OFDM

在机动目标跟踪中，机动目标模型是机动目标跟踪的基本要素之一，一般希望机动目标模型能准确表征目标机动时的各种运动状态。比较常用的模型有匀速运动(CV)模型、匀加速运动(CA) 模型、时间相关模型（Singer）和机动目标“当前”统计模型。上述模型均采用机动频率表征目标的机动情况。在应用当中，通常采用固定的机动频率，这就表示机动目标的机动时间是一定的，而实际上机动目标的机动时间是不断变化的，也就是说机动频率是不断变化的，采用固定机动频率必然会带来误差。采样周期在0.5—2S时，机动频率越小跟踪精度越高[1]，但机动频率仍然是固定值。本文提出的基于神经网络的机动频率自适应调整方法可以使机动频率随机动而变化，从而提高状态估计的准确性，提高跟踪精度。本文将小波神经网络用于机动目标跟踪中机动频率的自适应调整，该算法对机动目标“当前”统计模型中的机动频率进行实时修改, 从而自适应的改变机动频率,使跟踪算法与目标的真实状态更接近。该算法采用小波神经网络的离线训练，实时性好。(The maneuver of the maneuvering target is uncertain. The maneuvering frequency is constantly changeable, but traditionally it is beforehand determined as a constant based on the target state estimation in the state model of the maneuvering target. The maneuver of the maneuvering target makes the kinematics equation of the target model mismatch with the practical motion model and the tracking error will be increased. Based on the advantages of the self-learning, the rapid convergence rate and the nonlinear approximation ability of the wavelet neural network, it was put forward to be used in the field of target tracking in the paper. The new residual is used as the input of the wavelet neural network, the output of the network is used to adjust adaptively the maneuvering frequency of the CS model. The algorithm is more close to the real state of the target. The simulation results showed that tracking error can be reduced and the tracking accuracy can be improved.)

语音信号盲分离的一个号用的程序给大家共享，希望对大家有用(Blind separation of speech signals with a number of procedures to share, we hope to be useful)

基于频率方差和子带分离的语音端点检测程序，基于matlab开发(based on specband var Vad detection programe,using matlab)

不等半径泊车方法仿真代码，实现不等半径泊车算法的仿真，车辆参数如车长、车宽、转弯半径以及车位参数如长度、宽度均可调。(Simulation code radius ranging from parking way to achieve a radius ranging from parking the simulation algorithm, vehicle parameters such as vehicle length, vehicle width, turning radius and parking parameters such as length, width can be adjusted.)

几个轴承类的matlab文件 里面包含几个实例(Several bearing class matlab file which contains a few examples)

Based on Linear Membership

说明：  光伏电池simulink模型，分析了光照强度和温度变化时，功率的变化情况(Simulink model of photovoltaic cell)

说明：  处理图像使用其方法可以更好的查找图像中的标记特征点(Image processing methods can better use its search feature points marked in the image)

The model used for creating the reference voltage is shown in Fig. 4. First, photovoltaic output current (Ipv) and output voltage (Vpv) are passed through a first order low pass filter with a magnitude of G = 1 and a time constant of T = 0.01 seconds in order to filter out the high frequency components or harmonics from these signals as shown in Fig. 5 and Fig. 6. The filtered current and voltage signals (Ipv_F and Vpv_F) are then fed into the MPPT control block that uses the Incremental Conductance Tracking Algorithm. An algorithm that is based on the fact the slope of the PV array power curve shown in Fig. 7 is zero at the Maximum Power Point (MPP), positive on the left of the MPP, and negative on the right. The MPP can thus be tracked by comparing the instantaneous conductance (I/V) to the incremental conductance (∆ I/∆ V) [11] as in (1):

从别人那要来和收集到的很齐全的matlab的PID程序 希望对大家有帮助(From others that want to come and collect the PID is complete matlab program hopes to help everyone)