%0 Electronic Article
%A Jian Lu
%A Jian Yang
%A Xinghai Liu
%A Guangbin Liu
%A Yue Zhang
%K low-SNR echoes
%K root-mean-square-error
%K FrFT
%K unmanned aerial vehicle
%K performance deterioration
%K target echo waveform
%K linear frequency modulated signals
%K accumulative methods
%K fractional autocorrelation matrix
%K multiple signal classification
%K multitarget echoes
%K fractional Fourier transform
%K signal-to-noise ratios
%K accumulative effect
%K DOA estimation
%K robust direction-of-arrival estimation
%K UAV
%K phase compensation
%K Cramer–Rao bound
%K Monte-Carlo simulation trials
%X The conventional methods used to estimate the direction of arrival (DOA) of linear frequency modulated (LFM) signals at low signal-to-noise ratios (SNRs), such as the echoes reflected by a small unmanned aerial vehicle (UAV), demonstrate major performance deterioration. In order to eliminate the problem and achieve highly accurate DOA estimation for low-SNR echoes, this paper proposes a novel estimation approach that applies two accumulative methods based on fractional Fourier transform (FrFT). According to the findings of this paper, the algorithm directly accumulates the FrFT result of each echo when it is not possible to determine in advance the speed of the target. When the radial velocity of the small UAV has been estimated ahead of time, the algorithm performs phase compensation to enhance the accumulative effect. Through coherent integration, the algorithm then extracts all the peaks of the target echo waveform, which are then used for the construction of a fractional autocorrelation matrix. Thereafter, multiple signal classification is employed for DOA estimation. Furthermore, with the proposed algorithm, the DOAs of multi-target echoes can be estimated accurately. The effectiveness of the proposed algorithm was verified using Monte–Carlo simulation trials, and the root-mean-square-error of DOA estimation was close to the Cramer–Rao bound.
%@ 1751-9675
%T Robust direction of arrival estimation approach for unmanned aerial vehicles at low signal-to-noise ratios
%B IET Signal Processing
%D February 2019
%I Institution of Engineering and Technology
%U https://digital-library.theiet.org/;jsessionid=d0r3ipmdn01q.x-iet-live-01content/journals/10.1049/iet-spr.2018.5275
%G EN