Altitude measurement of low‐elevation target for VHF radar based on weighted sparse Bayesian learning. Issue 4 (1st June 2018)

• Record Type: Journal Article
• Authors: Li, Cunxu; Chen, Baixiao; Yang, Minglei; Zheng, Yisong
• Is Part Of: IET signal processing. Volume 12:Issue 4(2018)
• Page Start: 403
• Page End: 409
• Publication Date: 2018-06-01
• Subjects: direction‐of‐arrival estimation -- belief networks -- radar signal processing; altitude measurement -- low‐elevation target -- VHF radar -- weighted sparse Bayesian learning -- very high frequency radars -- direction‐of‐arrival estimation method -- perturbation multipath propagation model -- random perturbation coefficients -- real data analysis -- computer simulations; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2016.0738
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : In this study, a novel method is proposed to deal with the problem of altitude measurement of a low‐elevation target for very high frequency radars in complex terrains. The problem typically concerns about the direction‐of‐arrival (DOA) estimation method for closely spaced and correlated signals arose by multipath propagation, in which the multipath signal would be modulated by the rough and irregular reflecting surface, result in amplitude and phase perturbation and energy fluctuations of the receiving signals. A perturbation multipath propagation model is derived where the influence of irregular reflecting in complex terrain is taken as a random perturbation of the multipath signal. The spatial sparsity of the receiving signal is then exploited, and a weighted sparse Bayesian learning method is proposed to estimate both the random perturbation coefficients and DOA of the incident signal with high precision. Both the computer simulations and real data analysis indicate the efficiency and superior performance of the proposed method in dealing with altitude measurement in complex terrain.