一种新型自适应天线的空间谱估计性能分析

利用菲涅尔区相位修正聚焦结构和馈源阵列构成一种新型的多波束自适应天线。本文深入分析了基于该天线的阵列输出特性，与基于均匀线形阵的空间谱估计技术比较的基础上，揭示了其良好的去相关性能。最后应用极大似然估计算法实现了相干信号源在大角度入射的情形下，菲涅尔平板天线对来波方向的有效估计，从而避免了应用MUSIC算法进行来波方向估计时所遇到的观察区域的限制问题。仿真计算的结果表明菲涅尔平板天线良好的空间谱估计性能。     

用菲涅尔区板天线实现来波方向的鲁棒性估计

针对蜂窝移动通信局域散射电波传播的空间分布源模型,研究了应用菲涅尔区相位修正平面聚焦结构和馈源阵列的组合形成的一种新型多波束自适应天线．基于广义MUSIC算法及极大似然估计算法．分别实现相干及非相干的空间分布式源信号来波方向及其角度扩展参数的鲁棒性估计问题．数值仿真计算结果表明．在这一无线传播环境中,基于菲涅尔区相位修正平面聚焦结构在减小相关参数估计的RMS误差等方面,其性能优于传统的均匀直线阵列模型．因此基于该新型自适应天线．可以实现来波方向的鲁棒性估计。     

用菲涅尔区聚焦多波束天线实现来波方向估计

菲涅尔区相位修正平板聚焦结构有着一些新的特点,从而可作籽矣焦元件构成多波束天线。该文在基于菲涅尔区相位修正平板结构的多波束天线模型下,利用多信号分类算法同对一维平面内来波方向的设计。通过计算机模拟及对模拟结果的讨论,比较了分别采用噪声子空间和信号子空间进行来波方向估计时的结果,并给出了一些有意义的结论。     

菲涅尔区聚焦多波束天线空间谱估计性能分析

首先分析了基于菲涅尔区聚焦多波束天线的信号处理特点,在与基于均匀线形阵的空间谱估计性能比较的基础上,讨论了菲涅尔区聚焦多波束天线波达方向估计的性能;最后利用多信号分类法进行波达方向估计的计算机模拟,并定量地分析了数值结果,给出了一些有意义的结论。     

基于菲涅尔区修正结构的多波束自适应天线

菲涅尔区相位修正结构具有类似于二次曲面的聚焦特性和更好的偏轴扫描特性,将该结构与馈源阵列相组合,并通过用改进的ＬＭＳ算法对馈源阵列输出的自适应信号处理,实现了对来波方向的估计和数字波束形成,从而构成了一类新型的多波束自适应天线。计算机模拟的有关结果证实了其可行性。     

光生等离子体菲涅尔波带板天线

本文描述一种光生等离子体菲涅尔波带板天线的设计方法.利用中心波长为808 nm的近红外激光器阵列通过光学掩膜(菲涅尔波带板的照相底片)照射高电阻率的硅片,使硅片呈现出菲涅尔波带板的衍射特性,实现对入射毫米波的聚焦.给出了这种天线在94GHz处的实验验证,并得到了4dB的天线增益效果.     

一种新型自适应天线的阵列输出特性分析

针对杜惠平(1999)提出的一种基于菲涅尔区修正结构聚焦的多波束自适应天线,引入一个统一的模型,通过与均匀线形阵列的比较和分析,说明了这种自适应天线的阵列输出特性兼有均匀线形阵列和多波束天线二者的特点。计算机模拟结果表明,利用这种天线可以有效地完成自适应处理。     

太赫兹开槽介质型菲涅尔天线设计

设计了一款工作在太赫兹频段的开槽介质型菲涅尔(Fresnel)透镜天线,该天线由馈电结构和透镜结构两部分组成。利用CST微波仿真软件,首先设计波纹喇叭馈电结构,其次基于波纹喇叭馈电结构设计开槽型菲涅尔透镜天线,分析全波周期、焦径比和子区等对天线性能的影响,通过横向和纵向对比,确定最佳天线参数。结果表明对天线性能影响从大到小分别为:焦径比、全波周期、子区。焦径比F/D=3、全波周期w=3、子区P=4为最佳天线参数,波纹喇叭馈电天线经过开槽介质型菲涅尔透镜天线聚焦后,天线的增益提升12.5 d B。     

新型多星卫星接收天线

本文综述了基于菲涅尔原理聚焦的新型多星卫星接收天线,包括菲涅尔区相位修正结构的聚焦特点,多星卫星接收天线的实现形式、各自的特点及研究状况等。实践证明:接收多颗卫星信号技术的应用,必将取得较好的经济效益和社会效益。     

微波通信天线选择与优化方法研究

简述微波天线在通信中应用的广泛性和重要性,在时第一菲涅尔区、衰落因子和相对余隙等重要因素详细分析的基础上,提出选择微波天线时应注意的问题,并提出采用分集接收、自适应均衡、阻抗匹配和避雷保护等技术改善微波天线的性能.进而提出微波天线选择的优化方案.     

Maximum-likelihood bearing estimation with partly calibrated arraysin spatially correlated noise fields

The problem of using a partly calibrated array for maximum likelihood (ML) bearing estimation of possibly coherent signals buried in unknown correlated noise fields is shown to admit a neat solution under fairly general conditions. More exactly, this paper assumes that the array contains some calibrated sensors, whose number is only required to be larger than the number of signals impinging on the array, and also that the noise in the calibrated sensors is uncorrelated with the noise in the other sensors. These two noise vectors, however, may have arbitrary spatial autocovariance matrices. Under these assumptions the many nuisance parameters (viz., the elements of the signal and noise covariance matrices and the transfer and location characteristics of the uncalibrated sensors) can be eliminated from the likelihood function, leaving a significantly simplified concentrated likelihood whose maximum yields the ML bearing estimates. The ML estimator introduced in this paper, and referred to as MLE, is shown to be asymptotically equivalent to a recently proposed subspace-based bearing estimator called UNCLE and rederived herein by a much simpler approach than in the original work. A statistical analysis derives the asymptotic distribution of the MLE and UNCLE estimates, and proves that they are asymptotically equivalent and statistically efficient. In a simulation study, the MLE and UNCLE methods are found to possess very similar finite-sample properties as well. As UNCLE is computationally more efficient, it may be the preferred technique in a given application     

Performance analysis of direction finding with large arrays andfinite data

This paper considers analysis of methods for estimating the parameters of narrow-band signals arriving at an array of sensors. This problem has important applications in, for instance, radar direction finding and underwater source localization. The so-called deterministic and stochastic maximum likelihood (ML) methods are the main focus of this paper. A performance analysis is carried out assuming a finite number of samples and that the array is composed of a sufficiently large number of sensors. Several thousands of antennas are not uncommon in, e.g., radar applications. Strong consistency of the parameter estimates is proved, and the asymptotic covariance matrix of the estimation error is derived. Unlike the previously studied large sample case, the present analysis shows that the accuracy is the same for the two ML methods. Furthermore, the asymptotic covariance matrix of the estimation error coincides with the deterministic Cramer-Rao bound. Under a certain assumption, the ML methods can be implemented by means of conventional beamforming for a large enough number of sensors. We also include a simple simulation study, which indicates that both ML methods provide efficient estimates for very moderate array sizes, whereas the beamforming method requires a somewhat larger array aperture to overcome the inherent bias and resolution problem     

Maximum Likelihood Localization of a Diffusive Point Source Using Binary Observations

In this paper, we investigate the problem of localization of a diffusive point source of gas based on binary observations provided by a distributed chemical sensor network. We motivate the use of the maximum likelihood (ML) estimator for this scenario by proving that it is consistent and asymptotically efficient, when the density of the sensors becomes infinite. We utilize two different estimation approaches, ML estimation based on all the observations (i.e., batch processing) and approximate ML estimation using only new observations and the previous estimate (i.e., real time processing). The performance of these estimators is compared with theoretical bounds and is shown to achieve excellent performance, even with a finite number of sensors     

Maximum likelihood estimation and Cramer-Rao bounds for directionof arrival parameters of a large sensor array

A maximum likelihood (ML) method is developed for estimation of direction of arrival (DOA) and associated parameters of narrowband signals based on the Taylor's series expansion of the inverse of the data covariance matrix R for large M, M specifying number of sensors in the array. The stochastic ML criterion function can thus be simplified resulting in a computationally efficient algorithm for DOA estimation. The more important result is the derivation of asymptotic (large M) expressions for the Cramer-Rao lower bound (CRB) on the covariance matrix of all unknown DOA angles for the general D source case. The derived bound is expressed explicitly as a function of snapshots, signal-to-noise ratio (SNR), sensors, separation, and correlation between signal sources. Using the condition of positive definiteness of the Fisher information matrix a resolution criterion is proposed which gives a tight lower limit on the minimum resolvable angle     

最大似然卷积混合离散信号盲分离

In this paper, a Maximum Likelihood (ML) approach, implemented by Expec-tation-Maximization (EM) algorithm, is pro-posed to blind separation of convolutively mixed discrete sources. In order to carry out the expectation procedure of the EM algorithm with a less computational load, the algorithm named Iterative Maximum Likelihood algorithm (IML) is proposed to calculate the likelihood and recover the source signals. An important feature of the ML approach is that it has robust performance in noise environments by treating the covariance matrix of the additive Gaussian noise as a parameter. Another striking feature of the ML approach is that it is possible to separate more sources than sensors by exploiting the finite alphabet property of the sources. Simulation results show that the proposed ML approach works well either in determined mixtures or underdetermined mixtures. Furthermore, the performance of the proposed ML algorithm is close to the performance with perfect knowledge of the channel filters.     

Target Location Estimation in Sensor Networks With Quantized Data

A signal intensity based maximum-likelihood (ML) target location estimator that uses quantized data is proposed for wireless sensor networks (WSNs). The signal intensity received at local sensors is assumed to be inversely proportional to the square of the distance from the target. The ML estimator and its corresponding Crameacuter-Rao lower bound (CRLB) are derived. Simulation results show that this estimator is much more accurate than the heuristic weighted average methods, and it can reach the CRLB even with a relatively small amount of data. In addition, the optimal design method for quantization thresholds, as well as two heuristic design methods, are presented. The heuristic design methods, which require minimum prior information about the system, prove to be very robust under various situations     

Efficient realisation of alternating projection algorithm for maximum likelihood direction finding

An efficient iterative maximum likelihood (ML) algorithm for direction finding of multiple sources incident on an array of sensors is presented. This algorithm has smaller computational complexity per iteration and less number of iterations to convergence as compared to the alternating projection (AP) algorithm.<>     

Exact and approximate maximum likelihood localization algorithms

Sensors at separate locations measuring either the time difference of arrival (TDOA) or time of arrival (TOA) of the signal from an emitter can determine its position as the intersection of hyperbolae for TDOA and of circles for TOA. Because of measurement noise, the nonlinear localization equations become inconsistent; and the hyperbolae or circles no longer intersect at a single point. It is now necessary to find an emitter position estimate that minimizes its deviations from the true position. Methods that first linearize the equations and then perform gradient searches for the minimum suffer from initial condition sensitivity and convergence difficulty. Starting from the maximum likelihood (ML) function, this paper derives a closed-form approximate solution to the ML equations. When there are three sensors on a straight line, it also gives an exact ML estimate. Simulation experiments have demonstrated that these algorithms are near optimal, attaining the theoretical lower bound for different geometries, and are superior to two other closed form linear estimators.     

The estimation of the directions of arrival of the spread-spectrum signals with three orthogonal sensors

An effective technique in estimating the directions of arrival (DOAs) of incoming signals using three orthogonal sensors is proposed. The channel model with multipath transmissions for code-division multiple access (CDMA) users whose signals are modulated with binary phase-shift keying (BPSK) is used. Utilizing the maximum-likelihood (ML) method, the channel impulse response vectors of the three sensors can be estimated, then all of the corresponding propagation delays and amplitude weights of the three sensors' channels can be obtained. By comparing the propagation delays of the three channels, we can identify the same signal replica, then its DOA can be estimated by the relation of the three corresponding attenuation weights. Calculating results show this method can reach fairly high accuracy, and it needs only three sensors while in other techniques the required number of sensors is greater than the number of estimated signals.