ESPRIT和Root-MUSIC在到达角估计中的性能

2018 IEEE World Symposium on Communication Engineering (WSCE) Pub Date : 2018-12-01 DOI:10.1109/WSCE.2018.8690541

Chan-Bin Ko, Joon-Ho Lee

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引用次数: 11

摘要

AOA估计用于汽车雷达系统等事件信号的定位。[1]估计信号源的AOA有多种方法。MUSIC(MUltiple SIgnal Classification)[2]、CBF(conventional波束形成)[3]和capon波束形成[4]等到达角(AOA)估计算法需要大量的计算量，因为它们需要搜索整个方位角范围。然而，ESPRIT[5]和Root-MUSIC[6]具有计算复杂度低的优点，因为它们在不进行方位角搜索的情况下对AOA进行了分析估计。在本文中，我们使用蒙特卡罗模拟来定量地得到AOA估计中的误差。我们的研究证实了Root-MUSIC在低信噪比条件下的性能优于ESPRIT。当传感器数量为4时，需要使用Root-MUSIC算法，而不是ESPRIT

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Performance of ESPRIT and Root-MUSIC for Angle-of-Arrival(AOA) Estimation

AOA estimation is used for localization of incident signals such as automotive radar system. [1] There are various methods for estimating the AOA of the signal source. The angle-of-arrival(AOA) estimation algorithms such as MUSIC(MUltiple SIgnal Classification) [2], CBF(conventional beamforming) [3], and capon beamforming [4] require large amounts of computation because they need to search the full range of azimuths. However, ESPRIT [5] and Root-MUSIC [6] have an advantage of low computational complexity because they estimate the AOA analytically without azimuth search. In this paper, we use Monte-Carlo simulation to quantitively get an error in AOA estimation. Our study confirms that Root-MUSIC's performance is better than that of ESPRIT at low SNR(Signal-to-Noise Ratio) condition. When the number of sensors is 4, it is required to use a Root-MUSIC algorithm rather than ESPRIT

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2018 IEEE World Symposium on Communication Engineering (WSCE)

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