多速率DS-CDMA系统中基于Kalman滤波的盲多用户检测

在讨论可变扩频增益多速率直接序列码分多址(DS-CDMA)系统信号模型的同时,为了保证该检测器能适用于多径衰落信道环境,提出了基于Kalman滤波的盲多速率多用户检测器,并给出了Kalman滤波算法的实现步骤与最大值合并过程.仿真结果表明,与现有的一些盲自适应多用户检测器相比,本文提出的检测器在多速率环境下具有更为好的抗多址干扰、抗多径干扰能力,且能更快地收敛.     

ICA自适应核估计在多用户检测中的应用

将自适应核估计的ICA算法应用于DS-CDMA系统的多用户检测中,在没有关于混叠信号的分布假设时,根据信号的统计特性自动估计评价函数,采用自适应核密度估计ICA算法检测器的输出初始化独立分量分析的迭代,对任意混叠信号进行盲分离。通过与传统匹配滤波检测和ICA后处理匹配滤波检测进行仿真比对,证明了该算法在DS-CDMA多用户检测中的可行性和优越性。     

基于自适应拟牛顿子空间跟踪的盲多用户检测算法

该文提出了一种新的基于自适应拟牛顿子空间跟踪盲多用户检测算法,这种算法的计算复杂度为O(n2r),其中n为扩频因子,r为用户数。仿真分析表明:该文提出的方法的算法稳态性能及收敛速度比其他基于子空间的盲多用户检测算法都要好,因而是一种优秀的盲自适应多用户检测算法,具有一定的应用和研究价值。     

基于子空间跟踪的盲自适应多用户检测技术研究

研究了一种基于子空间跟踪的盲自适应多用户检测的算法,提出了一种改进的PASTd算法和卡尔曼滤波算法相结合的盲自适应多用户检测算法。把改进的PASTd算法和卡尔曼滤波器结合,提出了一种新的盲自适应多用户检测器并且能够实现在高信噪比和远近效应情况下得到稳定的跟踪性能。仿真结果表明,该检测器可以迅速地跟踪维数变化了的信号子空间,从而提高检测器的性能,实现稳定的跟踪并使输出具有良好的信干比。提出的盲自适应多用户检测算法虽然在计算量上较LMS算法略有提高,但其收敛性能却优于后者,与RLS算法相比,提出的算法具有更低的计算复杂度和更优的收敛性能。     

基于强跟踪滤波器的盲自适应多用户检测

盲自适应多用户检测是码分多址(CDMA)系统中抑制多址干扰的一项重要技术.为克服传统的基于最小化输出能量准则的盲自适应多用户检测技术收敛速度慢的这一缺点,利用一种新近提出的多用户接收器的典范表示方式,构造了多用户检测系统的状态空间模型,再利用强跟踪滤波器(STF)理论,设计了一种新的盲自适应多用户检测方法.仿真结果表明,在异步CDMA系统中,与基于LMS算法、RLS算法及Kalman算法的盲自适应多用户检测相比, 方法具有收敛速度快速稳定, 跟踪性能好等优点.因此,基于强跟踪滤波器的盲自适应多用户检测是有效的.     

基于盲波束成形的异步CDMA信号伪码估计

在分析阵列天线接收的异步CDMA信号的基础上,利用信号子空间与用户特征序列的关系,提出一种新的基于盲波束成形的伪码序列盲估计算法,能在波达方向未知的情况下,估计各个用户的扩频序列。算法克服了子空间盲波束成形算法信源个数必须小于阵元个数且信源波达方向不能靠近的缺点。同时提出一种基于盲波束形成的多用户检测算法,在低信噪比条件下,性能相比于单天线多用户检测性能有很大提高。     

基于改进卡尔曼滤波的水声通信盲自适应多用户检测算法

针对水声通信网络中遇到的多用户检测中目标用户的多址干扰等检测问题,提出了基于改进Kalman算法的盲自适应多用户检测算法,解决了多用户检测中的多址干扰对水声通信信道用户变动时的干扰抑制问题;仿真分析分别针对同步多用户、异步多用户通信过程,对比了传统Kalman算法及改进的Kalman算法的性能差异,通过仿真对比表明,改进后的Kalman检算法不需要训练序列即可以实现同步和异步通信状态下的多水声目标用户的盲自适应检测,改进后的算法目标检测的信干比比传统算法最大可提高6 dB;新算法对于水下多用户检测、区分,准确、稳定的实现基于CDMA协议的快速水声通信具有重要意义。     

一种改进的基于卡尔曼滤波的盲多用户检测算法

针对卡尔曼滤波盲多用户检测算法在多径信道存在波形失配的情况下性能迅速下降的问题,引入子空同概念对原状态空间模型进行了改进,进而得到了改进的盲多用户检测算法.该算法将检测器模型化为信号子空间的一个向量,采用卡尔曼滤波器自适应地获得系统参数.仿真实验表明,与原算法相比,该算法具有更低的计算复杂度和更快的收敛速度.当发生波形失配使信号受到严重削减时,该算法仍然表现出较好的性能.     

MC-CDMA系统中的自适应半盲多用户检测算法

研究多载波CDMA(MC-CDMA)上行半盲多用户检测技术,对基于Chebyshev逼近算法实现的最小输出能量(MOE)盲多用户检测进行改进,提出MC-CDMA系统下一种基于子空间跟踪和Chebyshev逼近的自适应半盲多用户检测算法。该算法基于MOE线性检测器原理,充分利用小区内所有用户的扩频码,设计了一种基于MOE准则的半盲检测器,很好地消除了多址干扰。为了减少计算复杂度,将修正的PASTd算法应用于Chebyshev逼近算法估计MOE半盲检测器的最优权向量。该算法计算复杂度低,具有较好的抗多址干扰性能和检测性能。仿真结果验证了该算法的可行性和优越性。     

消除噪声的ICA盲多用户检测

针对已有的基于独立分量分析（Independent Component Analysis,ICA）的盲多用户检测中为了便于分析经常将噪声信号忽略,从而造成系统检测性能下降的问题。提出了基于ICA的盲多用户检测的改进算法,该算法不仅能抑制远近效应和多址干扰,而且利用信号子空间和有效无偏估计原理可最大限度地消除被忽略的噪声,提高了系统的容量。通过Matlab实验仿真,验证了该算法的有效性和实用性。     

基于自适应卡尔曼滤波器的MEMS陀螺零点漂移的研究

随机噪声是影响MEMS陀螺精度的一个重要因素。本文基于时间序列分析方法建立MEMS陀螺的随机漂移AR模型后,使用自适应卡尔曼滤波器对信号进行滤波。通过比较陀螺原始信号和自适应卡尔曼滤波后的信号,可以得出结论：自适应卡尔曼滤波器在处理MEMS陀螺零点漂移中具有良好的滤波效果。     

Adaptive array detection in noise and completely unknown jamming

The presence of jamming usually degrades the detection performance of a detector. Moreover, sufficient information about the jamming may be difficult to be obtained. To overcome the problem of adaptive array signal detection in noise and completely unknown jamming, we temporarily assume the jamming belongs to a subspace which is orthogonal to the signal steering vector in the stage of detector design. Consequently, by resorting to the criteria of generalized likelihood ratio test (GLRT) and Wald test, we propose two adaptive detectors, which can achieve signal detection and jamming suppression. It is shown, by Monte Carlo simulations, that the two proposed adaptive detectors have improved detection performance over existing ones.     

基于改进子空间的盲MMSE多用户检测算法

算法收敛性与计算复杂度之间的矛盾是目前基于子空间的多用户检测技术难以投入实际应用的主要问题。将内部模型法则(IMP)与PAST算法相结合,提出了一种改进的子空间跟踪算法——newPAST-IMP算法,并应用于盲MMSE检测器中。该方法收敛速度快,跟踪性能好且计算复杂度较低。仿真表明,该检测器具有优异的抗多址、多径干扰以及抗远近效应的能力。     

A robust correntropy based subspace tracking algorithm in impulsive noise environments

The maximum correntropy criterion (MCC) demonstrates the inherent robustness to outliers in adaptive filtering. By employing the MCC based cost function in projection approximation subspace tracking (PAST) algorithm, the MCC-PAST algorithm is deduced and utilized for the subspace tracking under impulsive noise environments. To handle the fast varying subspaces circumstances, the variable forgetting factor (VFF) technique is developed and incorporated into the MCC-PAST algorithm. To assess the robustness of the proposed MCC-PAST with VFF algorithm, SαS processes are employed to comprehensively model different scenarios of impulsive noises. The simulation results show the proposed MCC-PAST algorithm with VFF performs better than the other two PAST algorithms developed for subspace tracking in impulsive noise environments, namely, the robust PAST algorithm and the robust Kalman filter based algorithm with variable number of measurements (KFVNM), especially when the noise is extremely impulsive or the GSNR (generalized signal to noise ratio) is relatively low.     

Adaptive GLR-, Rao- and Wald-based CFAR detectors for a subspace signal embedded in structured Gaussian interference

The problem of detecting a subspace signal embedded in subspace Gaussian interference and thermal noise is studied in this paper. In this problem, both the signal-independent and signal-dependent interferences are assumed to be present, therefore the overall interference subspace covers the signal subspace. The approach of this paper extends previous works involving either of those two kinds of interferences. A set of secondary data containing only interference plus noise is employed to estimate the interference covariance matrix and the noise power. Three new detectors are designed via the generalized likelihood ratio (GLR), Rao and Wald tests, respectively. Their probabilities of false alarms (PFAs) and detections are analytically derived. The PFAs show that the new detectors have the constant false alarm rate (CFAR) property against the interference and noise. Numerical results show that the new detectors outperform their counterparts for the studied problem. Furthermore, the new detectors are less sensitive to the secondary data size and to the mismatched subspace signal than some other detectors, such as the GLR detector (GLRD), the adaptive matched filter (AMF), the adaptive subspace detector (ASD), etc.     

基于改进卡尔曼滤波算法的盲多用户检测器

基于盲检测器的新典范表示形式,提出了一种多径信道下CDMA系统的盲多用户检测器。该检测器使用了一种改进的自适应卡尔曼滤波算法,它在进行状态滤波的同时,在线估计时变的未知噪声统计特性,提高了算法的滤波精度,确保了算法收敛于期望用户。仿真表明,对比原有的随机梯度（LMS）算法,该算法的收敛、跟踪性能更好,且误码率减小了18.7%。     

Adaptive and learning algorithms for seismic detection of personnel

This correspondence is concerned with adaptive digital processing to extract impulse-like signal features from the correlated background noise for detection of intruders with the seismic sensor data. Both the adaptive digital filtering and the adaptive Kalman filtering methods are developed and shown to perform nearly the same for a short data segment. For continued processing of a long duration seismic record, the adaptive Kalman filtering considered has better capability to learn the nonstationary data characteristics than the considered adaptive filtering and to adaptively remove the background noise. Detailed experimental results are presented. Other considerations such as the hardware implementation and the relationships among the parameters are also examined.     

Adaptive subspace detection of range-distributed target in compound-Gaussian clutter

This paper deals with high-resolution radar (HRR) adaptive detection of range-distributed target embedded in compound-Gaussian clutter which is modeled as a spherically invariant random process (SIRP). Using multiple dominant scattering (MDS) model of range-distributed target, we justify that range-distributed target can be modeled as a subspace random signal. The unknown deterministic parameters are replaced by their ML estimates and then the nonadaptive detector is proposed. A closed-form expression for the probability of false alarm of the nonadaptive detector is derived and it ensures CFAR property with respect to the unknown statistics of the clutter texture component. Moreover, an adaptive detector is obtained relying on a two-step GLRT-based design procedure. Performances of these proposed detectors are assessed through Monte Carlo simulations and are shown to have better detection performance compared with existing similar detector.     

A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

This paper explores multiple model adaptive estimation (MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter-multiple model adaptive estimation unscented Kalman filter (MMAE-UKF) rather than conventional Kalman filter methods, like the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters, which the improved filtering method can overcome. Meanwhile, this algorithm is used for integrated navigation system of strapdown inertial navigation system (SINS) and celestial navigation system (CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.      

Real-time motion planning of an autonomous mobile manipulator using a fuzzy adaptive Kalman filter

This paper presents the real-time motion planning i.e., map building and path planning of an autonomous mobile manipulator capable of scanning natural terrain using a detector e.g., a landmine detector. Map building generates a terrain map using the measurements of laser and ultrasonic rangefinders, and path planning uses the map to define an obstacle-free path for the detector. Map building involves sensor fusion to tackle the uncertainties associated with range measurement. Fusion takes place in a hierarchical filtering process that updates the map in real time and also optimizes the scanning process based on the terrain type. The filtering process includes a proposed fuzzy adaptive Kalman filter in which the gain of the filter is adapted using a fuzzy model that characterizes the terrain. The efficiency of the proposed map building and path planning methods has been verified by experiments on a prototype mine detector robot.