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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Maximum likelihood DOA estimation in unknown colored noise fields
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Maximum likelihood DOA estimation in unknown colored noise fields

机译：未知有色噪声场中的最大似然DOA估计

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摘要

Direction-of-arrival (DOA) estimation in unknown noise environments is an important but challenging problem. Several methods based on maximum likelihood (ML) criteria and parameterization of signals or noise covariances have been established. Generally, to obtain the exact ML (EML) solutions, the DOAs must be jointly estimated along with other noise or signal parameters by optimizing a complicated nonlinear function over a high-dimensional problem space. Although the computation complexity can be reduced via derivation of suboptimal approximate ML (AML) functions using large sample assumption or least square criteria, nevertheless the AML estimators still require multi-dimensional search and the accuracy is lost to some extent. A particle swarm optimization (PSO) based solution is proposed here to compute the EML functions and explore the potential superior performances. A key characteristic of PSO is that the algorithm itself is highly robust yet remarkably simple to implement, while processing similar capabilities as other evolutionary algorithms such as the genetic algorithm (GA). Simulation results confirm the advantage of paring PSO with EML, and the PSO-EML estimator is shown to significantly outperform AML-based techniques in various scenarios at less computational costs.
机译：未知噪声环境中的到达方向（DOA）估计是一个重要但具有挑战性的问题。已经建立了几种基于最大似然（ML）标准和信号或噪声协方差参数化的方法。通常，要获得精确的ML（EML）解决方案，必须通过在高维问题空间上优化复杂的非线性函数来共同估计DOA以及其他噪声或信号参数。尽管可以通过使用大样本假设或最小二乘准则推导次优近似ML（AML）函数来降低计算复杂度，但是AML估计量仍然需要多维搜索，并且准确性在某种程度上会有所下降。本文提出了一种基于粒子群优化（PSO）的解决方案，以计算EML函数并探索潜在的优越性能。 PSO的一个关键特性是，该算法本身具有很高的鲁棒性，但实现起来却非常简单，同时还能处理与其他进化算法（例如遗传算法）相似的功能。仿真结果证实了将PSO与EML进行比较的优势，并且PSO-EML估计器在各种情况下均以较低的计算成本大大优于基于AML的技术。

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