带未知模型参数和衰减观测率系统自校正分布式融合估计

研究了带未知模型参数和衰减观测率多传感器线性离散随机系统的信息融合估计问题.在模型参数和衰减观测率未知的情形下,应用递推增广最小二乘(Recursive extend least squares,RELS)算法和加权融合估计算法提出了分布式融合未知模型参数辨识器;应用相关函数对描述衰减观测现象的随机变量的数学期望和方差...     

非线性离散系统的相关观测融合时变Kalman滤波

针对带相关观测噪声和带不同观测函数的多传感器离散非线性系统,利用推广的离散Kalman滤波方法对状态系统和观测系统进行线性化处理,提出了基于岭估计的加权最小二乘(REWLS)分布式融合Kalman滤波算法.以风险函数为评价指标,利用信息滤波器比较了各种观测融合Kalman滤波算法,其中REWLS分布式融合算法精度最高.同时,分布式融合算法减少了计算负担,便于实时应用.仿真例子表明了理论分析的正确性.     

广义系统信息融合稳态与自校正满阶Kalman滤波器

基于线性最小方差标量加权融合算法和射影理论,对带多个传感器和带相关噪声的广义系统,提出了分布式标量加权融合稳态满阶Kalman滤波器.推得了任两个传感器子系统之间的稳态满阶滤波误差互协方差阵,其解可任选初值离线迭代计算.所提出的稳态融合滤波器避免了每时刻计算协方差阵和融合权重,减小了在线计算负担.当系统含有未知模型参数时,基于递推增广最小二乘算法和标量加权融合算法,提出了一种两段融合自校正状态滤波器.其中第1段融合获得未知参数的融合估计;第2段融合获得分布式自校正融合状态滤波器.与局部估计和加权平均融合估计相比,所提出的标量加权融合参数估计和自校正状态估计都具有更高的精度.仿真研究验证了其有效性.     

基于小波变换域的实时估计噪声特性算法研究

针对在非标准卡尔曼滤波情况下,解决未知观测噪声统计特性(均值和方差)的实时估计问题,提出了一种基于小波变换域的实时估计噪声特性算法,该算法可以实时跟踪观测噪声的变化,实现了对观测噪声均值和方差的实时估计,解决了在未知观测噪声的条件下卡尔曼滤波失效问题。仿真验证了算法的可行性和有效性,对未知观测噪声方差估计较传统估计精度提高约一个数量级。     

一种面向多无人机协同感知的分布式融合估计方法

针对基于扩展卡尔曼滤波的融合估计算法存在线性化误差,且分布式融合计算复杂等问题,基于无色变换、交互多模型和信息滤波,采用递阶分布式融合估计结构,提出一种分布式无色信息滤波算法.该方法中的无色变换能够保证更高的估计精度,交互多模型使其具有更好的鲁棒性,信息形式的卡尔曼滤波使融合估计计算变得简单.仿真结果表明,该算法能够提高多无人机融合估计性能.     

基于动态加权的分布式多传感器航迹融合算法

针对目前分布式航迹融合算法中鲁棒性和实时性问题,基于充分利用多传感器测量数据中互补和冗余信息的思想,通过局部航迹估计间模糊支持度函数的建立和支持度矩阵的求解,动态地实现各局部航迹估计在融合中心权重的合理分配,进而提出了一种基于动态加权的分布式多传感器航迹融合算法。最后,通过蒙特卡罗仿真验证了该算法的有效性。     

无线传感器网络最小测距误差估计定位算法

针对无线传感器网络的节点定位问题,提出一种分布式节点定位算法.算法通过构造未知节点到它邻居信标节点的测距误差之和的函数,并用Nelder-Mead单纯形方法计算该函数的最小值,以计算未知节点的坐标;通过使用时间轮机制来降低在迭代定位产生的累积误差.实验结果表明,用最小测距误差估计方法定位比用极大似然估计定位的平均定位精度高,使用时间轮机制可以有效降低迭代定位时产生的累积误差.     

一种频率域相关性分布式扩散最小均方算法

以均方误差为代价函数的最小均方（LMS）自适应滤波算法具有结构简单、易于实现、计算复杂度低、稳定性好等优点,然而在对未知系统的脉冲响应进行估计时,传统的分布式扩散最小均方（DLMS）算法易受到噪声的干扰,从而降低估计精度。针对该问题,提出一种频率域相关性分布式扩散最小均方（FCDLMS）算法。利用不相关信号的相关函数值趋近于零的性质,在DLMS算法基础上分别将输入信号的自相关函数以及输入和期望信号的互相关函数作为新的观测数据,消除噪声干扰,从而给出相关性DLMS （CDLMS）算法,并将算法扩展至频率域,在频率域中使用乘法运算而非卷积运算来更新抽头系数,减少计算复杂度。实验结果表明,与传统DLMS算法相比,频率域相关性分布式扩散最小均方算法在噪声环境下对分布式自适应网络中的未知系统脉冲响应具有更好的估计结果,算法性能更优,同时也能较好地适应多抽头数、多节点数、强噪声的复杂环境。     

一种滤波系数化的分布式航迹融合方法

传感器本身及外界环境存在的随机干扰,使得量测信息具有不确定性及相关性,且当某个传感器的测量精度较低时,直接融合观测信息导致融合算法有效性的降低。针对此问题,提出一种滤波系数化的分布式航迹融合方法。该方法通过局部估计信息建立支持度矩阵来完成滤波估计信息的系数化,最后加权组合滤波,实现对目标的实时融合跟踪。相对于原有融合算法,无论在哪种情况下该方法均使融合跟踪精度提高了16%以上,保证了算法的有效性。     

带相关噪声异步采样系统的分布式最优线性递推融合估计

对带相关噪声的异步均匀采样线性离散系统, 研究了分布式最优线性递推融合预报和滤波问题. 通过引入 满足伯努利分布的随机变量将系统同步化, 给出了局部Kalman预报器和滤波器. 分别推导了局部估值间的互协方 差阵、分布式最优线性融合估值与局部估值间的互协方差阵. 提出了分布式最优线性递推融合预报器和滤波器. 与 局部估值按矩阵加权的分布式融合估计算法相比, 所提出的算法具有更高的估计精度, 但与集中式融合相比有精度 损失. 为了进一步提高估计精度, 又提出了带反馈的分布式最优线性递推融合预报器和滤波器, 证明了带反馈的融 合估计与集中式融合估计具有相同的精度. 仿真例子验证了所提算法的有效性.     

带未知通信干扰和丢包补偿的多传感器网络化不确定系统的分布式融合滤波

研究了带有未知通信干扰、观测丢失和乘性噪声不确定性的多传感器网络化系统的状态估计问题.通过白色乘性噪声描述系统状态和观测中的随机不确定性,采用一组服从Bernoulli分布的随机变量描述网络传输过程中存在的观测丢失现象,且数据传输中存在未知的网络通信干扰.当发生丢包时,以当前丢失观测的预报值进行补偿.对每个单传感器子系统,应用线性无偏最小方差估计准则设计了不依赖于未知通信干扰的最优线性滤波器.推导了任两个局部滤波误差之间的互协方差阵.进而,应用矩阵加权融合估计算法给出了分布式融合状态滤波器.仿真例子验证了算法的有效性.     

Optimal distributed Kalman filtering fusion for a linear dynamic system with cross-correlated noises

In this article, we study the distributed Kalman filtering fusion problem for a linear dynamic system with multiple sensors and cross-correlated noises. For the assumed linear dynamic system, based on the newly constructed measurements whose measurement noises are uncorrelated, we derive a distributed Kalman filtering fusion algorithm without feedback, and prove that it is an optimal distributed Kalman filtering fusion algorithm. Then, for the same linear dynamic system, also based on the newly constructed measurements, a distributed Kalman filtering fusion algorithm with feedback is proposed. A rigorous performance analysis is dedicated to the distributed fusion algorithm with feedback, which shows that the distributed fusion algorithm with feedback is also an optimal distributed Kalman filtering fusion algorithm; the P matrices are still the estimate error covariance matrices for local filters; the feedback does reduce the estimate error covariance of each local filter. Simulation results are provided to demonstrate the validity of the newly proposed fusion algorithms and the performance analysis.     

基于最小二乘准则的多传感器参数估计数据融合

为了从含有加性测量噪声的线性测量数据中更加准确地估计未知的常值参数,测量噪声互不相关的多传感器测量系统得到广泛使用。在最小二乘准则下,提出了多传感器测量系统在多次同步测量时的集中式和分布式参数估计数据融合算法,两种算法完全等价,且都是全局最优的。数值仿真实验的结果表明,新算法可以明显改善传感器测量参数的估计精度。     

The sufficient condition for lossless linear transformation for distributed estimation with cross-correlated measurement noises

In most distributed fusion algorithms, the measurement noises in different sensors are often assumed to be uncorrelated, but in practical occasions the assumption may not be met and the measurement noises are often cross-correlated between sensors. So the lossless distributed fusion algorithms with the assumption of uncorrelated measurement noises usually cannot keep their lossless performance in practical applications. Therefore, in the case of cross-correlated measurement noises, the lossless compression rule for distributed estimation is proposed. We prove in theory that the sufficient condition of the lossless compression is the transformation matrix is of full column rank. Using the transformation matrix constructed by the proposed rule, the distributed fusion can achieve the performance of the centralized one. In addition, under this rule two optimal fusion algorithms are proposed and their performances are analyzed.     

Distributed fusion estimation for multi-sensor asynchronous sampling systems with correlated noises

This paper is concerned with the distributed fusion estimation problem for a class of multi-sensor asynchronous sampling systems with correlated noises. The state updates uniformly and the sensors sample randomly. Based on the measurement augmentation method, the asynchronous sampling system is transformed to the synchronous sampling one. Local filter is designed by using an innovation analysis approach. Then, the filtering error cross-covariance matrix between any two local filters is derived. Finally, the optimal distributed fusion filter is proposed by using matrix-weighted fusion algorithm in the linear minimum variance sense. Simulation results show the effectiveness of the proposed algorithms.     

多传感器自主在线融合方法

在先验知识未知的情形下,针对现有融合算法的不足,提出了一种新的融合算法。为了进一步提高融合精度,算法用均值和自熵两个概念充分挖掘测量中的冗余信息,进而确定传感器的融合权重。此外,为了预防“数据饱和”的发生,算法在迭代过程中引入限定记忆项,保证算法对数据变化的灵敏性。用均值融合算法、冲突证据预处理算法和新算法对样本数据进行仿真。仿真结果表明,运用新算法得到的权值分配方式更加合理,可进一步提高融合精度。     

State estimation and multi-sensor data fusion using data-based neurofuzzy local linearisation process models

Modelling unknown non-linear dynamic processes is an essential prerequisite for model-based state estimation and fusion. Fuzzy local linearisation (FLL) is a useful divide-and-conquer method for coping with complex problems such as data-based non-linear process modelling. In this paper, a hybrid learning scheme which combines a modified adaptive spline modelling (MASMOD) algorithm and the expectation-maximisation (EM) algorithm is developed for FLL modelling, based on which Kalman filter type algorithms for state estimation and multi-sensor data fusion are investigated. Two commonly used measurement fusion methods are analytically compared. A hierarchical multi-sensor data fusion architecture is proposed, with an example of non-linear trajectory estimation to validate the proposed method, which integrates the techniques for FLL modelling, neurofuzzy state estimation and multi-sensor data fusion. Whilst this paper mainly focuses on state estimation and data fusion for unknown non-linear dynamic processes, maneuvering targets are also briefly considered.     

A robust D–S fusion algorithm for multi-target multi-sensor with higher reliability

This paper addresses the problem of track fusion for unordered distributed sensors with unknown measurement noise. A robust Dempster–Shafer (D–S) fusion algorithm is proposed, which includes three parts, namely, the local track estimation, the track association, and the state fusion. First, a labeling VB-PHD filter is derived to present target states with track labels and the unknown measurement noises of local sensors. Next, a heuristic D–S method is proposed to determine the relationship of local tracks and fused tracks, where the accumulated information is taken into account. Finally, a fusion method is given to show the state fusion results, which can fully utilize local state estimates and measurement noise information. Simulation results are provided to illustrate the high precision of tracking and good robustness, comparing with the traditional methods.     

扩散式变阶数最大相关熵准则算法

固定阶数的分布式自适应滤波算法只有在待估计向量的阶数已知且恒定的情况下才能达到相应的估计精度,在阶数未知或时变的情况下算法的收敛性能会受到影响,变阶数的分布式自适应滤波算法是解决上述问题的有效途径。但是目前大多数分布式变阶数自适应滤波算法以最小均方误差(Mean square Error, MSE)准则作为滤波器阶数的代价函数,在脉冲噪声环境下算法的收敛过程会受到较大影响。最大相关熵准则具有对脉冲噪声的强鲁棒性,且计算复杂度低。为提高分布式变阶数自适应滤波算法在脉冲噪声环境下的估计精度,利用最大相关熵准则作为滤波器阶数迭代的代价函数,并将得到的结果代入固定阶数的扩散式最大相关熵准则算法,提出了一种扩散式变阶数最大相关熵准则(Diffusion Variable Tap-length Maximum Correntropy Criterion, DVTMCC)算法。通过与邻域的节点进行通信,所提算法以扩散的方式实现了整个网络的信息融合,具有估计精度高、计算量小等优点。仿真实验对比了在脉冲噪声下DVTMCC算法和其他分布式变阶数自适应滤波算法、固定阶数的扩散式最大相关熵准则算法的收敛性能。...