分组空时块码系统中的排序最大信噪比检测算法

提出并比较了应用于分组空时块码（G—STBC）系统中两种不同信道模型的排序最大信噪比（MaxSNR）检测算法。该系统在发端对发射天线分组，每组进行独立的空时块码编码；在收端应用排序最大信噪比算法进行检测。由于空时块码提供了额外的时域约束，所以可以利用这个约束对信道模型进行变换，得到一个等效信道模型，然后再使用排序MaxSNR检测；当然也可以直接利用原有的信道模型，即非等效信道模型进行检测。仿真结果表明：当接收天线数不少于发射天线数的一半时，采用等效信道模型下的排序MaxSNR算法，系统的误码曲线就能保持陡降性；并且在收发天线数相同时，其性能总是优于非等效信道模型。     

TBM末端机动弹道预测

TBM末端机动弹道的预测是现代防空C^3I系统建模中重要的组成部分，它直接影响着反导武器系统对TBM拦截的精度与概率。为了精确地预测出处于末端机动条件的TBM弹道，并满足防空作战实时性的要求，本文在广义卡尔曼滤波的基础上提出了一种基于输入估计的自适应滤波方法。结论表明，该方法较其它方法更适应于解决TBM末端机动弹道预测问题，具有很好的军事应用价值。     

DMT系统中一种易于实现的时域均衡器

总结了DMT系统中时域均衡器(TEQ)的主要设计方法，指出最大缩短信噪比(MSSNR)TEQ的最优设计方法很难实时实现。提出了一种MSSNR TEQ的次优设计方法，它首先估计出目标抽样窗的最优延迟，然后用分步求解法求出TEQ的抽头系数。和最优方法相比，次优方法的计算量大幅度减少，可实时实现，而其缩短信噪比的损失很小。仿真实验验证了次优方法的有效性。     

基于随机相位误差Tikhonov分布的MC-DS-CDMA系统性能分析

本文针对工作于慢变频率选择性Rayleigh衰落信道中的部分相干的MC-DS-CDMA系统，基于随机相位误差的Tikhonov密度分布提出了误码率上边界的一种分析方法。仿真结果表明，随着环路信噪比的增加，相位误差损耗所产生的系统误码率性能损失变得越来越小，当环路信噪比高于系统信噪比(Eb/N0)20dB时，系统误码率性能损失小于0．2dB。在MC-DS-CDMA系统中，由于能用频率分集取代路径分集，因而接收机结构更易实现。     

调幅系数为任意值的调幅波研究

本文讨论了调幅系数为任意值的调幅波，给出了它的表达式，频谱与调幅系数，调制效率和信噪比增益的计算公式。     

The smooth fractionator

A modification of the general fractionator sampling technique called the smooth fractionator is presented. It may be used in almost every situation in which sampling is performed from distinct items that are uniquely defined, often they are physically separated items or clusters like pieces, blocks, slabs, sections, etc. To each item is associated a ‘guesstimate’ or an associated variable with a more‐or‐less close – and possibly biased ? relationship to the content of the item. The smooth fractionator is systematic sampling among the items arranged according to the guesstimates in a unique, symmetric sequence with one peak and minimal jumps. The smooth fractionator is both very simple to implement and so efficient that it should probably always be used unless the natural sequence of the sampling items is equally smooth. So far, there is no theory for the prediction of the efficiency of smooth fractionator designs in general, and their properties are therefore illustrated with a range of real and simulated examples. At the cost of a slightly more elaborate sampling scheme, it is, however, always possible to obtain an unbiased estimate of the real precision and of some of the variance components. The only real practical problem for always obtaining a high precision with the smooth fractionator is specimen inhomogeneity, but that is detectable at almost no extra cost. With careful designs and for sample sizes of about 10, the sampling variation for the primary, smooth fractionator sampling step may in practice often be small enough to be ignored.     

微光CCD摄像器件综合特性的分析与比较

ＣＣＤ的优良特性使其在微光电视技术领域得到了广泛运用。本文对各种微光ＣＣＤ摄像器件的综合特性例如信噪比、探测率、动态范围与计数模式等进行了详细的研究与对比，并得出基本结论。     

一种红外成像系统作用距离试验评估方法

进行红外成像系统作用距离考核试验时，由于外场试验中的目标特性及气象条件与指标提出对应的目标特性及气象条件有差别，因此需要进行必要的折算。提出一种基于信噪比准则的可行的红外成像系统作用距离试验评估方法。     

Application of the blind source separation method to feature extraction of machine sound signals

As the result of vibration emission in air, a machine sound signal carries important information about the working condition of machinery. But in practice, the sound signal is typically received with a very low signal-to-noise ratio. To obtain features of the original sound signal, uncorrelated sound signals must be removed and the wavelet coefficients related to fault condition must be retrieved. In this paper, the blind source separation technique is used to recover the wavelet coefficients of a monitored source from complex observed signals. Since in the proposed blind source separation (BSS) algorithms it is generally assumed that the number of sources is known, the Gerschgorin disk estimator method is introduced to determine the number of sound sources before applying the BSS method. This method can estimate the number of sound sources under non-Gaussian and non-white noise conditions. Then, the partial singular value analysis method is used to select these significant observations for BSS analysis. This method ensures that signals are separated with the smallest distortion. Afterwards, the time-frequency separation algorithm, converted to a suitable BSS algorithm for the separation of a non-stationary signal, is introduced. The transfer channel between observations and sources and the wavelet coefficients of the source signals can be blindly identified via this algorithm. The reconstructed wavelet coefficients can be used for diagnosis. Finally, the separation results obtained from the observed signals recorded in a semi-anechoic chamber demonstrate the effectiveness of the presented methods .