任意稀疏结构的多量测向量快速稀疏重构算法研究

目前的稀疏重构算法求解多量测向量时存在两个问题:一是计算复杂度高;二是不能实现任意稀疏结构的多量测向量重构.为此,本文提出一种多量测向量快速重构算法.该算法首先构建矩阵平滑零范数法,实现对具有任意稀疏结构的多量测向量的重构,并获得多量测向量的初始支撑集;其次根据稀疏度与量测维度的关系,对初始支撑集进行筛选获得预选支撑集;然后采用贝叶斯组检验方式得到信号重构所需的最终支撑集;最后通过最终支撑集实现信号的重构.该算法充分利用了矩阵平滑零范数法的高效性以及贝叶斯组检验对冗余支撑集的剔除功能,不但实现了稀疏位置随机变化的多量测向量的高效重构,而且保证了算法的精度,并对噪声具有一定的鲁棒性,基于实测数据的ISAR成像实验验证了所提算法的有效性.     

卡尔曼滤波在分布式拉曼光纤温度传感系统去噪中的应用

提出一种基于卡尔曼滤波的统计学方法,对光纤温度传感系统的状态进行实时估计并去除系统的噪声,提高光纤传感系统的准确度。光纤温度传感系统属于线性动态系统,被测温度是服从高斯-马尔科夫随机过程的离散时间状态变量,状态噪声是加性高斯白噪声。基于贝叶斯最大后验概率推论（MAP）和最小均方误差（MMSE）准则,新的测量值通过量测更新方程修正后验状态估计值。这种迭代的算法最终可以得到状态的最优估计值。该模型和算法应用在分布式拉曼光纤温度传感系统（DOFS）FGC-LR 中,对其性能进行研究。用局部方差和信噪比评估该算法去噪的能力。常温点处温度的局部方差减小了83.56%,高温点处减小了84.09%。两探测点处的温度信噪比分别提高了18.45%和16.80%。算法在提高光纤传感系统的准确度,实现实时测量上取得了很好的效果。     

一种基于MMSE准则的直接盲均衡新算法及实现

基于互相关向量估计的直接盲MMSE均衡算法通常需要观测向量两个相邻延时的自相关矩阵来构建互相关向量外积.本文提出了一种盲MMSE均衡新算法,利用了统计独立输入信号自相关矩阵是移位矩阵的特殊性质,只需观测向量单个延时的自相关函数便可得到互相关向量的外积,使计算量减少二分之一.然后推导出了新算法的自适应实现过程,实时地对均...     

基于CKF的系统误差与目标状态联合估计算法

针对量测信息中系统误差对目标状态估计精度造成 的不利影响,提出了一种基于容积卡尔曼滤波(CKF)的系统 误差与状态联合估计(JE-CKF)算法。在算法实现中,首先采用状态向量维数扩展方法建立 非线性滤波框架下的系统误差配 准模型,其次根据系统误差配准模型对量测信息中的系统误差进行估计,进而通过对CKF实 现中量测预测值 的修正,改善量测残差中系统误差对滤波精度的影响。理论分析和仿真结果验证了算法的可 行性和有效性。     

EM—HEEM多频率线跟踪算法

基于隐马尔可夫模型的多频率线跟踪算法，能在很低的SNR环境下工作，但量化误差较大，和计算量大。本文提出另一种选择量测向量和计算量测概率的方法，创造条件减小量化误差。又经简单论证，将EM算法和HMM用于多频率线跟踪，严格地（而不是启发式地）得到EM-HMM算法，可以极大地减少计算量。本文又提出获得初始估计以启动EM－HMM算法的二种方法。仿真计算表明，所提的算法是有效的。     

基于稀疏贝叶斯学习的大规模多用户检测算法

针对现有算法大都基于高斯逆伽马先验模型的稀疏贝叶斯学习（GIG-SBL），忽略了稀疏解所对应的支撑集向量稀疏性的问题，提出一种基于伯努利高斯逆伽马先验模型的稀疏贝叶斯学习（BGIG-SBL）架构，通过引入一个伯努利先验的二元向量，设计了单测量向量（SMV）的BGIG-SBL-SMV算法，结合支撑集向量的稀疏性提高重构性能。进一步将该算法扩展到多测量向量（MMV）方案，通过共享相同控制稀疏解的超参数，利用MMV的联合稀疏性提出BGIG-SBL-MMV算法。实验结果表明，所提BGIG-SBL-SMV算法相较于传统GIG-SBL-SMV算法，在mMTC用户检测场景可实现2 dB的性能增益；同时，所提BGIG-SBL-MMV算法相对于单测量向量BGIG-SBL-SMV算法，用户检测率和数据检错率的性能增益可达到4 dB，证明了所提算法的优越性。     

同步误差条件下的DMT均衡算法性能比较

在离散多音调制(DMT)系统下,同步技术是实现信号准确接收的关键技术之一.该文主要研究发送和接收时钟在产生偏差时,对MMSE,MSSNR,Min-ISI和PTEQ四种均衡算法的性能影响.仿真根据G.DMT标准,采用5公里轻型被复线信道冲激响应模型.此时,通过三次样条插值方法对接收信号进行重新采样,从而收发实现时钟偏差.根据蒙特卡洛实验方法的仿真结果得知:此时四种均衡算法性能与时钟理想时的性能相比,信道容量均有较小下降.     

基于空频域信息的改进UKF算法研究

固定单站无源定位系统面临着可观测性弱、初始误差大的问题,为了实现稳定高精度定位,在定位模型中引入角度变化率和多普勒频率变化率信息,并在此基础上提出了一种基于空频域信息的改进不敏卡尔曼滤波(UKF)算法.该算法利用两次观测时刻之间的间隔,根据当前时刻定位结果,通过后向平滑算法平滑估计前一时刻状态向量和协方差矩阵的估计值,...     

基于典型相关性分析的稀疏表示目标追踪

传统稀疏表示目标追踪算法首先通过粒子滤波方法对状态粒子进行采样,然后利用灰度特征表征采样粒子观测向量,最后构造基于观测向量的稀疏表示模型来进行目标追踪。与传统稀疏表示模型不同,该文提出一个基于典型相关性分析的稀疏表示模型,此模型首先使用两种特征来表征粒子观测向量,然后对两种观测向量的子空间投影结果进行稀疏建模。所构建的模型可通过在子空间中探究特征间的相关性来实现不同特征的互补融合,提升稀疏表示模型在复杂监控环境下的鲁棒性。     

EM-HMM多频率线跟踪算法

基于隐马尔可夫模型的多频率线跟踪算法 ,能在很低的SNR环境下工作 ,但量化误差较大 ,和计算量大 .本文提出另一种选择量测向量和计算量测概率的方法 ,创造条件减小量化误差 .又经简单论证 ,将EM算法和HMM用于多频率线跟踪 ,严格地 (而不是启发式地 )得到EM HMM算法 ,可以极大地减少计算量 .本文又提出获得初始估计以启动EM HMM算法的二种方法 .仿真计算表明 ,所提的算法是有效的     

稳健的单站无源目标跟踪算法研究

无源定位与跟踪系统中面临着可观测性弱、初始误差大的问题,因此寻找一种稳健快速的跟踪算法显得尤为关键。本文在对现有跟踪算法进行分析和比较的基础上,提出一种IUKF(Improved Unscented Kalman Filter)算法,它通过对传统的UKF算法进行修正,改善了对状态滤波值和协方差的估计。与现有算法(如EKF,UKF)相比,新算法不仅适应能力强、稳定性高,而且收敛速度快、跟踪误差小,是一种稳健的无源目标跟踪算法,数值仿真和试验结果表明了算法的正确性和有效性。     

基于级联滤波的多AUV协同定位算法

针对交替领航下的多自主水下航行器(AUV)协同定位系统,利用级联滤波架构设计了一种多 AUV 协同定位自适应滤波算法,通过将协同定位系统中的跟随 AUV 状态估计分解为航向估计与位置估计两部分,克服了在仅有部分距离量测下航向角估计可观测性差的影响,减小了距离量测误差模型不确定所引起的定位误差波动。同时,引入算法复杂度评价标准对所设计的级联自适应滤波算法进行分析。仿真结果表明,该算法具有较高的定位精度,可有效降低计算复杂度。     

High-Gain Observers With Sliding Mode for State and Unknown Input Estimations

To handle the state estimation of a nonlinear system perturbed by a scalar disturbance distributed by a known nonlinear vector, a sliding-mode term is incorporated into the nonlinear high-gain observer (HGO) to realize a robust HGO. By imposing a structural assumption on the unknown input distribution vector, the observability of the disturbance with respect to the output is safeguarded, and the disturbance can be estimated from the sliding surface. Under a Lipschitz condition for the nonlinear part, the nonlinear observers are designed under the structural assumption that the system is observable with respect to any input. In the sliding mode, the disturbance under an equivalent control becomes an increment of Lipschitzian function, and the convergence of the estimation error dynamics can be proven similar to the analysis of HGOs. The proposed technique can be applied for fault detection and isolation. The simulation results for the bioreactor application demonstrate the effectiveness of the proposed method.      

Super-exponential blind adaptive beamforming

The objective of the beamforming with the exploitation of a sensor array is to enhance the signals of the sources from desired directions, suppress the noises and the interfering signals from other directions, and/or simultaneously provide the localization of the associated sources. In this paper, we present a higher order cumulant-based beamforming algorithm, namely, the super-exponential blind adaptive beamforming algorithm, which is extended from the super-exponential algorithm (SEA) and the inverse filter criteria (IFC). While both SEA and IFC assume noise-free conditions, this requirement is no longer needed, and all the noise components are taken into account in the proposed algorithm. Two special conditions are derived under which the proposed blind beamforming algorithm achieves the performance of the corresponding optimal nonblind beamformer in the sense of minimum mean square error (MMSE). Simulation results show that the proposed algorithm is effective and robust to diverse initial weight vectors; its performance with the use of the fourth-order cumulants is close to that of the nonblind optimal MMSE beamformer.     

边缘化迭代容积卡尔曼滤波的单站无源定位算法

针对单站无源定位可观测性弱、观测噪声大而导致的定位精度低、收敛速度慢等问题,提出了一种边缘化迭代容积卡尔曼滤波算法。该算法采用基于似然增加的迭代策略,不需要设置判决门限,且保证了算法的全局收敛性。同时,其充分考虑状态向量与观测噪声之间的互协方差,将状态向量扩维,构造条件线性模型并进行边缘化滤波,不仅提高了算法的定位精度以及收敛速度,还减少了扩维后所需的采样点,提高了算法的运算效率。仿真结果表明,新算法改善了单站无源定位的定位精度以及收敛速度。      

基于成像激光雷达的地形辅助导航研究

成像激光雷达通过扫描可以测量高精度的地形数据,基于成像激光雷达的地形辅助导航系统是纠正惯导误差的有效手段.根据激光雷达测距模型提出了基于成像激光雷达的地形辅助导航系统,将激光雷达看作一个多维距离传感器阵列,利用卡尔曼滤波器迭代估计系统的状态误差,从而纠正惯导累积误差.根据激光雷达测距关系推导了系统的扩展卡尔曼滤波方程,并对成像激光雷达多维测量数据采用最小均方误差准则进行融合,融合滤波器组合了多个测量数据的信息,有效克服了测量噪声和数据丢失对单个滤波器的影响,从而提高了导航性能.然后利用局部可观测性对系统的性能进行了分析,并对提出的算法作了大量的仿真实验进行验证.     

基于非理想CSI的下行MU-MIMO鲁棒波束成形

在下行多用户多入多出（MU-MIMO）系统中,基站（BS）所获得的非理想信道状态信息（CSI）会导致频分双工（FDD）系统预编码性能变差.现有的MU-MIMO鲁棒预编码算法虽然可以对抗非理想CSI所导致的系统性能损失,但其只考虑其中一种或两种信道误差的鲁棒性,因此系统性能提升有限.本文通过建立包含信道估计误差、量化误差和延时误差的联合信道误差模型,推导出具有集中式特性的基于最小均方误差（MMSE）的鲁棒波束成形矩阵的闭式解;随后将这种信道条件应用到分布式通信系统,并推导出具有分布式特性的基于信号泄露的MMSE的鲁棒波束成形矩阵的闭式解.数值分析表明,本文所提的集中式和分布式MU-MIMO波束成形算法,与只考虑量化误差的鲁棒MMSE算法相比,具有更优的系统和速率与误码率,补偿了上述三种信道误差所导致的预编码性能损失.     

A Study of the Technique of Linear Multiuser

The technique of linear multiuser detection in DS-CDMA systems is studied in this paper. The purpose is to find a receiver structure with good performance and moderate complexity, so that the receiver can efficiently suppress multiple-access interference(MAI) and multipath interference and has good near-far resistant ability, which may improve the system's capability while reducing the requirement for power control. The main work of the dissertation can be summarized as follows: the performance of MMSE multiuser detector in synchronous/asynchronous DS-CDMA systems over different channels is analyzed in chapter 2 of the dissertation. Using matrix method, we analyze the relation between performance measurement and spreading code correlation matrix, Signal-Interference-Ratio(SIR) and near-far factor, and prove that the performance of MMSE detector is better than that of the decorrelating detector. For fading channel, we analyze the performance of MMSE detector in DS-SS system firstly. Results show that the detector can efficiently suppress multipath interference. Extending to synchronous/asynchronous DS-CDMA systems over fading channels, we propose a simple linear detector structure that accomplishes despreading, detection and combining. Thus, the receiver is easy for implementation. Based on the proposed notion of combined spreading codes, we prove that the synchronous/asynchronous CDMA system is equivalent to the synchronous CDMA system over AWGN channel with double users. Therefore, the MMSE detector can efficiently suppress MAI and multipath interference in steady state, and has good near-far resistant ability. In chapter 3, we study the adaptive algorithm based on MMSE criterion. Firstly, the approach to the blind algorithm based on subspace is analyzed. We improve the algorithm in the part of channel estimation, which decreases the computational complexity while guaranteeing the performance. Meanwhile, we point out that CMOE-RLS algorithm is essentially an algorithm based on subspace approach. Also, it is shown from simulation that PASTd subspace tracking algorithm is not applicable for multiuser detection. Secondly, we propose an adaptive algorithm based on pilot channel, called PCA/PCRA. The algorithm does not require channel estimation, and has a rapid convergence rate. The steady state performance can be achieved by increasing the transmitting power in pilot channel. Computational complexity is only O(N2). Therefore, PCA/PCRA is suitable for the engineering application. The cost is that a pilot channel is needed for each user in the system. In chapter 4, constant algorithms for multiuser detection are studied. Firstly, we analyze the capture performance of CMA, and point out there exist many local stationary points. Initializations to guarantee CMA converges to the desired point are discussed. Results show that the convergence of CMA is decided by constant, step-size, spreading code correlation matrix and near-far factor. Secondly, we propose the constrained constant algorithm (C-CMA) for multiuser detection. It is shown that when the constant is greater than the triple power of the desired user, C-CMA globally converges to the desired point. Simulations illustrate that C-CMA has a rapid convergence rate and the steady state performance is good. However, great step-size can also reult in dispersion for the algorithm. Since C-CMA is a variable step-size CMOE-LMS algorithm, we propose a variable step-size constraint algorithm (VSCA). VSCA has the advantages of both CMOE-LMS and C-CMA such as robust, rapid convergence rate and good steady state performance. Thus, VSCA is suitable for engineering application. But the improper selection of step-size coefficients may degrade performance seriously. The computational complexity of the above constant algorithms is only O(N). In Section 5, the cyclostationarity of spreading signals is analyzed in the first part. We prove that spreading signals are ergodic cyclostationary signals with a cyclic period that is equal to the period of spreading code. Methods for processing cyclostationary signals are then given. It is shown that this method can mitigate the interference from a stationary noise for channel estimation. But the computational complexity for cyclostationary correlation is high, which prevents its application in implementation. In the second part, we discuss the application of oversampling technique in spreading communication systems. Although the oversampling can improve the performance of the linear multiuser detector, the improvement is trivial. On the contrary, oversampling increases the computational complexity of the weight vector greatly, which prevents its application in implementation. Additionally, we prove that FSE plus despreading or despreading pus FSE is equivalent to the linear detector with different lengths of delay line. However, the two kinds of structure have lower computational complexity.     

Parameter and State Estimation Algorithm for a State Space Model with a One-unit State Delay

This paper derives a state estimation based parameter identification algorithm for state space systems with a one-unit state delay. We derive the identification model of an observability canonical state space system with a one-unit state delay. The key is to replace the unknown states in the parameter estimation algorithm with their state estimates and to identify the parameters of the state space models. Finally, two illustrative examples are given to show the effectiveness of the proposed algorithm.