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

机译：低信噪比的无人机稳健的到达方向估计方法

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摘要

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.
机译：用于估算低信噪比（SNR）的线性调频（LFM）信号的到达方向（DOA）的常规方法，例如由小型无人机（UAV）反射的回波性能下降。为了消除该问题并实现针对低SNR回波的高精度DOA估计，本文提出了一种新颖的估计方法，该方法应用了两种基于分数傅里叶变换（FrFT）的累加方法。根据本文的发现，当无法提前确定目标速度时，该算法直接累加每个回波的FrFT结果。当提前估计小型无人机的径向速度时，该算法将执行相位补偿以增强累积效果。通过相干积分，该算法然后提取目标回波波形的所有峰值，然后将其用于分数自相关矩阵的构建。此后，采用多信号分类进行DOA估计。此外，利用所提出的算法，可以准确估计多目标回波的DOA。该算法的有效性通过蒙特卡洛仿真试验得到了验证，DOA估计的均方根误差接近于Cramer-Rao界。

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