JOURNAL ARTICLE

Parallel accelerated computing architecture for dim target tracking on‐board.

  • Published In: Computational Intelligence, 2024, v. 40, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Yu, Jiyang; Huang, Dan; Li, Wenjie; Wang, Xianjie; Shi, Xiaolong 3 of 3

Abstract

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]

Additional Information

  • Source:Computational Intelligence. 2024/02, Vol. 40, Issue 1, p1
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2024
  • ISSN:0824-7935
  • DOI:10.1111/coin.12604
  • Accession Number:175643266
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