kalman

快速fft的自编程序和DHT的程序，帮助理解FFT的原理和DHT的原理，进一步加快算法研究做好准备(Quick fft of self procedures and DHT program to help understand the principles and DHT FFT principle, ready to further accelerate the algorithm)

说明：  对SUI-3信道仿真和分析的文章，很不错，与大家分享！(On SUIchannel simulation and analysis of the article, it is good to share with you)

svmref1VR m file SVM一对多递归特征消除特征选择算法(SVM-RFE 1VR m file)

TM波照射导体圆柱，根据电场积分方程求表面电流。(TM-wave radiation cylindrical conductor, under the electric field integral equation for surface currents.)

1.设计自适应信号分离器，用以从白噪声中提取周期信号。其中选取正弦信号s=sin(2*pi*t/10)为周期信号，宽带噪声信号为高斯白噪声，设置参考通道延迟为50。 2.宽带信号中的窄带干扰抑制实际上是自适应信号分离的一种应用，借助自适应信号分离器可以方便的实现窄带干扰的对消。(1. Design of adaptive signal separator, used to extract from the white noise periodic signal. The extraction of sinusoidal signals s = sin (2* pi* t/10) for periodic signals, broadband noise signal is Gaussian white noise, set the reference channel delay of 50. 2. Wideband signals in the narrow-band interference suppression is actually an application of adaptive signal separation, using adaptive signal separator can be easily achieved narrowband interference cancellation.)

problems of optical communications

该源码使用C4.5算法进行分类，该程序为matlab源码。(Classify using Quinlan s C4.5 algorithm)

this function perform and make block diagram on image with defferent image and block size . by using matlab program

在机动目标跟踪中，机动目标模型是机动目标跟踪的基本要素之一，一般希望机动目标模型能准确表征目标机动时的各种运动状态。比较常用的模型有匀速运动(CV)模型、匀加速运动(CA) 模型、时间相关模型（Singer）和机动目标“当前”统计模型。上述模型均采用机动频率表征目标的机动情况。在应用当中，通常采用固定的机动频率，这就表示机动目标的机动时间是一定的，而实际上机动目标的机动时间是不断变化的，也就是说机动频率是不断变化的，采用固定机动频率必然会带来误差。采样周期在0.5—2S时，机动频率越小跟踪精度越高[1]，但机动频率仍然是固定值。本文提出的基于神经网络的机动频率自适应调整方法可以使机动频率随机动而变化，从而提高状态估计的准确性，提高跟踪精度。本文将小波神经网络用于机动目标跟踪中机动频率的自适应调整，该算法对机动目标“当前”统计模型中的机动频率进行实时修改, 从而自适应的改变机动频率,使跟踪算法与目标的真实状态更接近。该算法采用小波神经网络的离线训练，实时性好。(The maneuver of the maneuvering target is uncertain. The maneuvering frequency is constantly changeable, but traditionally it is beforehand determined as a constant based on the target state estimation in the state model of the maneuvering target. The maneuver of the maneuvering target makes the kinematics equation of the target model mismatch with the practical motion model and the tracking error will be increased. Based on the advantages of the self-learning, the rapid convergence rate and the nonlinear approximation ability of the wavelet neural network, it was put forward to be used in the field of target tracking in the paper. The new residual is used as the input of the wavelet neural network, the output of the network is used to adjust adaptively the maneuvering frequency of the CS model. The algorithm is more close to the real state of the target. The simulation results showed that tracking error can be reduced and the tracking accuracy can be improved.)

说明：  地震勘探的必备工具。大家相互交流，请专业方面的朋友多多指教！(An indispensable tool for seismic exploration. U.S. mutual exchanges, please professional exhibitions Friend!)