particle-filter-target-tracking

MATLAB 模式识别包 初学者的好工具 (Pattern recognition package MATLAB good tool for beginners)

自适应滤波matlab源程序，含有波形仿真程序，以及自适应滤波原理，lms算法介绍。(Adaptive filter matlab source code, containing the waveform simulation program, as well as the principle of adaptive filtering lms algorithm is introduced.)

说明：  空间信号的波达方向（Direction of Arrival，简称DOA）估计作为阵列信号处理研究的一个主要课题，在近些年来越来越得到大量的关注和研究。它可实现对空间中信源信号的准确的角度估计，实现信源方位等参数的具体精确检测，并且还具有很高的分辨力。这些特性使得DOA估计在声呐、通信及雷达等多种领域具有广阔的应用和发展前景。此程序为面阵中二维角度估计算法程序。(direction of arrival,usually called DOA,has been an important subject recently.This program contains unitary esprit)

说明：  matlab教程，以网页的形式，比较简单、实例很充分，适合与初学。可以看看。(matlab tutorial to the page the form of relatively simple, examples of very good, suitable with the beginners. Can look at.)

基于矢量控制算法的永磁同步电机电压前馈PI控制模型，可作为算法开发的基础模型。(The voltage feed-forward PI control model of PMSM based on vector control algorithm can be used as the basic model of algorithm development.)

基于Matlab6.5的 声卡数据采集程序 可以简便的进行方波 曲线信号的采集(Sound card Matlab6.5 based data acquisition program can be simple square wave signal acquisition curve)

stateflow九州恒润培训教材，重量级资料(stateflow training workshop for you)

MATLAB 6.5 辅助小波分析与应用 pdf格式文本书籍(MATLAB 6.5 auxiliary wavelet analysis and application pdf format text books)

一个求解纳什交换的均衡问题的例程，运用了类似基因表示的做法，需要的请自行下载(Nash for a balanced exchange of the routines that use a similar approach expressed by the gene, Requests need to download)

遗传算法是计算机科学人工智能领域中用于解决最优化的一种搜索启发式算法，是进化算法的一种。这种启发式通常用来生成有用的解决方案来优化和搜索问题。进化算法最初是借鉴了进化生物学中的一些现象而发展起来的，这些现象包括遗传、突变、自然选择以及杂交等。(In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.[1] Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.)