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access icon free Robust direction of arrival estimation approach for unmanned aerial vehicles at low signal-to-noise ratios

  • Author(s): Jian Lu 1 ; Jian Yang 1, 2 ; Xinghai Liu 1 ; Guangbin Liu 1 ; Yue Zhang 3
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  • Source: Volume 13, Issue 4, June 2019, p. 456 – 463
    DOI:  10.1049/iet-spr.2018.5275 , Print ISSN 1751-9675, Online ISSN 1751-9683
© The Institution of Engineering and Technology
Received 03/07/2018, Accepted 31/01/2019, Revised 14/11/2018, Published 04/02/2019

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.

Inspec keywords: Fourier transforms; autonomous aerial vehicles; direction-of-arrival estimation; mobile robots; frequency modulation; signal classification; echo; correlation methods; mean square error methods; Monte Carlo methods; matrix algebra

Other keywords: Cramer–Rao bound; fractional autocorrelation matrix; signal-to-noise ratios; accumulative methods; phase compensation; linear frequency modulated signals; low-SNR echoes; FrFT; fractional Fourier transform; target echo waveform; accumulative effect; performance deterioration; multiple signal classification; UAV; DOA estimation; root-mean-square-error; Monte-Carlo simulation trials; unmanned aerial vehicle; robust direction-of-arrival estimation; multitarget echoes

Subjects: Monte Carlo methods; Signal processing and detection; Digital signal processing; Integral transforms in numerical analysis; Mobile robots; Aerospace engineering computing; Interpolation and function approximation (numerical analysis); Linear algebra (numerical analysis); Monte Carlo methods; Interpolation and function approximation (numerical analysis); Telerobotics; Linear algebra (numerical analysis); Integral transforms in numerical analysis

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