基于概率假设密度滤波平滑器的检测前跟踪算法

基于概率假设密度滤波(PHD)的检测前跟踪(TBD)技术可以有效解决未知弱小多目标检测问题。PHD-TBD算法粒子权重计算受量测噪声影响明显,导致目标数估计存在起伏现象,制约了PHD-TBD算法性能。对PHD-TBD技术进行研究,引进概率假设密度滤波平滑器,提出基于平滑的PHD-TBD算法。该算法对当前帧目标数估计时,综合利用前向递推和后向平滑结果对粒子权重进行更新,在一定程度上克服了随机量测噪声的影响。通过仿真验证,该算法能够有效发现目标,准确估计目标数目和位置,性能有较大提高。     

区间衍生粒子滤波器

针对非线性、非高斯环境下具有不确定动态模型参数的系统状态估计问题,提出了一种新颖的区间衍生粒子滤波算法.该算法利用区间滤波生成的重要性密度函数,在系统状态转移概率密度的基础上,融入最新的系统观测数据,从而提高了对系统状态后验概率的逼近程度.为了进一步提高算法的实时性,提出一种类似光子衍射的粒子衍生过程,进而缓解了滤波精度与运算量之间的矛盾.通过陀螺/星敏感器组合定姿问题验证了该算法的有效性和鲁棒性.     

混合退火粒子滤波器

针对非线性、非高斯系统状态的在线估计问题，提出一种新的基于序贯重要性抽样的粒子滤波算法. 在滤波算法中，用状态参数分解和退火系数来产生重要性概率密度函数，此概率密度函数综合考虑了转移先验、似然、噪声的统计特性以及最新的观察数据，因此更接近于系统状态的后验概率. 理论分析与仿真实验表明该粒子滤波器的性能明显优于标准的粒子滤波器和扩展卡尔曼滤波器. 关键词： 非线性 非高斯 粒子滤波 序贯重要性抽样     

基于自适应EKF的弱GNSS信号跟踪算法

弱GNSS信号跟踪技术是卫星导航接收机关键技术之一,跟踪技术的好坏将直接影响卫星导航接收机在弱信号条件下的跟踪性能;在动态环境和先验信息不充分的情况下,由于扩展卡尔曼滤波(EKF)的固定设计使其不能满足要求,针对此不足引入一种自适应扩展卡尔曼滤波(AEKF)的信号跟踪算法;该自适应滤波算法能够实时监测残差或滤波器新息的动态变化,来修正观测噪声方差和状态噪声方差,以此调整滤波器增益,观测值和控制预测值在滤波结果中的权重;理论分析和结果表明,该算法能够充分利用观测信号的统计特性,克服了传统EKF算法不足,获得更好的跟踪性能。     

一种增强结构信息的图像平滑算法

基于非线性扩散滤波提出了可以同时对同质区域进行平滑和对结构信息进行增强的算法．该算法在同质区域采用各项同性滤波而在边缘附近采用各项异性扩散滤波，避免了图像平滑和增强结构信息之间的矛盾．在实现时，根据一致性程度选择扩散矩阵的扩散系数，可确保偏微分方程在离散化时的简洁性和一致性．数字实验结果表明，可以有效的平滑一致性低的区域并增强一致性高的结构．     

基于高斯混合概率假设滤波的水下目标跟踪算法

为了解决传统水下目标跟踪中目标数目估计不准确、状态估计误差增长过快的问题,提出了一种基于高斯混合概率假设滤波的水下目标跟踪算法。该算法基于双基地观测模型,采用高斯混合概率假设滤波算法处理方位和时延信息,利用粒子群算法处理多普勒频率获得矢量速度,进一步提升算法的跟踪精度。结果表明,该算法能完成在杂波环境下对目标的跟踪,相比传统的关联算法,能够有效地实现目标个数估计和抑制状态误差增长的目的。     

基于概率假设密度的无线传感器网络多目标跟踪算法

针对无线传感器网络多目标跟踪过程中杂波难以去除以及由数据关联复杂带来的计算复杂度高的问题,将概率假设密度滤波器应用于无线传感器网络,以更好地对多目标状态信息进行融合估计;首先,建立簇-树型无线传感器网络模型,并运用随机有限集理论对目标状态模型和传感器观测模型进行描述;然后,根据目标与节点之间的距离设置观测阈值,当传感器节点测量值小于观测阈值时,概率假设密度滤波器将实时对该组测量数据进行处理,从而实现传感器网络对目标状态的联合检测与跟踪;仿真结果表明,在无线传感器网络的多目标跟踪应用中,该算法比粒子滤波算法具有更高的跟踪效率和精度。     

眼底图像中视盘的平滑滤波与CV模型分割

可靠、有效的视盘分割对自动眼底病变诊断分析具有重要意义。提出了一种基于平滑滤波和CV模型的视盘分割算法。在利用投影法定位出视盘中心后,首先采用Gabor滤波技术提取并移除视盘局部区域的主血管,然后利用邻域信息通过插值运算填充被移除血管区域的像素,采用边缘保留平滑滤波对视盘区域进行平滑,最后利用基于区域信息的CV水平集模型分割出视盘的边界。该方法在MESSIDOR数据库上进行了测试,平均分割准确率为0.83,表现出了良好的分割性能。     

大失准角传递对准算法研究

经典Kalman滤波器不适用于大失准角情况下的传递对准,故只能采用非线性滤波器进行失准角的次优估计,其工程实现效果无法满足惯性姿态系统对姿态精度的要求。针对上述问题,提出了动态解析对准与传递对准相结合的大失准角传递对准算法,设计了"速度+角速度"匹配的传递对准滤波模型。该算法利用动态解析对准算法完成大失准角的初步补偿,解决传递对准非线性误差模型无法进行线性化的问题。基于Kalman滤波最优估计理论,设计FIR低通滤波器完成观测信息去噪滤波,实现姿态失准角的最优估计补偿,以达到惯性姿态系统大失准角条件下快速精确传递对准的目的。试验结果表明,在大失准角条件下,惯性姿态系统可在50s内完成方位精度为3′、水平姿态精度为1′的初始对准。     

基于混合粒子滤波的水下小目标跟踪

针对水下小目标粒子滤波估计过程中“粒子贫化”引起的估计性能下降,提出了混合粒子滤波算法。该算法在常规粒子滤波算法基础上,在每一步迭代估计过程中进行量测的再次随机采样,以丰富随机粒子多样性,缓解水下小目标状态估计过程中的“粒子贫化”的影响。对算法进行了仿真分析,并将该方法用于水下小目标探测实验的数据处理,结果表明,相比于常规的粒子滤波算法,所提出的混合粒子滤波得到了误差更小且稳定的状态估计结果,有效地改善水下小目标跟踪的精度和稳健性。     

Information and Entropy Flow in the Kalman–Bucy Filter

We investigate the information theoretic properties of Kalman–Bucy filters in continuous time, developing notions of information supply, storage and dissipation. Introducing a concept of energy, we develop a physical analogy in which the unobserved signal describes a statistical mechanical system interacting with a heat bath. The abstract universe comprising the signal and the heat bath obeys a non-increase law of entropy; however, with the introduction of partial observations, this law can be violated. The Kalman–Bucy filter behaves like a Maxwellian demon in this analogy, returning signal energy to the heat bath without causing entropy increase. This is made possible by the steady supply of new information. In a second analogy the signal and filter interact, setting up a stationary non-equilibrium state, in which energy flows between the heat bath, the signal and the filter without causing any overall entropy increase. We introduce a rate of interactive entropy flow that isolates the statistical mechanics of this flow from marginal effects. Both analogies provide quantitative examples of Landauers Principle.     

互谱域MVDR自适应时延估计方法研究

针对窄带信号,通过构造互谱时间序列,在互谱域建立了平稳时间序列时延估计的最小方差无畸变响应(MVDR)滤波器模型;利用分段近似处理,类比空间MVDR自适应算法,给出了其具体算法(Algorithm of MVDR in cross spectral domain,CSMVDR);进行了数值仿真实验研究和海上实验数据处理。数值仿真与实验数据处理结果初步验证了CSMVDR时延估计对于舰船辐射噪声的适用性,CSMVDR时延估计有比相关检测更好的时延估计性能,能够提高信噪比增益和时延估计精度。     

Signal reconstruction in wireless sensor networks based on a cubature Kalman particle filter

For solving the issues of the signal reconstruction of nonlinear non-Gaussian signals in wireless sensor networks(WSNs), a new signal reconstruction algorithm based on a cubature Kalman particle filter(CKPF) is proposed in this paper.We model the reconstruction signal first and then use the CKPF to estimate the signal. The CKPF uses a cubature Kalman filter(CKF) to generate the importance proposal distribution of the particle filter and integrates the latest observation, which can approximate the true posterior distribution better. It can improve the estimation accuracy. CKPF uses fewer cubature points than the unscented Kalman particle filter(UKPF) and has less computational overheads. Meanwhile, CKPF uses the square root of the error covariance for iterating and is more stable and accurate than the UKPF counterpart. Simulation results show that the algorithm can reconstruct the observed signals quickly and effectively, at the same time consuming less computational time and with more accuracy than the method based on UKPF.     

改进粒子滤波在汽轮机故障诊断中的应用夏飞,郝硕涛

针对汽轮机的振动信号容易受到较为复杂的随机噪声污染,提出了一种改进粒子滤波的振动信号降噪方法。首先建立采集振动信号的数学模型,将其作为粒子滤波的状态方程;然后利用小波分析提取采集振动信号的背景噪声,将其和状态信号一起作为观测信号,得到观测方程,把降噪问题转化成在状态空间模型下的滤波问题。由于采用序贯重要性采样的粒子滤波存在着样本退化问题,在重采样阶段采用了一种权值排序、优胜劣汰的重采样算法,就是对各粒子的归一化权值从小到大的排列顺序,并根据权值方差大小淘汰粒子,从而得到了改进的粒子滤波算法,在一定程度上解决了标准粒子滤波的退化问题。进而运用改进粒子滤波算法对振动信号进行降噪处理,降噪前信号和降噪后信号分别通过小波包分解系数求取频带能量,根据各个频带能量的变化提取故障特征向量浓缩了汽轮机振动故障的全部信息,对提取的故障特征向量应用诊断识别算法进行故障模式识别。通过对比降噪前信号和降噪后信号的故障诊断识别率,证明了改进粒子滤波在汽轮机故障诊断中的应用效果更佳。     

Full Causal Theory of Bulk Viscosity and Specific Entropy of the Universe

This article deals with the full Israel–Stewart causal theory of bulk viscosity as employed to the dissipative expansion of the early universe. It is shown that the nontruncated full theory can be cast in the form of a noncausal theory with an auxiliary condition which states that the square of dissipative contribution to the speed of sound varies with the particle number in a comoving volume. Also, a generalized temperature appears in a cosmological invariant which is shown to hold good for the dissipative expansion in an intermediate brief transition period (around the epoch time = 10^–23 s) between the very early mild inflation stage of the universe and the standard radiation-dominated FRW era of it. With this generalized temperature, the Gibbs equation has been generalized. This equation is also shown to have an alternative form with a term depending on bulk viscosity. In the dissipative transition period, the universe as a thermodynamically open system of viscous fluid can generate specific entropy. In this period the temperature rose to a considerable extent. Due to the cosmological invariant, the dissipative contribution to the speed of sound and consequently the particle number decreased sharply, ensuring the second law of thermodynamics. It is possible to have an estimate of the specific entropy in consistency with the observations. The total entropy and the particle number of the observable universe have also been found here. These estimates agree with the accepted values for them.     

Entropies of the various components of the universe

In this study, we investigate the entropies of photons, ideal gas-like dust (baryonic matter), and a special kind of dark energy in the context of cosmology. When these components expand freely with the universe, we calculate the entropy and specific entropy of each component from the perspective of statistics. Under specific assumptions and conditions, the entropies of these components can satisfy the second law of thermodynamics independently. Our calculations show that the specific entropy of matter cannot be a constant during the expansion of the universe, except for photons. When these components interact with the space-time background, particle production (annihilation) can occur. We study the influence of the interaction on the entropies of these components and obtain the conditions guaranteeing that the entropy of each component satisfies the second law of thermodynamics.     

量子纠缠与宇宙学弗里德曼方程

量子纠缠作为量子信息理论中最核心的部分,代表量子态一种内在的特性,是微观物质的一种根本的性质,它是以非定域的形式存在于多子量子系统中的一种神奇的物理现象.熵也是量子信息理论的重要概念之一,纠缠熵作为量子信息的一个测度已经成为一种重要的理论工具,为物理学中的各类课题提供了新的研究方法.本文主要考虑量子纠缠的宇宙学应用,试图更好地从纠缠的角度来理解宇宙动力学.本文研究了量子信息理论的概念和宇宙学之间的深层联系,利用费米正则坐标和共形费米坐标构建了弗里德曼- 勒梅特-罗伯逊-沃尔克宇宙学弗里德曼方程和纠缠之间的联系.假设小测地球（a geodesic ball）的纠缠熵在给定体积下是最大的,可以从量子纠缠第一定律推导出弗里德曼方程.研究表明引力与量子纠缠之间存在着某种深刻的联系,这种联系对引力场方程的解是成立的.     

Study of fully developed wind wave spectrum by application of quantum statistics

The spectrum of fully developed wind waves is studied by application of the method of quantum statistics. A particle picture of water waves is introduced as an analogy of wave–particle duality. “Water wave particles” are conceived which are similar to phonons for elastic waves in solids. However, due to the property of wave breaking, the number of “water wave particles” in a quantum state is restricted. The spectrum of fully developed wind waves is studied on the basis of the maximum entropy principle. The similarity law of fully developed wind wave spectrum is proved. In the high frequency range, the spectral form is in agreement with the result of observations. In the particle picture, a saturated spectrum is introduced which is in conceptual consistency with the saturated spectrum introduced by Phillips in the wave picture, and the form of which is the same as Phillips’. It is further shown that in the high frequency range the spectrum is only half saturated for fully developed wind waves. The frequency downshifting phenomenon which cannot be explained by wave theory is explained in the particle picture.