Baseband_System_QPSK

Interactive music visualization

实现图像的JPEG压缩和解压的 Matlab程序(To achieve the JPEG image compression and decompression of the Matlab program)

根据” J.S. Erkelens and R. Heusdens, "Tracking of nonstationary noise based on data-driven recursive noise power estimation”所开发的源码(noisetracker based on data-driven recursive noise power estimation)

使用OFDM-QPSK的调制解调代码，包含一部分时域频域图(Using OFDM-QPSK modulation and demodulation code, contains part of the time-domain frequency domain diagram.)

使用Matlab生成standard array，weight distribution，以及在BCH下的编码过程(Using matlab to generate standary, weight distribution and encoding and decoding in BCH channel. )

说明：  SC Display/output truecolor images with a range of colormaps

内含大量的通信系统调制的simulink仿真程序，包括AMDSB，SSB，FM，GMSK，FSK，OQPSK，PM等simulink仿真(Contains a large number of communication systems modulation simulink simulation program, including AMDSB, SSB, FM, GMSK, FSK, OQPSK, PM, etc. simulink simulation)

An implementation of Unscented Kalman Filter for nonlinear state estimation. (Nonlinear state estimation is a challenge problem. The well-known Kalman Filter is only suitable for linear systems. The Extended Kalman Filter (EKF) has become a standarded formulation for nonlinear state estimation. However, it may cause significant error for highly nonlinear systems because of the propagation of uncertainty through the nonlinear system. The Unscented Kalman Filter (UKF) is a novel development in the field. The idea is to produce several sampling points (Sigma points) around the current state estimate based on its covariance. Then, propagating these points through the nonlinear map to get more accurate estimation of the mean and covariance of the mapping results. In this way, it avoids the need to calculate the Jacobian, hence incurs only the similar computation load as the EKF. For tutorial purpose, this code implements a simplified version of UKF formulation, where we assume both the process and measurement noises are additive to avoid augment of state and a)

智能天线技术是第三代移动通信系统的关键技术之一，也是国内外热门的研究课题。由于无线移动通信的信道传输环境具有复杂性和不确定性，存在多径衰落和时延扩展，因此造成了符号间串扰、同信道干扰、多址干扰等，这些干扰降低了链路性能和系统容量，智能天线技术是解决以上问题的方法之一。 本文首先阐述了智能天线和白适应波束形成的基本理论，然后对自适应算法进行了研究。对一些基本的自适应算法最小均方算法、恒模算法及递推最小均方算法进行了分析讨论，用计算机仿真的结果论证了算法的性能。针对相干干扰介绍了空间平滑技术，对传统的相干干扰抑制方法-空间平滑法进行了分析，采用改进算法有效降低了空间平滑算法的运算量。对信号波达方向(DOA)的估计是智能天线研究的另一关键问题。第四章在介绍了阵列信号处理方法的基础上，先简单介绍了Bartlett、Capon、MUSIC等空间谱估计算法，分析了信源数目过估计与欠估计情况下的MUSIC算法性能，提出一种改进算法，在信源数目有误差的情况下该算法的性能良好。第五章针对多径干扰环境，在分析MMUSIC算法的基础上，本文提出了基于低秩矩阵逼近法的改进MUSIC算法(In this paper, firstly, the author illustrates the principles of smart antenna and adaptive beam forming, and then introduces some typical adaptive algorithms, the LMS ,CMA and RLS algorithms are analyzed and discussed and simulate them in the computer to prove their capability. but they have the disadvantage of high complexity, so they are not fit for practice. The paper analyses the conventional method rejecting spatial smoothing coherent interfering signals, which is spatial smoothing technique)

Signal And System Lab_2