Array Signal Processing: Estimator Design and Experiments

阵列信号处理：估计器设计和实验

• 批准号: 9202081
• 负责人: Mostafa Kaveh
• 金额: $ 25.24万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-07-01 至 1996-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9202081&HistoricalAwards=false
• 关键词: Array; Signal; Processing; Estimator; Design

Kaveh This research builds on experience that has been gained during the construction and calibration of a laboratory ultrasound-in-air array, in order to establish a systematic approach for experimentation and theoretical analysis. Optimizing functionals are being developed for a general class of estimators with similar computational burden as the well-known method of MUSIC. These estimators are expected to significantly reduce the threshold signal to noise ratio, for a general calibrated array, over a method such as MUSIC. Analysis techniques are being established within a probabilistic framework for comparing the resolution thresholds of estimators in this class. Detection methods are also being developed in conjunction with experimental measurements that make optimum use of the array calibration data. The new algorithms are being generalized to cases involving wideband signals and signals possessing special characteristics such as cyclostationarity. The ongoing experimental work, which includes the implementation of subspace estimation algorithms on DSP processors, is intended to establish the level of practicality of many proposed methods.

这项研究基于在实验室超声阵列的构建和校准期间获得的经验，以建立一种系统的实验和理论分析方法。正在为具有与著名音乐方法相似的计算负担的一般估计器开发优化功能。 对于一般的校准阵列，预计这些估计器将显着降低诸如音乐之类的方法。 分析技术是在概率框架内建立的，用于比较此类估计器的分辨率阈值。检测方法还与实验测量相结合开发，这些实验测量可以最佳地使用阵列校准数据。 新算法已被推广到涉及具有特殊特征（例如循环性）的宽带信号和信号的案例。正在进行的实验工作包括在DSP处理器上实施子空间估计算法，旨在确定许多提出的方法的实用性。