Radar Detection of Plasma-Covered Reentry Object Based on Crossed Two-Component LFM Signal

Authors

  • Xuyang Chen School of Aerospace Science and Technology Xidian University, Xi'an, Shaanxi 710071, China
  • Fangfang Shen School of Aerospace Science and Technology Xidian University, Xi'an, Shaanxi 710071, China
  • Yanming Liu School of Aerospace Science and Technology Xidian University, Xi'an, Shaanxi 710071, China
  • Xiaoping Li School of Aerospace Science and Technology Xidian University, Xi'an, Shaanxi 710071, China
  • Wei Ai 2 Science and Technology on Space Physics Laboratory China Academy of Launch Vehicle Technology, Beijing 100076, China

Keywords:

Crossed two-component LFM signal, plasma sheath, radar detection, reentry object

Abstract

A precise and efficient radar detection method based on crossed two-component LFM signal is proposed to deal with the detection problem of plasmacovered object. The method contains two segments: 1) design of transmitted signal, and 2) detection of object information (position and velocity) from the ambiguity function of the echo signal. For the first segment, the transmitted signal is designed to be a 2-component LFM signal with each component crossing with the other one in the time-frequency domain. The crossing design of the two components eliminates the disturbance term in solving the ambiguity function, guaranteeing the stability of detection. In the second segment, a mixed detection technique is proposed, which contains priorinformation- based component classification and optimal parameters solution, maintaining both the accuracy and efficiency in detection. By the proposed method, both the efficiency in computation and accuracy in detection are achieved. The simulation results illustrate the validity of the method.

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Published

2021-07-25

How to Cite

[1]
Xuyang Chen, Fangfang Shen, Yanming Liu, Xiaoping Li, and Wei Ai, “Radar Detection of Plasma-Covered Reentry Object Based on Crossed Two-Component LFM Signal”, ACES Journal, vol. 33, no. 06, pp. 665–674, Jul. 2021.

Issue

2018: Vol 33 Iss 6

Section

Articles