基于多径分量簇的信号处理技术研究

阵列信号处理在无线通信中有着重要应用。其中,阵列天线与先进的信号处理技术相结合就构成了智能天线系统,利用不同的空间信号处理技术,考察将一个来波簇作为单一来波进行处理时所表现出来的现象及规律,并进行了仿真分析,有关分析结果将为实际移动通信系统中的阵列信号处理技术提供重要的参考,并对系统级别的仿真与性能评估提供有益的帮助。     

一种改进型CE-ML算法的二维DOA估计

基于最大似然估计（ML）的阵列测向方法具有测向精度高、可以分辨相干信号等优点,但是因为计算复杂度过高而工程应用受限。针对该问题,利用交叉熵（CE）方法对最大似然估计快速求解,并对初始样本的产生和平滑参数的设置进行了优化,提出改进型CE—ML二维测向算法,最后进行了算法运算量分析和仿真验证。仿真实验表明,在精度相近条件下,改进型的CE-ML算法的迭代次数大约是粒子群算法（Ps0）的1／3,大大减少了ML测向的计算量。     

光伏InSb硅信号处理技术研究

光伏ＩｎＳｂ硅信号处理技术研究韩建忠，赵建中（电子工业部第十一研究所北京１０００１５）本文介绍了光伏ＩｎＳｂ阵列信号处理电路的设计考虑，以焦平面方式工作的原理及信号读出技术。对电荷放电模式（ＣＤＭ）理论进行了全面测试分析，并提出了进一步改进性能，信号...     

阵列多波束三坐标雷达的数字波束形成及信号处理技术研究

通过对方位上机械旋转、俯仰上进行一维相位扫描的阵列多波束三坐标雷达的研究,对其关键的数字波束形成（DBF）及信号处理技术进行了设计方法分析及仿真,结果表明设计分析方法正确且切实可行,为工程实现打下了良好的基础。     

一种基于LMS算法的天线阵通道失配校正技术及VLSI实现

天线阵各通道之间的通道失配（幅相特性不一致）会严重影响阵列信号处理的性能[1,2],为此在进行阵列信号处理前必须对各通道的幅相特性进行均衡.本文针对天线阵通道之间与频率无关的恒定通道失配问题,提出了一种基于LMS好地校正与频率无关的通道失配.本算法的易于工程实现的通道校正技术.计算机仿真结果表明,采用这种技术可以很文最后还给出了该算法在VLSI（超大规模集成电路）平台上的实现结构.     

被动虚拟阵列的CRB界及最大似然估计方法研究

针对利用机载运动平台对窄带微波信号进行侦测的背景,研究了被动虚拟阵列（PASA）对窄带微波信号的参数估计性能。在考虑方向角、频率和幅度均为未知参数的条件下,推导了方向角估计的克拉美劳界（CRB）的表达式,同时给出了PASA合成孔径长度的选取方案。另外,本文给出了PASA对方位角估计的最大似然（ML）估计算法。研究表明,随着合成孔径长度和信噪比的增加,ML估计误差可以很快地收敛于CRB,但存在阈值效应。计算机仿真结果验证了本文研究结果的正确性。     

一种基于重要抽样的麦克风阵列时延估计

麦克风阵列信号处理已经广泛引用于语音处理、通信等领域。时延的精确估计决定了麦克风阵列声源定位系统的性能。将重要抽样的方法应用到麦克风阵列的时延估计中,可以保证时延估计精确性的前提之下,有效地回避了网格搜索和迭代计算,使结果不依赖于初始值,并且降低了计算的复杂度。仿真实验结果表明,重要抽样时延估计方法在计算量降低一个数量级时,仍保持着与ML网格搜索法相近的性能,更适合麦克风阵列时延的实时估计。     

基于REPT变换和空间EP谱的宽带LFM 信号参数估计

演化谱估计作为研究非平稳信号的重要手段,具有许多优良的性质。将演化谱应用于参数估计和宽带阵列信号处理中,提出了Radon-EP（Evolutionary Periodogram）谱变换（REPT）估计线性调频信号参数和空间分段短时EP谱估计宽带线性调频信号到达角。仿真实验显示了算法的有效性。     

阵列信号处理实验平台设计

本文针对一维线阵和二维面阵的阵列信号处理算法研究,设计了一种发射阵列和接收阵列上下交错排布的二维稀疏超声相控阵列信号处理实验平台,并给出了实验平台的硬件总体功能设计、发射和接收电路设计及软件架构。相控阵声场特性仿真分析结果表明该实验平台具有较好的波束成形和波达方向估计效果。     

阵列信号处理在雷达和移动通信中的应用研究

本文简要概述了有关阵列信号处理的一系列问题,介绍了阵列波束形成技术,重点对阵列信号处理在雷达和移动通信中的应用进行了研究.     

Properties of two-photon pumped cavity lasing in novel dye dopedsolid matrices

Two-photon pumped frequency upconversion cavity lasing at ~600 nm is accomplished in three types of dye-doped solid rods pumped with ~10 ns and 1.06-μm IR laser pulses. The dopant is a new dye, trans-4-[p-(N-ethyl-N-(hydroxyethyl)amino)styryl]-N-methylpyridinium tetraphenylborate, abbreviated as ASPT, which possesses a greater two-photon absorption cross section and stronger upconversion fluorescence emission than common commercial dyes (such as rhodamine). Three different materials were chosen as solid matrices: poly(2-hydroxyethyl methacrylate), VYCOR porous glass, and sol-gel glass. Using a Q-switched Nd:YAG pulse laser as the pump source, strong cavity lasing could be achieved in these three ASPT doped solid rods as well as in ASPT solution in a liquid cell. The spectral, temporal, and spatial characteristics of the cavity lasing output have been systematically investigated. The measured output-input characteristics, lasing lifetime, and damage threshold for the three different rods are presented     

Neural-Network-Based Path Planning for a Multirobot System With Moving Obstacles

Recently, a coordinated hybrid agent (CHA) framework was proposed for the control of multiagent systems (MASs). It was demonstrated that an intelligent planner can be designed for the CHA framework to automatically generate desired actions for multiple robots in an MAS. However, in previous studies, only static obstacles in the workspace were considered. In this paper, a neural-network-based approach is proposed for a multirobot system with moving obstacles. A biologically inspired neural-network-based intelligent planner is designed for the coordination of MASs. A landscape of the neural activities for all neurons of a CHA agent contains information about the agent's local goal and moving obstacles. The proposed approach is able to plan the paths for multiple robots while avoiding moving obstacles. The proposed approach is simulated using both MATLAB and Vortex. The Vortex module executes control commands from the control system module, and provides the outputs describing the vehicle state and terrain information, which are, in turn, used in the control module to produce the control commands. Simulation results show that the developed intelligent planner of the CHA framework can control a large complex system so that coordination among agents can be achieved.      

Maximum-likelihood versus maximum a posteriori parameter estimation of physiological system models: the C-peptide impulse response case study

Maximum-likelihood (ML), also given its connection to least-squares (LS), is widely adopted in parameter estimation of physiological system models, i.e., assigning numerical values to the unknown model parameters from the experimental data. A more sophisticated but less used approach is maximum a posteriori (MAP) estimation. Conceptually, while ML adopts a Fisherian approach, i.e., only experimental measurements are supplied to the estimator, MAP estimation is a Bayesian approach, i.e., a priori available statistical information on the unknown parameters is also exploited for their estimation. In this paper, after a brief review of the theory behind ML and MAP estimators, we compare their performance in the solution of a case study concerning the determination of the parameters of a sum of exponential model which describes the impulse response of C-peptide (CP), a key substance for reconstructing insulin secretion. The results show that MAP estimation always leads to parameter estimates with a precision (sometimes significantly) higher than that obtained through ML, at the cost of only a slightly worse fit. Thus, a three exponential model can be adopted to describe the CP impulse response model in place of the two exponential model usually identified in the literature by the ML/LS approach. Simulated case studies are also reported to evidence the importance of taking into account a priori information in a data poor situation, e.g., when a few or too noisy measurements are available. In conclusion, our results show that, when a priori information on the unknown model parameters is available, Bayes estimation can be of relevant interest, since it can significantly improve the precision of parameter estimates with respect to Fisher estimation. This may also allow the adoption of more complex models than those determinable by a Fisherian approach.     

Performance of the maximum likelihood constant frequency estimatorfor frequency tracking

The performance of maximum likelihood (ML) estimators for an important frequency estimation problem is considered when the signal model assumptions are not valid. The motivation for this problem is to understand the robustness of the hidden Markov model-maximum likelihood (HMM-ML) tandem frequency estimator, where the signal is divided into time blocks, and the frequency in each time block is estimated using the ML approach under the assumption that the signal has a constant frequency in each time block. In order to analyze the sensitivity of ML estimators to the model assumptions, the mean frequency of a discrete complex tone that has a time-varying (ramp) frequency is estimated under the incorrect assumption that it has a constant frequency. In particular, the behavior of the threshold region with respect to different chirp rates is analyzed, and a simple rule is given. The mean squared error above the threshold region is shown to be constant even at very high SNR levels. These results are supported by simulations     

Discrete multiloop, modified multiloop, and plate-loopantennas-multifrequency and wideband VSWR characteristics

Three types of loop antennas are presented: discrete multiloop (ML), modified ML and plate-loop (PL) antennas. The discrete ML and modified ML antennas are composed of N square loops. The N square loops of the modified ML antenna are connected by wires at the loop corners. The PL antenna is regarded as a modified ML antenna with infinite loops (N=∞). The analysis of the discrete ML antenna shows that one of the N loops resonates when its circumference is approximately one wavelength. It follows that the discrete ML antenna has N minima in the frequency response curve of the VSWR. In contrast to the discrete ML antenna, the modified ML has a VSWR with a wide-band frequency response: approximately 16% with N=7, which is more than 2.5 times as wide as that for a single-loop antenna (N=1). Further analysis reveals that the PL antenna has a VSWR bandwidth similar to that of the modified ML antenna. The maximum gain of the PL antenna is approximately 9 dB, which is very close to those of the discrete and modified ML antennas     

Maximum likelihood estimation, analysis, and applications ofexponential polynomial signals

We model complex signals by approximating the phase and the logarithm of the time-varying amplitude of the signal as a finite order polynomial. We refer to a signal that has this form as an exponential polynomial signal (EPS). We derive an iterative maximum-likelihood (ML) estimation algorithm to estimate the unknown parameters of the EPS model. The initialization of the ML algorithm can be performed by using the result of a related paper. A statistical analysis of the ML algorithm is performed using a finite-order Taylor expansion of the mean squared error (MSE) of the estimate about the variance of the additive noise. This perturbation analysis gives a method of predicting the MSE of the estimate for any choice of the signal parameters. The MSE from the perturbation analysis is compared with the MSE from a Monte Carlo simulation and the Cramer-Rao Bound (CRB). The CRB for this model is also derived     

Model-based estimation for dynamic cardiac studies using ECT

The authors develop a strategy for joint estimation of physiological parameters and myocardial boundaries using ECT (emission computed tomography). They construct an observation model to relate parameters of interest to the projection data and to account for limited ECT system resolution and measurement noise. The authors then use a maximum likelihood (ML) estimator to jointly estimate all the parameters directly from the projection data without reconstruction of intermediate images. They also simulate myocardial perfusion studies based on a simplified heart model to evaluate the performance of the model-based joint ML estimator and compare this performance to the Cramer-Rao lower bound. Finally, the authors discuss model assumptions and potential uses of the joint estimation strategy.     

An evaluation of maximum likelihood reconstruction for SPECT

A reconstruction method for SPECT (single photon emission computerized tomography) that uses the maximum likelihood (ML) criterion and an iterative expectation-maximization (EM) algorithm solution is examined. The method is based on a model that incorporates the physical effects of photon statistics, nonuniform photon attenuation, and a camera-dependent point-spread response function. Reconstructions from simulation experiments are presented which illustrate the ability of the ML algorithm to correct for attenuation and point-spread. Standard filtered backprojection method reconstructions, using experimental and simulated data, are included for reference. Three studies were designed to focus on the effects of noise and point-spread, on the effect of nonuniform attenuation, and on the combined effects of all three. The last study uses a chest phantom and simulates Tl-201 imaging of the myocardium. A quantitative analysis of the reconstructed images is used to support the conclusion that the ML algorithm produces reconstructions that exhibit improved signal-to-noise ratios, improved image resolution, and image quantifiability.     

Semi-supervised multi-layered clustering model for intrusion detection

A Machine Learning (ML)-based Intrusion Detection and Prevention System (IDPS) requires a large amount of labeled up-to-date training data to effectively detect intrusions and generalize well to novel attacks. However, the labeling of data is costly and becomes infeasible when dealing with big data, such as those generated by Internet of Things applications. To this effect, building an ML model that learns from non-labeled or partially labeled data is of critical importance. This paper proposes a Semi-supervised Multi-Layered Clustering ((SMLC)) model for the detection and prevention of network intrusion. SMLC has the capability to learn from partially labeled data while achieving a detection performance comparable to that of supervised ML-based IDPS. The performance of SMLC is compared with that of a well-known semi-supervised model (tri-training) and of supervised ensemble ML models, namely RandomForest, Bagging, and AdaboostM1 on two benchmark network-intrusion datasets, NSL and Kyoto 2006+. Experimental results show that SMLC is superior to tri-training, providing a comparable detection accuracy with 20% less labeled instances of training data. Furthermore, our results demonstrate that our scheme has a detection accuracy comparable to that of the supervised ensemble models.     

Unraveling Mechanoluminescent Mechanisms in Doped CaZnOS Materials: Co-Mediation of Trap-Controlled and Non-Trap-Controlled Processes

Doped CaZnOS materials show great potential for mechanoluminescence (ML) applications spanning the ultraviolet-visible-near infrared (UV–vis–NIR) range. However, conflicting reports regarding the generation and reproducibility of ML hinder the understanding and practical utilization of these materials. To address this issue, a comprehensive characterization strategy combining NIR laser-assisted de-trapping, UV irradiation-induced trap-filling, in situ mechanical stimulation, and continuous ML recording is proposed. Herein, the ML behaviors of four representative doped CaZnOS materials (Mn²⁺, Bi³⁺, Er³⁺, and Ce³⁺) are investigated using this approach. The results reveal that de-trapped materials exhibit non-trap-controlled ML, wherein ML intensity gradually weakens under successive mechanical stimuli without self-recovery. In contrast, trap-filled materials demonstrate both trap-controlled ML and non-trap-controlled ML, with the former predominantly contributing to the overall ML intensity. Notably, trap-controlled ML shows only partial recovery after trap filling. The non-trap-controlled ML is attributed to plastic ML and destructive ML phenomena, while explaining trap-controlled ML through the carrier de-trapping model. These results not only clarify conflicting reports but also provide clear insights into the ML properties and mechanisms of CaZnOS-based materials, facilitating advancements in practical applications. Furthermore, the developed characterization strategy is expected to serve as a valuable reference for establishing standardized protocols to evaluate ML performance.