Equalization Reconstruction Algorithm Based on Reference Signal Frequency Domain Block Joint for DTMB-Based Passive Radar

Journal of Beijing Institute of Technology ›› 2024, Vol. 33 ›› Issue (1) : 41 -53.

PDF (6968KB)
Journal of Beijing Institute of Technology ›› 2024, Vol. 33 ›› Issue (1) : 41 -53. DOI: 10.15918/j.jbit1004-0579.2023.091

Equalization Reconstruction Algorithm Based on Reference Signal Frequency Domain Block Joint for DTMB-Based Passive Radar

Author information +
History +
PDF (6968KB)

Abstract

Channel equalization plays a pivotal role within the reconstruction phase of passive radar reference signals. In the context of reconstructing digital terrestrial multimedia broadcasting (DTMB) signals for low-slow-small (LSS) target detection, a novel frequency domain block joint equalization algorithm is presented in this article. From the DTMB signal frame structure and channel multipath transmission characteristics, this article adopts a unconventional approach where the delay and frame structure of each DTMB signal frame are reconfigured to create a circular convolution block, facilitating concurrent fast Fourier transform (FFT) calculations. Following equalization, an inverse fast Fourier transform (IFFT)-based joint output and subsequent data reordering are executed to finalize the equalization process for the DTMB signal. Simulation and measured data confirm that this algorithm outperforms conventional techniques by reducing signal errors rate and enhancing real-time processing. In passive radar LSS detection, it effectively suppresses multipath and noise through frequency domain equalization, reducing false alarms and improving the capabilities of weak target detection.

Keywords

passive radar / frequency domain equalization / reference signal reconstruction / digital terrestrial multimedia broadcasting (DTMB)

Cite this article

Download citation ▾
null. Equalization Reconstruction Algorithm Based on Reference Signal Frequency Domain Block Joint for DTMB-Based Passive Radar. Journal of Beijing Institute of Technology, 2024, 33(1): 41-53 DOI:10.15918/j.jbit1004-0579.2023.091

登录浏览全文

4963

注册一个新账户 忘记密码

References

AI Summary AI Mindmap
PDF (6968KB)

478

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/