Performance analysis of ML-based DOA estimation algorithm in bistatic MIMO radar system

J. Paik, Joon-Ho Lee
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引用次数: 1

Abstract

This paper proposes a new method to attain explicit expressions of some quantities associated with performance analysis of the maximum likelihood (ML) direction-of-arrival (DOA) algorithm in the presence of an additive Gaussian noise on the antenna elements. The motivation for the paper is to make quantitative analysis of the ML DOA algorithm in the case of multiple incident signals. We present a simple method to derive closed-form expression of estimation error of DOA estimate based on the Taylor series expansion. We address the performance analysis of the ML angle-of-arrival (AOA) estimation algorithm in this paper. We discuss the estimation of azimuth of multiple incident signals using bistatic multiple input multiple output (MIMO) radar model. Based on the Taylor series expansion and approximation, we get explicit expressions of estimation error of all incident signals. The validity of the derived expressions is shown by simulation results.
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双基地MIMO雷达系统中基于ml的DOA估计算法性能分析
本文提出了一种新的方法,在天线单元加性高斯噪声存在的情况下,求出最大似然到达方向(ML)算法性能分析中一些物理量的显式表达式。本文的动机是对多事件信号情况下的ML DOA算法进行定量分析。提出了一种简单的基于泰勒级数展开的DOA估计误差的封闭表达式。本文对机器学习的到达角估计算法进行了性能分析。本文讨论了用双基地多输入多输出(MIMO)雷达模型估计多个入射信号的方位。基于泰勒级数展开和近似,得到了所有事件信号估计误差的显式表达式。仿真结果表明了推导式的有效性。
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