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Position Report Enhancement Using Bayesian Estimator
IEEE Aerospace and Electronic Systems Magazine ( IF 3.6 ) Pub Date : 2021-01-01 , DOI: 10.1109/maes.2020.3015604
Allan Tart , Tonu Trump

In this article, a Bayesian estimator for a target position report is proposed. It is based on a maximum a priori algorithm, where the user's device knowledge about its location is used to deduce the prior probability density function. The algorithm does not require knowledge about the signal and noise levels, meaning, noninformative priors are used. It is well known that with a higher number of antenna elements in an array, narrower beams can be formed. In beamforming, task narrow beams are useful for serving many users simultaneously, whereas in direction-of-arrival (DOA) estimation, we are not interested in narrow beams as such; instead, estimation accuracy is important. So, reducing the number of antenna elements used for DOA estimation is beneficial from a system complexity point of view. In this article, signal source location report is used to enhance the estimated DOA, for the task, MAP estimator is developed. We will show that in the case of small array size and large array covariance matrix error values, the proposed estimator is the only one capable of improving the prior knowledge about the transmitter when comparing it with other popular algorithms, such as the maximum likelihood estimator and Root MUSIC. The algorithm could be used in a variety of different scenarios, but its advantages emerge in the case of complex signal propagation environments, such as urban canyons and large airports.

中文翻译:

使用贝叶斯估计器增强位置报告

在本文中,提出了用于目标位置报告的贝叶斯估计器。它基于最大先验算法,其中用户关于其位置的设备知识用于推断先验概率密度函数。该算法不需要有关信号和噪声水平的知识,这意味着使用了无信息先验。众所周知,阵列中的天线单元数量越多,可以形成越窄的波束。在波束成形中,任务窄波束可用于同时为多个用户提供服务,而在到达方向 (DOA) 估计中,我们对窄波束本身不感兴趣;相反,估计精度很重要。因此,从系统复杂性的角度来看,减少用于 DOA 估计的天线单元的数量是有益的。在本文中,信号源位置报告用于增强估计的 DOA,针对该任务,开发了 MAP 估计器。我们将证明,在小阵列尺寸和大阵列协方差矩阵误差值的情况下,所提出的估计器是唯一一种能够在与其他流行算法(例如最大似然估计器和根音乐。该算法可用于多种不同场景,但在城市峡谷、大型机场等复杂信号传播环境的情况下,其优势就显现出来了。与其他流行算法(例如最大似然估计器和 Root MUSIC)进行比较时,所提出的估计器是唯一能够改进有关发射器的先验知识的估计器。该算法可用于多种不同场景,但在城市峡谷、大型机场等复杂信号传播环境的情况下,其优势就显现出来了。与其他流行算法(例如最大似然估计器和 Root MUSIC)进行比较时,所提出的估计器是唯一能够改进有关发射器的先验知识的估计器。该算法可用于多种不同场景,但在城市峡谷、大型机场等复杂信号传播环境的情况下,其优势就显现出来了。
更新日期:2021-01-01
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