基于Kalman滤波的陀螺降噪技术研究

在自适应滑动平均技术基础上针对光纤陀螺的动静态输出设计了混合Kalman滤波算法,首先用窗口分割原始数据,接着用自适应滑动平均法检测窗口内的间断点;没有间断点的窗口内部,使用修正值为M1的Kalman滤波进行信号降噪;窗口内部具有间断点的使用修正值为M2的Kalman滤波进行信号降噪.使用DWT算法、普通Kalman滤波法、混合Kalman滤波对陀螺的动态和静态输出信号进行降噪,仿真结果表明只有混合Kalman滤波同时适用于陀螺的动静态输出.     

复杂运动状态的视频目标跟踪自适应切换算法

针对实际环境中运动目标的状态转移模型以及随机噪声分布存在的不确定性,提出了一种适用于复杂运动状态的视频目标跟踪算法。该算法同时结合了Kalman滤波(KF)实时性好的优点,以及粒子滤波(PF)能同时处理非线性、非高斯滤波问题的优点,通过对Kalman滤波性能进行分析,定义了评价滤波性能优劣的参数并作为判断条件,实现了不同运动状态下Kalman滤波和粒子滤波自适应切换。通过实验表明该方法在目标运动状态发生显著变化时仍能够实现稳定跟踪,同时具有较高的跟踪精度。     

基于无极卡尔曼滤波算法的雅可比矩阵估计

在基于图像的机器人视觉伺服中,采用在线估计图像雅可比的方法,不需事先知道系统的精确模型,可以避免复杂的系统标定过程。为了有效改善图像雅可比矩阵的在线估计精度,进而提高机器人的跟踪精度,针对机器人跟踪运动目标的应用背景,提出了利用无极卡尔曼滤波算法在线估计总雅可比矩阵。在二自由度的机器人视觉伺服仿真平台上,分别用卡尔曼滤波器（KF）、粒子滤波器（PF）和无极卡尔曼滤波器（UKF）三种算法进行总雅可比矩阵的在线估计。实验结果证明,使用UKF算法的跟踪精度优于其他两种算法,时间耗费仅次于KF算法。     

Adjoint EKF learning in recurrent neural networks for nonlinear active noise control

In this paper, active noise control using recurrent neural networks is addressed. A new learning algorithm for recurrent neural networks based on Adjoint Extended Kalman Filter is developed for active noise control. The overall control structure for active noise control is constructed using two recurrent neural networks: the first neural network is used to model secondary path of active noise control while the second one is employed to generate control signal. Real-time experiment of the proposed algorithm using digital signal processor is carried-out to show the effectiveness of the method.     

改进的交互式多模型跟踪算法

针对传统交互式多模型算法实行正则滤波的单一化缺点,提出一种改进的跟踪算法。利用卡尔曼滤波匹配系统线性部分,粒子滤波匹配非线性部分,根据匹配深度判断目标遮挡程度,当目标被严重遮挡时,采用迭代的多级粒子滤波方法进行重采样,并结合卡尔曼滤波更新模型概率。实验结果表明,该算法实时性强,能提高模型滤波速度和目标状态的估计精度,缩短计算时间,解决跟踪过程中的遮挡问题。     

小波滤波器低功耗并行的VLSI结构设计

提出一种基于行和提升算法,实现JPEG2000编码系统中的小波正反变换(discretewavelettransform)的低功耗、并行的VLSI结构设计方法·利用该方法所得结构一次处理两行数据,分时复用行处理器,使行处理器内以及行、列处理器实现并行处理,且最小化行缓存·对称扩展通过嵌入式电路实现,整个结构采用流水线设计方法优化,加快了变换速度,增加了硬件资源利用率,降低了功耗,效率几乎达到100%·小波滤波器正反变换结构已经经过FPGA验证,可作为单独的IP核应用于正在开发的JPEG2000图像编解码芯片中·     

基于加权最小二乘的卡尔曼滤波算法

为了将卡尔曼滤波(KF)应用于非线性系统中,利用了离散采样点将非线性模型线性化.通过加权最小二乘原理.得到近似的线性化模型,再将KF算法应用于这个线性模型中.结果表明,加权最小二乘与KF结合的方法在非线性模型中的计算结果同扩展卡尔曼滤波(EKF)算法接近,且不需要EKF那样求偏导就能很容易地应用到非线性系统中.这种方法实现容易,预测可靠,具有实际应用的价值.     

FPGA–DSP co-processing for feature tracking in smart video sensors

Motion estimation in videos is a computationally intensive process. A popular strategy for dealing with such a high processing load is to accelerate algorithms with dedicated hardware such as graphic processor units (GPU), field programmable gate arrays (FPGA), and digital signal processors (DSP). Previous approaches addressed the problem using accelerators together with a general purpose processor, such as acorn RISC machines (ARM). In this work, we present a co-processing architecture using FPGA and DSP. A portable platform for motion estimation based on sparse feature point detection and tracking is developed for real-time embedded systems and smart video sensors applications. A Harris corner detection IP core is designed with a customized fine grain pipeline on a Virtex-4 FPGA. The detected feature points are then tracked using the Lucas–Kanade algorithm in a DSP that acts as a co-processor for the FPGA. The hybrid system offers a throughput of 160 frames per second (fps) for VGA image resolution. We have also tested the benefits of our proposed solution (FPGA + DSP) in comparison with two other traditional architectures and co-processing strategies: hybrid ARM + DSP and DSP only. The proposed FPGA + DSP system offers a speedup of about 20 times and 3 times over ARM + DSP and DSP only configurations, respectively. A comparison of the Harris feature detection algorithm performance between different embedded processors (DSP, ARM, and FPGA) reveals that the DSP offers the best performance when scaling up from QVGA to VGA resolutions.     

FPGA-based IP cores implementation for face recognition using dynamic partial reconfiguration

This paper presents a combination of novel feature vectors construction approach for face recognition using discrete wavelet transform (DWT) and field programmable gate array (FPGA)-based intellectual property (IP) core implementation of transform block in face recognition systems. Initially, four experiments have been conducted including the DWT feature selection and filter choice, features optimisation by coefficient selections and feature threshold. To examine the most suitable method of feature extraction, different wavelet quadrant and scales have been evaluated, and it is followed with an evaluation of different wavelet filter choices and their impact on recognition accuracy. In this study, an approach for face recognition based on coefficient selection for DWT is presented, and the significant of DWT coefficient threshold selection is also analysed. For the hardware implementation, two architectures for two-dimensional (2-D) Haar wavelet transform (HWT) IP core with transpose-based computation and dynamic partial reconfiguration (DPR) have been synthesised using VHDL and implemented on Xilinx Virtex-5 FPGAs. Experimental results and comparisons between different configurations using partial and non-partial reconfiguration processes and a detailed performance analysis of the area, power consumption and maximum frequency are also discussed in this paper.     

基于均方根容积卡尔曼的δ-GLMB多目标跟踪算法

在非线性高杂波密度场景下,高斯混合(Gaussian Mixture,GM)实现的δ-广义标签多伯努利滤波器(δ-Generalized Labeled Multi-Bernoulli Filter,δ-GLMB)难以准确地估计目标数目及运动状态。针对这一问题,提出基于均方根容积卡尔曼滤波(Square-rooted Cubature Kalman Filter,SCKF)的δ-GLMB高斯混合实现算法。基于三阶球面-径向容积准则选取一组等权的容积点集,对GM-δ-GLMB滤波器的伯努利分量传递过程中的高斯参量进行预测及更新,实现非线性模型系统下的目标跟踪。仿真结果表明,与现有的δ-GLMB滤波器的扩展卡尔曼滤波(Extended Kalman Filter,EKF)高斯混合实现及无迹卡尔曼滤波(Unscented Kalman Filter,UKF)高斯混合实现相比,该算法可提高非线性高杂波密度环境下的目标跟踪精度。     

嵌入式人脸检测系统设计

介绍了以计算机视觉为背景,基于FPGA实现嵌入式人脸检测系统的设计。系统设计过程包括配置MicroBlaze软核处理器、裁剪μClinux内核和应用程序设计3部分。以MicroBlaze软核为处理核心,通过片内添加外设接口和IP在Virtex-ⅡPro开发板上搭建硬件平台。以μClinux内核为软件平台,通过优化OpenCV提供的源代码设计人脸检测应用程序。     

伪卫星时钟同步算法的研究

在伪卫星自主组网定位的时钟同步问题中,卡尔曼滤波(Kalman Filter,KF)算法因迭代时间短而被广泛应用,但是其精度不高.相反地,粒子滤波(Particle Filter,PF)算法精度高,但是其迭代时间长.在此基础上,提出一种混合优化算法(Hybrid Optimizing Algorithm,HOA),该算...     

适用处理非高斯观测噪声的强跟踪卡尔曼滤波器

在高斯噪声条件下,卡尔曼滤波器(KF)能够获得系统状态的一致最小方差线性无偏估计.但当噪声非高斯,KF性能将严重下降.观测噪声非高斯现象在深空探测自主导航中经常遇到,然而现有模型可能存在着精度不高、稳定性不强或者计算复杂度较高的缺点.针对这种现状,本文在传统强跟踪卡尔曼滤波器(STKF)中新息正交原则的基础上,推导了适用处理非高斯观测噪声的强跟踪卡尔曼滤波器(STKFNO),并将其嵌入到无迹卡尔曼滤波(UKF)框架下形成适用处理非线性系统非高斯观测噪声的强跟踪无迹卡尔曼滤波器(STUKFNO).所提出的算法被应用到深空光学自主导航系统中,仿真结果表明所提出的算法能够较好地应对观测噪声的非高斯性.     

小波变换的频响特性及其在语音去噪中的应用

讨论小波变换在实际语音信号去噪处理中应用。由于语音信号的复杂性 ,信号本身含有奇异性 ,因此不能单一使用阈值去噪法。文中定义了小波变换频响特性 ,并利用它重构低尺度参数上的小波变换模极大 ,达到去噪目的。实例证明它的有效性     

一种新的高动态GPS载波跟踪算法

提出了基于无迹粒子滤波（UPF）算法的高动态GPS载波跟踪环路,仿真分析了该方案在高斯噪声和非高斯噪声环境下对高动态GPS信号的跟踪性能,并与分别基于扩展卡尔曼滤波（EKF）、无迹卡尔曼滤波（UKF）、粒子滤波（PF）及扩展卡尔曼粒子滤波（EPF）这四种算法的载波跟踪环路进行了性能对比。仿真结果表明,基于UPF估计器的载波跟踪环路在高动态、弱信号以及非高斯噪声环境下具有优越的跟踪性能,既可以提高跟踪精度,又解决了非高斯噪声干扰问题。通过模拟实验验证了该方案的有效性。     

在FPGA中实现嵌入式TCP/IP通信协议栈

研究了嵌入式TCP/IP通信协议栈在Xilinx FPGA上的实现,介绍了其软硬件的系统组成和原理,提出一种实时操作系统上TCP/IP协议栈的高效工作模式,并在Virtex-5FPGA上移植成功。通过建立测试平台进行数据传输测试,证明其具有稳定、高效的通信性能,为嵌入式设备开发提供了新的思路。     

嵌入式网络技术在接收机中的应用

设计了一款应用于接收机通信的嵌入、式网络接口,并在数字信号处理器上实现了标准的传输控制(TCP/IP)协议栈.从关键芯片选取、接口及中断设计三方面对网口的硬件设计进行了阐述,介绍了TCP/IP协议栈中各模块的功能,对实现难度最大的TCP模块进行了论述,说明了软件模块在任务调度和模块化设计方面的考虑,最后以对该网口的测试...     

基于Kalman点匹配估计的运动目标跟踪

针对目前的角点匹配跟踪实时性差和抗遮挡、相似性物体等环境因素能力差的缺点,提出了一种基于Kalman点匹配估计的目标跟踪方法。通过在Kalman滤波粗定位的基础上,提取具有一定的抗几何缩放能力的多尺度Harris角点,对获得的其响应函数值进行加权以及目标区域进行恰当的分块,然后,将各块中的响应函数值求取平均值组成特征向量,在搜索域内进行点匹配跟踪。实验结果表明,该算法计算效率有很大的提高,能够用到实时的目标跟踪系统中,且对环境因素的影响有一定的鲁棒性。     

Target tracking using Interactive Multiple Model for Wireless Sensor Network

Target tracking in a Wireless Sensor Network (WSN) environment is a challenging research problem. Interactive Multiple Model (IMM) is a popular scheme for accurate target tracking. The existing target tracking scheme used in WSN employs Kalman Filter (KF) which fails to track the target accurately due to non availability of target data at regular intervals and missing of packets. Though existing KF based tracking in WSN scheme detects the target, it fails to identify the target. To overcome these problems, this paper proposes a IMM based Target Tracking in WSN named ITTWSN that uses multiple models (velocity and acceleration) to handle both maneuvering and non maneuvering targets and multiple sensors to detect and identify the targets. The performance of the proposed ITTWSN is compared with the KF scheme and it is found that the accuracy of the proposed ITTWSN is better than the existing KF based approach.     

The eXogenous Kalman Filter (XKF)

It is well known that the time-varying Kalman Filter (KF) is globally exponentially stable and optimal in the sense of minimum variance under some conditions. However, nonlinear approximations such as the extended KF linearises the system about the estimated state trajectories, leading in general to loss of both global stability and optimality. Nonlinear observers tend to have strong, often global, stability properties. They are, however, often designed without optimality objectives considering the presence of unknown measurement errors and process disturbances. We study the cascade of a global nonlinear observer with the linearised KF, where the estimate from the nonlinear observer is an exogenous signal only used for generating a linearised model to the KF. It is shown that the two-stage nonlinear estimator inherits the global stability property of the nonlinear observer, and simulations indicate that local optimality properties similar to a perfectly linearised KF can be achieved. This two-stage estimator is called an eXogeneous KF (XKF).