容积卡尔曼一致滤波

针对卡尔曼一致滤波的应用受限于被估计系统需 满足线性条件的问题,通过容积卡尔曼滤波(CKF)和一致性策 略的动态结合,提出一种容积卡尔曼一致滤波(CKCF)算法。算法采用分布式融合机制, 传感器节点采集可通信相邻 节点的信息,并作为自身节点的量测信息应用于CKF,获取局部状态估计 值。在此基础上,利用一 致性策略实现对整个量测系统中传感器节点局部估计值的优化,进而通过增强传感器节点估 计值一致性实现目标 状态估计精度的提升。相对于标准卡尔曼一致滤波,本文算法将一致性策略推广到非线性系 统估计领域。理论分析 与仿真实验验证了算法的可行性与有效性。     

基于容积卡尔曼滤波的异质多传感器融合算法

针对机动目标跟踪系统建模中的非线性问题,提出一种基于容积卡尔曼滤波(CKF)的雷达与红外传感器融合算法。考虑到被估计系统对目标跟踪算法实时性与精度的要求,在容积滤波框架下构建了集中式量测融合(CMF)和分布式状态融合(DSF)两种结构形式。CMF结构采用最优加权方法,首先对雷达和红外两种异类传感器的方位角度量测信息进行融合,并将其与融合后的雷达径向距量测构建新的量测数据,进而通过CKF算法对机动目标进行跟踪。DSF结构则首先对雷达量测中径向距信息进行加权融合,并将融合结果作为红外传感器的虚拟径向距量测,以实现红外量测的扩维处理,进而对每组量测数据应用CKF进行分布式并行加权融合,获得目标运动状态的最终估计。仿真场景中,对两种融合方法的性能进行比较,理论分析与仿真实验验证了算法的可行性与有效性。     

多站无源跟踪边缘化卡尔曼滤波算法

多站无源跟踪量测方程非线性强,对跟踪算法的稳定性及精度提出了更高的要求。为实现稳定高精度跟踪,提出了新的基于边缘化卡尔曼滤波（MKF）的多机无源跟踪算法。该算法将非线性的量测方程表示为p阶Hermite多项式的加权和,将加权矩阵的先验分布建模为高斯过程,求得其后验分布后对其进行积分来消除加权矩阵的影响,最终可得对状态及其协方差矩阵估计的闭式解。以只测角跟踪为例对所提算法性能进行验证,仿真结果表明,相对于扩展卡尔曼滤波（EKF）算法、不敏卡尔曼滤波（UKF）算法及容积卡尔曼滤波（CKF）算法,所提算法具有更好的跟踪性能。      

五阶容积卡尔曼滤波算法及其应用

容积卡尔曼滤波(CKF)是一种新型的非线性滤波方法,可获得优于扩展卡尔曼滤波(EKF)和无迹卡尔曼滤波(UKF)的滤波精度和滤波效率.但是,传统的CKF基于三阶容积准则而提出,因此滤波精度受到限制,为进一步提高CKF滤波性能,文中将容积准则由三阶扩展到五阶,采用两种不同容积点集选择方案,提出一种新型的五阶CKF算法.该算法可有效改善传统CKF在精度方面的理论局限,并有效改善一般五阶CKF计算量大的问题.机动目标跟踪仿真结果表明了新方法的有效性和可行性.     

两阶段高维容积信息滤波及其在目标跟踪中的应用

非线性系统估计的过程是一个多传感器信息融合的过程,在集中处理量测数据的过程中,Kalman滤波具有很高的计算复杂度.尤其当系统模型中存在随机偏差时,扩维后计算量大幅增大,容易造成系统溢出和运行失败的问题.通过将两阶段容积Kalman滤波嵌入到扩展信息滤波框架的方式,提出了一种两阶段高维容积信息滤波算法.该算法初始化容易,计算量较小,直接利用协方差矩阵的逆与信息矩阵之间的等价关系参与滤波递推的过程,减少了对滤波增益阵的计算.在协方差矩阵的解算过程中,两阶段算法的协方差矩阵之间存在有耦合关系,因此在信息滤波中,两阶段信息矩阵之间也存在着某种耦合关系,算法中通过将非线性T变换和矩阵求逆应用于信息矩阵,得到了两阶段信息矩阵与协方差矩阵之间的耦合关系.通过纯方位跟踪系统的仿真实验,验证了两阶段高维容积信息滤波算法在精度上高于容积Kalman滤波算法,在运行时间上也短于容积Kalman滤波算法,证明了该算法的可用性.     

基于后向平滑容积卡尔曼滤波的单站无源定位算法

单站无源定位跟踪是一个典型的非线性滤波问题,由于测量精度不高、初始误差较大等原因容易导致滤波算法定位精度低、收敛速度慢。本文将一种新型的滤波算法——容积卡尔曼滤波（cubature Kalman filter,CKF）应用于单站无源定位领域,并将后向平滑滤波思想与CKF算法相结合,提出了一种后向平滑容积卡尔曼滤波算法（backward-smoothing CKF,BSCKF）。该算法使用容积数值积分原则直接计算非线性随机函数的均值和方差,并采用后向平滑值进行递归滤波,具有更优非线性估计性能。仿真实验表明,与EKF、UKF和CKF算法相比,BSCKF算法定位精度更高、收敛速度更快。      

Fault detection in sensor information fusion Kalman filter

An approach to the test of the sensor information fusion Kalman filter is proposed. It is based on the introduced statistics of mathematical expectation of the spectral norm of a normalized innovation matrix. The approach allows for simultaneous test of the mathematical expectation and the variance of innovation sequence in real time and does not require a priori information on values of the change in its statistical characteristics under faults. Using this approach, fault detection algorithm for the sensor information fusion Kalman filter is developed.     

相干光OFDM系统中一种新颖时域容积卡尔曼相位噪声补偿算法

针对激光器产生的相位噪声会严重影响相干光正交频分复用(CO-OFDM)系统的性能问题,提出了一种新颖容积卡尔曼滤波(CKF)相位噪声补偿算法。该算法利用导频信息,先通过扩展卡尔曼滤波(EKF)和线性插值算法补偿公共相位误差(CPE)噪声,然后对相位噪声粗补偿后的信号进行预判决,在时域对预判决后的信号进行次符号处理的CKF实现对载波间干扰(ICI)相位噪声的精细补偿。对补偿后的信号进行二次迭代,从而提高补偿效果。分析和仿真表明:提出的新颖CKF算法能有效补偿相位噪声对信号的影响,在相位噪声线宽较大时能有效增强对ICI相位噪声的补偿效果,改善CO-OFDM系统对激光器线宽的容忍度,有效提高系统的性能。     

基于IMM-RDCKF的机动目标跟踪算法

针对机动目标跟踪问题,提出了一种IMM-RDCKF算法。首先充分利用量测方程中只有部分状态变量是非线性的特点,对于非线性的量测方程采用降维滤波方法,可以在保障跟踪精度条件下减小计算量。其次,对IMM算法中的转移概率矩阵进行实时估计,提高了模型匹配概率。再次,滤波过程中由于误差累积可能导致协方差矩阵失去正定性,对算法进行了优化,确保了滤波过程中协方差矩阵的正定性,提高了算法稳定性。Monte-Carlo仿真结果表明,与CKF算法相比,该算法的跟踪精度有明显的提高,计算效率提高了一倍。     

一种适用于分布式摄像机网络的SCIWCF算法

本文使用容积卡尔曼滤波器来处理分布式摄像机网络中的目标跟踪问题.平方根容积信息滤波（Square-Root Cubature Information Filter,SCIF）是容积卡尔曼滤波的一种扩展,其具有有效性和可靠性等方面优势,有利于对多源信息进行信息融合.然而当该算法应用于像摄像机网络这种大规模网络时,如果采用一般的集中式处理,中心节点可能会承受较大的计算压力.针对这个问题,本文首先将平方根容积信息滤波器进行了扩展,提出分布式平方根容积信息滤波器,使其能适应大规模网络.另外在摄像机网络中,由于摄像机装置在一个较大的区域内,由于摄像机观测区域有限,目标可能会出现在观察的盲区,这样就会存在某些摄像机的测量数据无效.针对这个问题,本文提出了平方根容积信息加权一致性滤波器（Square-Root Cubature Information Weighted Consensus Filter,SCIWCF）对状态信息和信息矩阵加权,减小这些无效信息在一致性算法的作用,从而提高整体的滤波性能.仿真实验结果表明,本文提出的算法能够在摄像机网络中对目标进行有效跟踪,在估计精度和滤波器稳定性等方面要优于传统的信息滤波.     

Augmented consensus filter for simultaneous localization and tracking with limited sense range

In this paper, we investigate how to exploit distributed average consensus fusion for conducting simultaneous localization and tracking (SLAT) by using wireless sensor networks. To this end, we commence by establishing a limited sense range (LSR) nonlinear system that characterizes the coupling of target state and sensor localization with respect to each sensor. We then employ an augmented extended Kalman filter to estimate the sensor and target states of our system. Furthermore, we adopt a consensus filtering scheme which fuses the information from neighboring sensors. We thus obtain a two-stage distributed filtering framework that not only obtains updated sensor locations trough augment filtering but also provides an accurate target state estimate in consensus filtering. Additionally, our framework is computationally efficient because it only requires neighboring sensor communications. The simulation results reveal that the proposed filtering framework is much more robust than traditional information fusion methods in limited ranging conditions.     

平方根容积卡尔曼滤波在角测量跟踪中的应用

为解决扩展卡尔曼滤波算法(EKF)在处理角测量跟踪问题时对复杂非线性状态估计收敛速度慢、估计精度低的问题,引入一种平方根容积卡尔曼滤波算法(SRCKF)。SRCKF是一类sigma点滤波方法,基于容积原则的数值积分方法计算非线性随机函数的均值与协方差,避免了EKF中Jacobian矩阵的计算,有效提高了计算效率。另外,与一般容积卡尔曼滤波算法相比,SRCKF确保了状态协方差矩阵的对称性与半正定性,有效改进了数值精度和鲁棒性。将SRCKF应用于角测量跟踪系统中,仿真结果表明,SRCKF、Unscented卡尔曼滤波(UKF)滤波精度较传统EKF有较大提高,同时,与UKF相比,SRCKF能以较快的运行效率获得较好的滤波效果。     

测角目标定位的协方差矩阵旋转变换滤波算法

基于滤波量比预测量和测量量都准确这一原理,该文提出一种适用于只测角无源定位的非线性滤波算法——协方差矩阵旋转变换的推广Kalman滤波算法.文中推导了协方差矩阵变换的原理,给出了二维只测角无源定位应用的协方差矩阵旋转变换公式.仿真表明该算法比同类型的基于微分线性化和泛线性化的Kalman滤波算法具有更好的性能.     

Missing data estimation and IoT-based flyby monitoring of a water distribution system: Conceptual and experimental validation

Achieving uninterrupted water supply to the consumer node is considered to be the significant aspect for urban water distribution system (WDS). Under circumstances such as sensor failures or large sampling interval, the intermittent vital data are ignored, which lead to the missing data problem. In this work, an enhanced version of Kalman filter (KF) is proposed termed to be customized KF (CKF). The proposed CKF is equipped to handle the state- and input-dependent noises, which are amplified based on the input and states in WDS. These noises corrupt the measured response from the WDS added to the conventional sensor and model uncertainties. In this case, a real-world-existing WDS is considered to test the credibility of the proposed algorithm. This reduces the complexity of the computation and the sampling rate of the measured head level, and the flows are considered to be random. The study indicates that the proposed CKF performs better in estimating the missing data voids with the noise-corrupted measurements. To have the continuous monitoring accessible in remote sections, the vital parameters are monitored through Internet of Things (IoT) in android platform. The estimated data from the MATLAB and the sensor data of the consumer demands are monitored through user-defined android application. This technique provides the advantage of monitoring the WDS in flyby condition and also even in the presence of any sensor failures, which also helps to localize the fault location.     

基于IMMCKF的机动目标跟踪算法

针对非线性机动目标跟踪中滤波器易发散、跟踪精度低等问题,将容积卡尔曼滤波器(CKF)引入到交互式多模型算法(IMM)中,设计了交互式多模型容积卡尔曼滤波算法(IMMCKF)。该算法采用Markov过程描述多个目标模型间的切换,利用CKF滤波器对每个模型进行滤波,将各滤波器状态输出的概率加权融合作为IMMCKF的输出。仿真结果表明,与IMMUKF算法相比,IMMCKF算法跟踪精度更高,模型切换速度更快,计算量更小,该算法具有重要的工程应用价值。     

A Novel Distributed Sensor Fusion Algorithm for RSSI-Based Location Estimation Using the Unscented Kalman Filter

We address the Bayesian sensor fusion approach for distributed location estimation in the wireless sensor network. Assume each sensor transmits local calculation of target position to a fusion center, which then generates under a Bayesian framework the final estimated trajectory. We study received signal strength indication-based approach using the unscented Kalman filter for each sensor to compute local estimation, and propose a novel distributed algorithm which combines the soft outputs sent from selected sensors and computes the approximated Bayesian estimates to the true position. Simulation results demonstrate that the proposed soft combining method can achieve similar tracking performance as the centralized data fusion approach. The computational cost of the proposed algorithm is less than the centralized method especially in large scale sensor networks. In addition, it is straightforward to incorporate the proposed soft combining strategy with other Bayesian filters for the general purpose of data fusion.

     

一种量测转换的移动外辐射源多站协同跟踪算法

针对移动外辐射源跟踪问题,提出一种融合到达角(Angle of Arrival,AOA)与到达时间差(Time Difference of Arrival,TDOA)观测量的量测转换Kalman滤波(Converted Measurement Kalman Filter,CMKF)算法。首先,采用了一种考虑了传感器位置偏差影响的无源定位算法作为转换非线性的AOA与TDOA观测量至笛卡尔坐标系下观测量的方法,并证明了当AOA与TDOA的测量噪声以及传感器位置偏差都服从高斯分布且噪声强度不大时,该量测转换方法的位置转换误差能达到克拉美罗(Cramér-Rao Lower Bound,CRLB)界;其次,在量测转换的基础上构建了关于移动外辐射源的线性状态空间模型,将非线性的目标跟踪问题转化为线性滤波问题,并最终使用标准Kalman滤波器实时跟踪移动外辐射源位置。仿真结果不仅验证了量测转换精度与理论分析结论吻合,还表明了所提CMKF算法的跟踪精度同时优于扩展Kalman滤波器、无迹滤波器以及粒子滤波器。     

Cubature Information Filters Using High-Degree and Embedded Cubature Rules

The information form of the Kalman filter (KF) is preferred over standard covariance filters in multiple sensor fusion problems. Aiming at this issue, two types of cubature information filters (CIF) for nonlinear systems are presented in this article. The two approaches, which we have named the embedded cubature information filter (ECIF) and the fifth-degree cubature information filter (FCIF), are developed from a fifth-degree cubature Kalman filter and a newly proposed embedded cubature KF. Theoretical analysis shows that the proposed filters can achieve higher level estimation accuracy than conventional information filters, such as the CIF and the extended information filter (EIF). Performance comparisons of the proposed information filters with the conventional CIF are demonstrated via two independent multisensor tracking problems. The experimental results, presented herein, demonstrate that the proposed algorithms are more reliable and accurate than the CIF.     

改进的迭代CKF及其在GNSS/INS紧组合中的应用

为改善GNSS/INS紧组合中非线性滤波器的稳定性和精度,基于Backtracking线搜索(BLS)方法提出一种新型的迭代容积卡尔曼滤波方法(BLS-ICKF)。首先建立Sigma点滤波方法的迭代框架,采用阻尼Newton迭代改善状态初始误差较大时的滤波更新精度,同时,为提高组合系统状态预测过程的鲁棒性定义了一种简化的迭代更新结构。实验和仿真结果表明,BLS-ICKF牺牲较小的时间复杂度较CKF的航向角误差减少约59%。     

基于理想弹道的全捷联弹药视线角估计方法

针对惯性测量元件不能满足低成本制导弹药作战需求问题,提出一种基于理想弹道的全捷联激光半主动末制导弹药视线角估计方法。该方法根据弹目相对运动模型及导引头量测模型建立非线性滤波系统;针对弹体运动参数在末制导段变化范围较小的特点,通过分析弹体运动参数对系统不确定性的影响,将理想弹道弹体运动参数标准值作为滤波系统参数;利用激光半主动导引头量测信息,结合容积卡尔曼滤波对弹目视线角进行估计。数字仿真实验结果表明:在小扰动条件下,弹目视线倾角与偏角末制导段的均方根误差分别为0.182与1.668,其最大估计误差分别为0.259与2.913,具有较好的估计精度与鲁棒性能。