network-of-matlab-toolbox

说明：  viterbi的MATLAB编程，大家可以看看。谢谢(viterbi of MATLAB programming, we can look at. Thanks)

线性调频信号，脉冲信号，M序列码等模糊函数图仿真分析(Chirp signal, pulse signal, M sequence codes ambiguity function diagram simulation analysis)

采用了小波去噪的思想，独立成分分析算法降低原始数据噪声，一个师兄的毕设。( Using wavelet denoising thought, Independent component analysis algorithm reduces the raw data noise, A complete set of brothers.)

超宽带程序示范程序\M-PSK8psk.rar(Ultra-wideband Program Files demonstration program M-PSK 8psk.rar)

数字维纳FIR（有限长）滤波与理想滤波效果比较(wiener filter)

在图像中采用目标模板匹配对目标进行跟踪，跟踪快速稳定，效果很好。(Using the target in the image template matching target tracking, tracking fast and stable, with good results.)

该程序主要进行重力一位界面的反演，该程序精度准确,具有很好的实际应用效果。(The program mainly for gravity inversion of an interface, the program precision, with good practical results.)

说明：  matlab图像处理，以一一个你一好呵呵合金(matlab)

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.)

一个非常好用的国外牛人写的kalman滤波程序，(kfdemo.m is a Matlab script file to run a demonstration of the Bayesian Kalman filter. It loads file kfdemo.mat (saved as version 4 so that it will read in either v4 or v5 Matlab).The other files are called by kf.m (the Kalman filter) or included as they may be useful. Normalis.m normalises data to zero-mean, unit variance along components. I always do this before any further analysis.)