LFM_radar_signal

CDMA coding and decoding mechanism

最大熵分割，成功调试过，可以完全实现最大熵分割，效果完美(Maximum Entropy Segmentation, successfully debugged Maximum Entropy Segmentation can be fully realized, the effect is perfect)

Introduction to Simulink with Engineering Applications 2nd Edition Mar 2008

Antoni的快速峭度谱程序以及相应地MSSP论文(Programs and manuscripts of the Fast Kurtogram proposed by Antoni)

一种基于蚁群算法的旅行商问题的源代码.............................................................................(TSP BASED ON ANT COLONY ALGORITHM)

以上是驱动系统的混沌微分方程的matlab源程序(The above is the chaos of driving system of differential equation of the source program)

In this i attached matlab code for indoor localization using WIFI and FM signal features classified by KNN classifier to predict the fingure print of the building structure.

功率谱估计是利用有限长的数据估计信号的功率谱，广泛应用于各个领域。功率谱估计主要分为经典谱估计与现代谱估计。常用的经典谱估计方法有周期图法，相关法，周期图的改进法，常用的现代谱估计方法有最大熵谱估计，AR模型，MA模型，ARMA模型。经典谱估计适用于长序列的信号，其主要缺陷是描述功率谱波动的数字特征方差性能差，频率分辨率低，现代谱估计适用于短序列的信号，旨在改善谱估计的分辨率，并将其应用于实际地震资料的谱分析。 (Power spectrum estimation is the use of a finite length data to estimate the power spectrum of the signal, widely used in various fields. Power spectrum estimation is divided into classic and modern spectral estimation spectral estimation. Commonly used methods of classical periodogram spectrum estimation method, the relevant law, the cycle graph Improvement Act, commonly used in modern spectral estimation methods have maximum entropy spectral estimation, AR model, MA model, ARMA model. Classical spectral estimation applied to signal a long sequence, its main drawback is the fluctuation power spectrum describing digital signature variance performance is poor, the frequency resolution is low, modern spectral estimation applied to signal short sequences designed to improve spectral estimation resolution, and The analysis applied to real seismic data spectrum.)

Image evaluation (mean, clarity, information entropy, standard deviation)

采用经典的数据关联方法算法实现的对单目标和多目标的跟踪(Classic data association algorithm was adopted to realize the track of single objective and multiple objectives)