Array Signal Processing: Estimator Design and Experiments

Kaveh 本研究建立在实验室空气中超声阵列的构建和校准过程中获得的经验的基础上，以建立一个系统的实验和理论分析方法。优化功能正在开发的一个一般类的估计与类似的计算负担，众所周知的方法MUSIC。 对于一般校准阵列，这些估计器预计将显著降低阈值信噪比，超过诸如MUSIC的方法。 分析技术正在建立在一个概率框架内比较在这一类的估计的分辨率阈值。探测方法也正在结合实验测量加以开发，以便最佳利用阵列校准数据。 新的算法被推广到涉及宽带信号和具有特殊特性的信号，如循环平稳的情况下。正在进行的实验工作，其中包括子空间估计算法在DSP处理器上的实现，旨在建立许多建议的方法的实用性水平。

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.