语音信号阈值去噪

传递对准中的挠曲和柑臂计算,该代码只是个计算的例子,提供方法(Transfer Alignment and Gan arm deflection calculation, the code is just a calculation example, provide a way)

说明：  这个资料是学习kalman滤波的基础材料，里面有程序和文档介绍，适合初学的童鞋(The attachment is the material for Kalman learning, especally for beginners.Hopefully, it will help)

KALMAN FILTER CODE for tracking

function [BESTX,BESTY,ALLX,ALLY]=ImageClassify(K,N,Pm,LB,UB,D,c,m)

说明：  实现脊线提取，处理信号工具，基于小波变换，可以进行脊线提取(Ridges extraction and signal processing are implemented. Ridge extraction can be done based on wavelet transform.)

OFDM LSE Channel Estimation； the performance of LSE channel estimation will be evaluated based on bit error rate in received data(OFDM Channel Estimation; performance of the channel estimation will LSE be evaluated based on bit error rate in received data)

说明：  运动载体的定位与导航一般应用Kalman滤波算法。可 靠的Kalman滤波算法要求有可靠的函数模型、随机模型以 及合理的估计方法。实际情况中运动载体一般难以确保规 则运动，因而精确的函数模型的构造十分困难。近年来， 有不少学者对机动载体运动模型进行了讨论，所属内容各 具特点，建立的各种模型包括微分多项式模型、常速度模 型、常加速度模型、时间相关模型，半马尔柯夫模型、 Noval统计模型及机动载体的“当前”统计模型(Moving vehicle positioning and navigation general application of the Kalman filter algorithm. Kalman filter algorithm requires a reliable function model, a stochastic model, as well as a reasonable estimation method. Moving carrier in the actual situation is generally difficult to ensure a regular motion, and thus the the precise function model structure is very difficult. In recent years, many scholars have discussed the movement of the motor carrier model, each with distinct characteristics of their respective content, establish various models including the differential polynomial model, constant velocity model, constant acceleration model, time-dependent model, semi-Markov model, Noval statistical model and motor carrier &quot current&quot statistical model)

说明：  matlab编写的动态跟踪程序，可用USB摄像头来实现(Dynamic tracking program written in matlab)

基于压缩感知(应用匹配追踪算法)处理二维信号(MP2D compressed sensing using mp)

说明：  利用遗传算法优化10元稀疏天线阵列的单位间距，得到低副瓣的结果(Using genetic algorithm to optimize the unit spacing of 10 element sparse antenna array, the result of low sidelobe is obtained)