未知数量稀疏源盲分离的一种新方法

提出了一种新的用于未知数量稀疏源盲分离的统一方法。为了改善聚类分离的精度,该方法选取混合空间中半径给定的、中心位于原点的超球面以外的所有数据点,然后将这些数据点映射到中心位于原点的单位超球面上以得到集合Cy。由此,原来的聚类变为致密聚类,各聚类互相重叠的现象几乎消失。随后,先通过关于Cy的聚类分离来估计混合矩阵,再根据混合矩阵估计源,其中最佳不相似阈值和相应的聚类数量是自动生成的。计算机仿真结果验证了该方法对具有不同程度稀疏性源的有效性。当源充分稀疏时,重构信噪比大约是300 dB。因此,该方法精确、便利。     

现代故障诊断技术研究现状与趋势

回顾了状态监测与故障诊断技术的发展历史,归纳和总结了基于系统数学模型、基于系统输入输出信号处理以及基于人工智能等途径的故障诊断方法。从混合智能诊断技术、BIT技术、远程协作诊断技术3个方面,对现代故障诊断技术的发展趋势和有待解决的问题进行了分析与探讨。提出了故障诊断领域目前和将来的研究方向。     

PD雷达信号时频特性分析

应用时频分析的基本理论和基本方法,分析了不同脉冲宽度、不同脉冲重复频率以及不同采样频率对PD雷达发射信号时频分布特征的影响,研究了不同目标距离、不同目标速度、不同目标加速度以及噪声背景下不同目标反射强度条件下PD雷达目标反射信号的时频分布特征,并使用MATLAB时频分析工具箱对其进行仿真。     

直扩信号侦收技术研究

DS信号是一种具有优良电子防御能力的新型通信信号,广泛运用于军事、民用通信系统中。对抗这种技术的侦察手段目前还没有取得实用性的进展,因此,需开展大量的理论和实践工作。总结了在低信噪比下对直接序列扩频信号侦收的方法,并比较各个方法的优缺点,探讨了其中一些关键问题和新技术途径。     

脉压雷达信号处理仿真分析研究

针对脉冲压缩雷达,从系统仿真的角度建立了描述雷达与目标位置的三维坐标系,根据雷达信号脉冲压缩和非相参积累模型,通过计算机Matlab仿真得出了目标回波经过脉压雷达信号处理后的结果,并进一步分析了脉冲压缩和脉冲积累处理的效能。结果表明:目标回波信号通过脉冲压缩和脉冲积累处理后,显著提高了目标回波信噪比以及对目标距离估计的精度。     

基于联合时频滤波的多目标回波信号分离算法

针对弹道中段多目标回波信号分离的问题,提出了基于联合时频滤波的多目标信号分离算法。该算法先对多目标回波信号进行自相关处理和峰值提取,得到目标信号的周期,接着用该周期对回波信号的Gabor变换和PWVD变换结果进行分段,然后将各段时频图相乘,获得新的时频支撑域,最后用新的时频支撑域对回波信号进行时频滤波,克服了交叉项的干扰和时频聚集性不高的缺点,分离出了不同周期的目标信号。仿真结果证明了该方法的有效性和先进性。     

Signal‐to‐interference‐plus‐noise ratio estimation for wireless communication systems: Methods and analysis

The Signal‐to‐Interference‐plus‐Noise Ratio (SINR) is an important metric of wireless communication link quality. SINR estimates have several important applications. These include optimizing the transmit power level for a target quality of service, assisting with handoff decisions and dynamically adapting the data rate for wireless Internet applications. Accurate SINR estimation provides for both a more efficient system and a higher user‐perceived quality of service. In this paper, we develop new SINR estimators and compare their mean squared error (MSE) performance. We show that our new estimators dominate estimators that have previously appeared in the literature with respect to MSE. The sequence of transmitted bits in wireless communication systems consists of both pilot bits (which are known both to the transmitter and receiver) and user bits (which are known only by the transmitter). The SINR estimators we consider alternatively depend exclusively on pilot bits, exclusively on user bits, or simultaneously use both pilot and user bits. In addition, we consider estimators that utilize smoothing and feedback mechanisms. Smoothed estimators are motivated by the fact that the interference component of the SINR changes relatively slowly with time, typically with the addition or departure of a user to the system. Feedback estimators are motivated by the fact that receivers typically decode bits correctly with a very high probability, and therefore user bits can be thought of as quasipilot bits. For each estimator discussed, we derive an exact or approximate formula for its MSE. Satterthwaite approximations, noncentral F distributions (singly and doubly) and distribution theory of quadratic forms are the key statistical tools used in developing the MSE formulas. In the case of approximate MSE formulas, we validate their accuracy using simulation techniques. The approximate MSE formulas, of interest in their own right for comparing the quality of the estimators, are also used for optimally combining estimators. In particular, we derive optimal weights for linearly combining an estimator based on pilot bits with an estimator based on user bits. The optimal weights depend on the MSE of the two estimators being combined, and thus the accurate approximate MSE formulas can conveniently be used. The optimal weights also depend on the unknown SINR, and therefore need to be estimated in order to construct a useable combined estimator. The impact on the MSE of the combined estimator due to estimating the weights is examined. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

某型飞机魔球信号采集与分析

从阐述魔球操作对目标瞄准的实现机制入手,分析了数据采集系统的总体结构及采集系统的硬件设计和软件设计思想.实现了对飞机魔球信号的实时采集,并对采集结果进行仿真分析,从而推导出不同相关武器种类、目标与弹轴不同夹角、不同目标距离、不同工作状态下魔球信号的变化规律.     

基于分形维数的数字调制信号识别

在复杂的电磁环境下,如何快速准确地对低信噪比下的常见通信信号进行较全面的识别是一个亟待解决的问题。在对信号分形维数仿真讨论的基础上,提出了一种新的通信信号识别方法。首先,对接收到的信号进行预处理,然后提取其盒维数和信息维数,将两者作为识别的特征参数,最后基于支持向量机实现未知信号的识别。由仿真结果分析可知:预处理后的信号序列的盒维数和信息维数分离度高且受噪声和信号参数的影响小,识别结果较好。在信噪比为5 dB时,不同调制类型信号间的识别准确率最低为87%。     

基于Jacobsen法的LFMCW雷达测距算法

提出了一种基于Jacobsen法的LFMCW雷达测距算法。该算法通过应用Jacobsen法估计规则区差拍信号的频率,并对多段规则区差拍信号进行联合分析,以提高算法的稳定性和测距精度。实验结果表明:测距范围为10~10.25 m,信噪比为8~12 dB,算法的绝对误差始终小于1 mm;噪声干扰会在一定程度上影响算法的测距精度。