TDOAAOA

经典的扩展卡尔曼滤波卡尔曼滤波程序，首先对非线性函数取一阶近似，生成近似的新型函数，然后进行卡尔曼滤波，通过一步状态和估协方差估计产生新息，然后生成状态预测(Classical extended Kalman filter, Kalman filter procedure, first take the first order approximation of nonlinear function to generate the new function approximation, and then proceed to the Kalman filter step state estimate covariance is estimated to generate new interest, and then generate the state prediction)

主要利用稳健方法比如迭代算法来对跳频信号在Alpha稳定分布噪声环境下进行参数估计(Main advantage of robust methods such as iterative algorithm for frequency-hopping signals Alpha stable distribution parameter estimation in noisy environments)

雷达系统分析仿真，包括线性调频信号仿真，脉冲压缩，模糊函数图形，MTI、MTD等(Radar system analysis and simulation, including linear FM signal simulation, pulse compression, the ambiguity function graphics, MTI, MTD)

自适应波束形成算法中，LMS算法和RLS算法的性能分析与比较优劣性(Adaptive beamforming algorithm, LMS algorithm and RLS algorithm performance analysis and compare the advantages and disadvantages of)

模式分类 课后练习答案 佩斯大学整理版本，每章都有（包括 7 8 9 10章） 平均20多道题，跟网内那种一章选几道的完全不一样~~谁用谁知道pattern classification(Duda pattern classification on the answer machine after-school practice exercises finishing version Pace University (New) pattern classification)

一种实用的交互式多模型跟踪算法IMM，真实好用，测试过(A practical interactive multi model tracking algorithm IMM, which is true and useful and tested)

TSP问题的人工蚁群算法中，假设m只蚂蚁在图的相邻节点间移动，从而协作异步地得到问题的解。每只蚂蚁的一步转移概率由图中的每条边上的两类参数决定：1、信息素质，也称信息素痕迹。2、可见度，即先验值。(Artificial ant colony algorithm TSP problem, assume that m ants move between adjacent nodes in the graph, which asynchronously collaborate get problem solution. Each ant transition probability is determined by the figure of two kinds of parameters for each edge: 1, the quality of information, also called pheromone trails. 2, visibility, ie priors.)

IT WILL DATAWRAPING WITN LESSION DETECTION IN THE TISSUE AND METASIS

mimo的仿真，误码率，比特率随接收天线的变化情况(mimo simulation, error rate, the bit rate changes with the receiving antenna)

一种无线传感网络寿命优化算法2，研究无线传感网络非常有用(A wireless sensor network lifetime optimization algorithms in wireless sensor networks are useful)