微波功率器件大信号S参数的提取

介绍了微波有源器件大信号模型的计算机模拟技术和利用商品化软件ＨＰＭＤＳ提取大信号Ｓ参数的方法。     

大信号参数测试仪设计技术

通过计算机产生一定脉冲重复频率的窄脉冲来测试ＧａＡｓ，ＦＥＴ或ＰＨＥＭＴ的Ｉ－Ｖ特性，能大大克服了器件在直流状态下的热效应和电子陷阱效应，对器件大信号建模和器件机理分析具有重要意义，着重论述了测试仪的硬件，软件设计和实际使用效果。     

GaAsFET大信号等效电路参数提取

将新型算法—退火遗传算法用于GaAsFET大信号等效电路模型参数的提取,给出了具体流程并提取了器件的模型,结果表明该算法快速可靠,文中为提取大信号模型提供了新的方法,并扩展了遗传算法的应用。     

GaAsFET大信号模型与参数提取

提出适用于功率ＧａＡｓＦＥＴ的新的大信号模型以及脉冲Ｉ－Ｖ和小信号Ｓ参数相结合的整体化参数提取方法。用研制出的大信号建模软件提取了功率ＦＥＴ的大信号模型参数，并用该模型模拟和测量了器件大信号Ｓ参数，结果完全一致。     

微波功率器件大信号S参数的提取

介绍了微波有源器件大信号模型的计算机模拟技术和利用商品化软件ＨＰ ＭＤＳ提取大信号Ｓ参数的方法。     

大信号有源器件S参数测试方法研究

利用ＨＰ８５１０自动网络分析仪小信号测试系统，研究实现了大信号Ｓ参数的测试方法，并建立了大信号Ｓ参数测试系统。研究设计的适应多种尺寸的有源测试夹具和整个系统的误差修正方法，使建立的该系统具有与ＨＰ８５１０系列分析仪同等的指标     

大信号有源器件S参数测试方法研究

利用ＨＰ８５１０自动网络分析仪小信号测试系统，研究实现了在信号Ｓ参数的测试方法，并建立了大信号Ｓ参数测试系统。研究设计的适应多种尺寸的有源测试夹具和整个系统的误差修正方法，使建立的该系统具有与ＨＰ８５１０系列分析仪同等的指标。     

GaAs FET大信号模型与参数提取

提出适用于功率ＧａＡｓＦＥＴ的新的大信号模型以及脉冲Ｉ－Ｖ和小信号Ｓ参数相结合的整体化参数提取方法。用研制出的大信号建模软件提取了功率ＦＥＴ的大信号模型参数，并用该模型模拟和测量了器件大信号Ｓ参数，结果完全一致。     

InP基HBT GP大信号模型直流参数提取的研究

基于HBT特殊的物理机理及结构,将适用于BJT的GP大信号模型用于InP基HBT的研究中.通过构建误差函数,采取解析法提取了该模型中的13项SPICE直流参数.并设计了参数提取实验装置,最后将研究结果用于发射极为2μm×19μm的InP/InGaAs HBT建模中.通过对比模型仿真和器件实测的数据可以看出,本文采用的HBT GP模型准确度高,可以较好地表征实际HBT器件的直流特性.     

半导体激光器大信号等效电路模型的参数提取

本文提出了一种由半导体激光器（ＬＤ）的外特性，如端口电特性和小信号频率响应，确定其大信号等效电路模型参数的新方法。并对一个ＩｎＧａＡｓＰ脊形波导ＬＤ和一个隐埋异质结ＬＤ进行了参数提取。这种方法，不需要了解ＬＤ的内部特性，分析的结果与已报道的理论、实验值一致。     

The parameter analysis of the large signal mathematical model of Gunn devices operating in the harmonic mode

This paper analyses in detail the parameters of the large signal mathematical model of a Gunn device operating in the harmonic mode using the frequency-domain harmonic balance method and indicates the variation trend of the output power and the operating frequency of a harmonic oscillator under the influence of the model parameters of the device. The analysis results offer the theoretical basis for the production of Gunn devices and for the design of harmonic oscillators     

正弦波信号参数分析仪

为了满足低端场合的信号参数分析仪性价比高的要求,设计出低成本正弦波信号参数分析仪。系统由频率测量、幅度测量和信号发生器三大部分组成。频率测量部分由单片机89S52采用测频和测周的方法对信号进行频率测量,测量误差远优于1%。幅度测量部分采用峰值保持电路,测量误差能保证在2%左右。信号发生器采用文氏电桥实现了幅度从0～10 V连续可调、频率从66 Hz~75 kHz连续可调的正弦波。     

三角波信号参数测试仪

提出由三角波信号发生器和三角波信号参数测试仪组成的系统设计.信号发生器以FPGA为控制核心,基于直接数字频率合成原理,产生频率、幅度、占空比连续可调的三角波信号;而三角波信号参数测试仪以等精度法实现了精度为10~(-6)的三角波频率测量:以数字峰值检波的方法实现了幅度测量,其误差小于1%;以多点求均值的方法也降低了求取斜率的误差.该系统设计为实验室产生多参数同时可调的三角波及三角波的精确检潮提供可能.     

Some results on the stochastic signal parameter estimation problem

The problem of finding maximum-likelihood estimates of the partially or completely unknown autocorrelation function of a zero-mean Gaussian stochastic signal corrupted by additive, white Gaussian noise is analyzed. It is shown that these estimates can be found by maximizing the output of a Wiener estimator-correlator receiver biased by a smoothed version of the output noise-to-signal ratio of the Wiener estimator over the class of admissible autocorrelation functions. For the case where the autocorrelation function is known except for an amplitude scale parameter, an illuminating expression for the Cramer-Rao minimum estimation variance is derived. Detailed study of this expression yields, among other results, an interpretation of the maximum-likelihood estimator as an adaptive processor.     

Some lower bounds on signal parameter estimation

New bounds are presented for the maximum accuracy with which parameters of signals imbedded in white noise can be estimated. The bounds are derived by comparing the estimation problem with related optimal detection problems. They are, with few exceptions, independent of the bias and include explicitly the dependence on the a priori interval. The new results are compared with previously known results.     

Remarks, mostly historical, on signal detection and signal parameter estimation

Historical remarks outlining some of the evolution of the statistical theory of detecting signals in noise and of estimating signal parameters make up the greater part of the paper. The time period covered is roughly from the years of World War II to the middle 1960s. The remarks are rather severely limited in scope; they do not touch on nonparametric statistical methods, or filtering, or prediction theory, for example. The last part of the paper is a discussion of one aspect of a signal-processing problem area that is of current interest in optics and radar, and can be regarded as one of many descendants of the early parameter estimation theory. The area is object-field reconstruction or restoration, and the aspect of interest is the intrinsic relation between the inherent instability of solutions of many such problems and the precision or resolution of detail that can be attained in the construction.     

Joint signal detection and parameter estimation in multiusercommunications

The problem of simultaneously detecting the information bits and estimating signal amplitudes and phases in a K-user asynchronous direct-sequence spread-spectrum multiple-access communication system is addressed. The joint maximum-likelihood (ML) estimator has a computational complexity that is exponential in the total number of bits transmitted and thus does not represent a practical solution to the problem. An estimator that combines a suboptimum tree-search algorithm with a recursive least-squares estimator of complex signal amplitude is considered. The complexity of this estimator is O(K²) computations per decoded bit, and its performance is very close to that of the joint ML receiver. This receiver has the advantage that the transmitted signal powers and phases are extracted from the received signal in an adaptive fashion without using a test sequence     

A subspace rotation approach to signal parameter estimation

A new approach to Estimation of Signal Parameters by Rotational Invariance Techniques (ESPRIT) is described in the context of direction-of-arrival estimation, but is also applicable to other problems. The method relies on finding the underlying rotation between the common subspaces associated with an array of pairwise-matched and codirectional sensor doublets. ESPRIT has several remarkable advantages over earlier techniques such as MUSIC, and also provides asymptotically unbiased and efficient estimates.