Skip to main navigation Skip to search Skip to main content

Phase-Correlation-Informed LSTM for Space-Target Pose Estimation from HRRP Sequences

  • Liang Zhang
  • , Ji Yuan Wang
  • , Yun Chuan Wang
  • , Cheng Tao Zhao
  • , Xin Yue Lou
  • , Xiaomin Pan*
  • *Corresponding author for this work
  • Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Direct pose estimation from high-resolution range profile (HRRP) sequences remains challenging because HRRPs vary with the radar line of sight (RLOS). This paper proposes a phase-correlation-informed LSTM that fuses the phase correlation between adjacent HRRPs with RLOS through cross-attention. The fused feature is used to condition the LSTM candidate-state update. On non-overlapping non-uniform test views, the proposed method achieves a 93.27% qualified rate under the 1° criterion and retains approximately 88% at an SNR of 5 dB, outperforming representative baselines in accuracy and noise robustness.

Original languageEnglish
Title of host publicationCSRSWTC 2026 - 2026 Cross Strait Radio Science and Wireless Technology Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798319507570
DOIs
Publication statusPublished - 2026
Externally publishedYes
Event2026 Cross Strait Radio Science and Wireless Technology Conference, CSRSWTC 2026 - Harbin, China
Duration: 10 Jul 2026 → 12 Jul 2026

Publication series

NameCSRSWTC 2026 - 2026 Cross Strait Radio Science and Wireless Technology Conference, Proceedings

Conference

Conference2026 Cross Strait Radio Science and Wireless Technology Conference, CSRSWTC 2026
Country/TerritoryChina
CityHarbin
Period10/07/26 → 12/07/26

Keywords

  • High-resolution range profile (HRRP) sequence
  • LSTM
  • phase correlation
  • radar line of sight (RLOS)
  • space targets

Fingerprint

Dive into the research topics of 'Phase-Correlation-Informed LSTM for Space-Target Pose Estimation from HRRP Sequences'. Together they form a unique fingerprint.

Cite this