fd_predator_prey

综合利用了图象处理和图象理解的基本函数，对刚熟悉matlab窗口和菜单编程的朋友很有帮助(Utilization of image processing and image understanding of the basic function, just familiar with the programming menu matlab window and helpful friends)

从多层树中取得偶数生成列，取得动态目标值的个数，从两个不同的树中，提取第二个树中独有的元素（和第一个树的元素不同的元素），并计算其和。(Generated columns to obtain an even number, the target value obtained from the dynamic multi-tree, from two different tree, extracts a second element that is unique in the tree (a tree of elements and different elements), and and calculate its.)

说明：  此程序给出一个简单的粒子滤波编程，通过一个简单的例子来分析粒子滤波算法与EKF算法的性能(This program gives a simple particle filter programming, through a simple example to analyze the particle filter algorithm with the performance of EKF)

模糊自适应PID控制和常规PID控制的仿真，对阶跃信号的仿真(Fuzzy adaptive PID control and conventional PID control simulation, the simulation of the step signal)

This is used to check the healthy of tongue

说明：  一种未知信源数的高分辨DOA 估计算法：众多性能优良的超分辨波达方向(DOA)估计算法通常需要预先判定信源数目，然而，现有的信源数估计 算法在有限采样快拍条件下，估计性能随着信噪比的降低而下降，错误概率也相应增加，最终导致DOA 估计失败。 该文提出一种超分辨的DOA 估计算法，此算法不需要预判信源个数和进行特征值分解，同时在时变环境中，针对 快拍数较少的情况下，依然保持较高的角度分辨能力，可以被认为是综合了Capon 法和MUSIC 法的优点。(Algorithm on High Resolution DOA Estimation without Sources Number：Many of the super resolution DOA(Direction-Of-Arrival) estimation algorithms presume the number of signal sources. Since the sample snapshot is limited, however, the available algorithms on source number estimation may fail at lower SNR. In this paper, a novel algorithm for high resolution DOA estimation is proposed, which not only skips sources number estimation and EVD, but also behaves satisfactorily with a few snapshot, it may be viewed as a combination of merit of conventional Capon and MUSIC method.)

说明：  数据通信中误码检测机制的研究，使用MATLAB对奇偶校验码进行测试(Error Detection Techniques in Data Communications， One-dimensional parity check is simulated by MATLAB)

Project on Image Restoration

简单的数据降维算法（PCA）举例，构造随机的10维数据，降维成3维的。Sample可替换成用户数据(Examples of simple data reduction algorithms (PCA) are presented)

给出了关于卡尔曼滤波器的详细说明，是新人学习必备(Given on a detailed description of Kalman filter is a new learning required)