design an ordering algorithm to enhance event coherence computed between sentence and sentence-context pairs. Drawing on the findings of earlier research, we also incorporate topic continuity measures and time information into the scheme. We evaluate the performance of the model and its variants automatically and manually, with experimental results showing clear advantage of the event-based model over baseline and non-event-based models in information ordering for multi-document news summarization. We are confident that the event-enriched VSM has even greater potential in summarization and beyond, which awaits further research. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]