Position-Location-Techniques-and-Applications

对卡尔曼滤波原理进行了仿真！ 可用于雷达脉冲受随机噪声干扰后的原始数据最优估计。(The principle of the Kalman filter simulation! Can be used for radar pulse by the random noise of the original data after the optimal estimation.)

1）了解小波分解与小波包分解概念区别与联系； 2）了解小波包的分解和重构算法积； 3）用matlab编程对信号进行小波包分解及小波能量图的绘制。 (1) to understand the wavelet decomposition and wavelet packet decomposition of differences and relations between concepts 2) to understand the wavelet packet decomposition and reconstruction algorithms of product 3) the signal using matlab programming wavelet packet decomposition and wavelet energy mapping.)

eeglab11_0_0_0b最新版eeglab，界面更清晰，功能更全面。 这是一款可以处理采集到的脑电数据以及ERP数据的软件，富含各种数据处理方法ICA，频谱分析等。(Latest version eeglab, the interface is clearer, more comprehensive.This plugin can process the acquired EEG data and ERP data, a variety of data-rich approach the ICA and spectral analysis.)

am调制信号，适合matlab仿真，希望能帮到大家，功能还算可以(am modulation signal, for matlab simulation, hope to help you, you can still function)

一篇关于宽带MIMO-OFDM系统的文章，从最基本的系统结构，到复杂的信道估计等都有极其详细的论述，可以说代表了当今MIMO-OFDM系统研究的潮流和研究现状的总述！(one of the Broadband MIMO-OFDM articles from the most basic system structure, to complex channel estimation and others all have very detailed exposition It can be said on behalf of today's MIMO-OFDM system and the trend of the total study to the status quo!)

Wavelet matlab Wavelet matlab

alamouti_new: Orthogonal space-time block coding for 2xM antenna configurations.

说明：  IBD算法源代码 能够让初学者很好的了解IBD算法(blind image restoration)

本代码是关于MIMO的空时分组码的仿真，内容比较全面。(The code is on the MIMO space-time block code simulation, content more comprehensive.)

说明：   empirical formula with kaiser clc clear all fs=1000 fc=250 df=50 r=0.001 f=fc/fs dw=2*pi*(df/fs) a=-20*log(r) n=floor(((a-8)/(2.285*dw))+1) if a>50 b=0.1102*(a-8.7) elseif a>=21 && a<=50 b=0.5842*((a-21)^0.4)+0.07886*(a-21) elseif a<21 b=0.0 end w=kaiser(n,b) for i=1:n if i~=(n-1)/2 hd(i)= (2*f*sin((i-((n-1)/2))*2*pi*f))/((i-((n-1)/2))*2*pi*f) elseif i==(n-1)/2 hd(i)=2*f end end for j=1:n h(j)=w(j)*hd(j) end subplot(3,1,1), plot(w) subplot(3,1,2), plot(h) subplot(3,1,3), plot(h,n)