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。     

Distributed Information Consensus Filters for Simultaneous Input and State Estimation

This paper describes the distributed information filtering where a set of sensor networks are required to simultaneously estimate input and state of a linear discrete-time system from collaborative manner. Our research purpose is to develop a consensus strategy in which sensor nodes communicate within the network through a sequence of Kalman iterations and data diffusion. A novel recursive information filtering is proposed by integrating input estimation error into measurement data and weighted information matrices. On the fusing process, local system state filtering transmits estimation information using the consensus averaging algorithm, which penalizes the disagreement in a dynamic manner. A simulation example is provided to compare the performance of the distributed information filtering with optimal Gillijins–De Moor’s algorithm.     

集中式BLUE滤波器在组网雷达中的应用

为解决组网雷达对目标跟踪中的量测非线性问题,提出基于最佳线性无偏估计器(BLUE)准则的融合滤波方法。建立以融合中心为原点的组网雷达对目标定位的量测方程,推导出极坐标系与球坐标系下跟踪目标的BLUE滤波模型。理论分析表明,集中式BLUE滤波架构在估计单个雷达量测转换误差统计特性的同时,还估计出雷达间量测转换误差的统计特性。因此,跟踪精度和置信度较分布式BLUE滤波方法有显著提高,计算量较其他算法也有明显优势。不同场景下的仿真分析证明:该方法在不同状态噪声水平下的表现优异,是一种很有竞争力的跟踪算法。     

一种基于分步式滤波的数据融合算法

本文提出了一种基于分步式滤波的多传感器动态系统数据融合算法.在由多传感器组成的分布式动态系统中,当对目标状态的所有观测值到来时,首先基于系统先前信息对该时刻目标状态进行预测估计,利用Kalman滤波器和各局部观测值依次对该时刻目标状态的估计值进行更新,从而得到该时刻目标状态基于全局信息的融合估计值.文中详细推证了融合算法的具体形式,并与传统的集中式数据融合算法在计算复杂度上进行了比较,计算机仿真表明该算法与传统的集中式算法对目标状态具有相同的估计精确度.     

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。     

带反馈信息的分布多传感器航迹融合算法

为了改善传感器级的跟踪性能,本文研究带反馈信息的多传感器状态估计技术。在给出有、无反馈信息情况下的局部节点状态估计解的基础上,该文提出多坐标系中有、无反馈信息情况下的航迹融合方程。并指出有、无反馈信息情况下的两种融合解是等价的、最优的。仿真结果表明,在分布式多传感器信息融合系统中引入反馈机制可以明显改善局部节点估计精度,其性能已接近融合中心。在集中和雷达反隐身系统中,就空间重叠、覆盖而论,融合系统局部节点一般选2至4个为宜。     

基于强跟踪滤波器的多传感器非线性动态系统状态与参数联合估计

本文将强跟踪滤波理论与多传感器数据融合技术相结合,提出基于强跟踪滤波器的多传感器状态与参数联合估计新算法;对拥有相同采样率的分布式多传感器单模型非线性动态系统,应用强跟踪滤波器,得到目标状态基于全局信息融合估计结果,并利用计算机仿真结果对算法的有效性进行了验证;这些工作初步解决了Kalman滤波中由于模型的不确定性而造成估计误差值偏大情况下的状态融合估计问题,从而丰富和发展了多源信息融合理论.     

Information Regularized Sensor Fusion: Application to Localization With Distributed Motion Sensors

We propose the information regularization principle for fusing information from sets of identical sensors observing a target phenomenon. The principle basically proposes an importance-weighting scheme for each sensor measurement based on the mutual information based pairwise statistical similarity matrix between sensors. The principle is applied to maximum likelihood estimation and particle filter based state estimation. A demonstration of the proposed regularization scheme in centralized data fusion of dense motion detector networks for target tracking is provided. Simulations confirm that the introduction of information regularization significantly improves localization accuracy of both maximum likelihood and particle filter approaches compared to their baseline implementations. Outlier detection and sensor failure detection capabilities, as well as possible extensions of the principle to decentralized sensor fusion with communication constraints are briefly discussed.     

分布式多目标伯努利滤波器的网络共识技术

本文研究了基于网络共识的分布式多目标伯努利(Multi-Bernoulli, MB)滤波器的目标跟踪技术。网络共识技术是实现传感器网络中分布式计算的一个强大工具,但同时对传感器间公共信息被“重复计算”问题尤为敏感。为解决该问题,本文首先在基于广义协方差交集(Generalized Covariance Intersection, GCI)准则的分布式MB (GCI-MB) 滤波器的基础上,通过采用序贯信息交互-本地融合的策略,提出网络共识(Consensus)-GCI-MB融合算法,简称C-GCI-MB融合;然后,通过数学理论分析了C-GCI-MB融合可以有效的避免“重复计算”问题;最后给出了C-GCI-MB融合算法的混合高斯(Gaussian Mixture)实现方法,并通过典型场景仿真验证了该算法的有效性及性能优势。      

基于格拉布斯准则和改进粒子滤波算法的水下传感网目标跟踪

水下无线传感网络(UWSN)执行目标跟踪时,因为各个传感器节点测量值对目标状态估计的贡献不一样以及节点能量有限,所以探索一种好的节点融合权重方法和节点规划机制能够获得更好的跟踪性能。针对上述问题,该文提出一种基于Grubbs准则和互信息熵加权融合的分布式粒子滤波(PF)目标跟踪算法(GMIEW)。首先利用Grubbs准则对传感器节点所获得的信息进行分析检验,去除干扰信息和错误信息。其次,在粒子滤波的重要性权值计算的过程中,引入动态加权因子,采用传感器节点的测量值与目标状态之间的互信息熵,来反映传感器节点提供的目标信息量,从而获得各个节点相应的加权因子。最后,采用3维场景下的簇-树型网络拓扑结构,跟踪监测区域内的目标。实验结果显示,该算法可有效提高水下传感器网络测量数据对目标跟踪预测的准确度,降低跟踪误差。     

Decentralized Consensus Based Target Localization in Wireless Sensor Networks

Target localization is an attractive subject for modern systems that utilize different types of distributed sensors for location based services such as navigation, public transport, retail services and so on. Target localization could be performed in both centralized and decentralized manner. Due to drawbacks of centralized systems such as security and reliability issues, decentralized systems are become more desirable. In this paper, we introduce a new decentralized and cooperative target localization algorithm for wireless sensor networks. In cooperative consensus based localization, each sensor knows its own location and estimates the targets position using the ranging techniques such as received signal strength. Then, all nodes cooperate with their neighbours and share their information to reach a consensus on targets location. In our proposed algorithm, we weight the received information of neighbour nodes according to their estimated distance toward the target node. Simulation results confirm that our proposed algorithm is faster, less sensitive to targets location and improves the localization accuracy by 85% in comparison with distributed Gauss–Newton algorithm.     

基于互信息的多传感器最优布设方法研究

在目标跟踪系统中，一个核心的问题是如何根据传感器的量测数据准确估计目标的运动状态。传统的滤波估计算法仅是对量测数据的优化处理，但量测数据才是决定目标跟踪系统性能的基础。量测数据的优劣不仅取决于传感器的性能，也取决于传感器与目标的相对位置。本文针对纯方位量测传感器，以互信息为效能函数，推导了一个基于互信息最大化的多传感器最优布设方法，有效提高了目标估计的精度，并通过仿真证明了理论分析的正确性。     

Optimal sensor and path selection for target tracking in wireless sensor networks

This paper addresses target tracking in wireless sensor networks where the nonlinear observed system is assumed to progress according to a probabilistic state space model. Thus, we propose to improve the use of the quantized variational filtering by jointly selecting the optimal candidate sensor that participates in target localization and its best communication path to the cluster head. In the current work, firstly, we select the optimal sensor in order to provide the required data of the target and to balance the energy dissipation in the wireless sensor networks. This selection is also based on the local cluster node density and their transmission power. Secondly, we select the best communication path that achieves the highest signal‐to‐noise ratio at the cluster head; then, we estimate the target position using quantized variational filtering algorithm. The best communication path is designed to reduce the communication cost, which leads to a significant reduction of energy consumption and an accurate target tracking. The optimal sensor selection is based on mutual information maximization under energy constraints, which is computed by using the target position predictive distribution provided by the quantized variational filtering algorithm. The simulation results show that the proposed method outperforms the quantized variational filtering under sensing range constraint, binary variational filtering, and the centralized quantized particle filtering. Copyright © 2012 John Wiley & Sons, Ltd.     

Information Content-Based Sensor Selection and Transmission Power Adjustment for Collaborative Target Tracking

For target tracking applications, wireless sensor nodes provide accurate information since they can be deployed and operated near the phenomenon. These sensing devices have the opportunity of collaboration among themselves to improve the target localization and tracking accuracies. An energy-efficient collaborative target tracking paradigm is developed for wireless sensor networks (WSNs). A mutual-information-based sensor selection (MISS) algorithm is adopted for participation in the fusion process. MISS allows the sensor nodes with the highest mutual information about the target state to transmit data so that the energy consumption is reduced while the desired target position estimation accuracy is met. In addition, a novel approach to energy savings in WSNs is devised in the information-controlled transmission power (ICTP) adjustment, where nodes with more information use higher transmission powers than those that are less informative to share their target state information with the neighboring nodes. Simulations demonstrate the performance gains offered by MISS and ICTP in terms of power consumption and target localization accuracy.     

基于DP-TBD的分布式异步迭代滤波融合算法研究

该文主要运用检测前跟踪动态规划(Dynamic Programming-Track Before Detect)算法解决目标跟踪问题。动态规划(Dynamic Programming, DP)是一种通过对量测空间栅格化处理,然后对离散的量测空间中所有可能的物理路径进行遍历的算法。然而,该算法提供的是一种未经滤波和平滑的点迹序列。随着实际战争环境日益复杂,基于单雷达的DP-TBD算法在信噪比(SNR)较低时跟踪效果不佳。此外,由于DP-TBD算法没有状态误差协方差矩阵,因此无法将不同雷达的点迹序列进行融合。而且由于通信时延和不同的采样周期,不同雷达的数据往往是异步的。为了解决以上问题,该文提出了一种基于DP-TBD的分布式异步迭代滤波融合算法(DynamicProgramming?Fuison, DPF)。该算法分为两步,第1步提出了一种迭代滤波方法对DP点迹进行处理;第2步将不同雷达获得的异步状态估计转化为同步的,接着利用几种分布式的融合方法来获取融合之后的状态估计。仿真结果说明,和单雷达相比,该融合算法可以有效提升目标跟踪的性能,同时,该算法也可以降低航迹丢失率和计算量。      

Smart Phone Based Sensor Fusion by Using Madgwick Filter for 3D Indoor Navigation

In this paper, we proposed an enhanced pedestrian dead reckoning (PDR) system based on sensor fusion schemes using a smartphone. PDR is an effective technology for 3D indoor navigation. However, still, there are some obstacles to be overcome in its practical application. To track and simulate pedestrian’s position, which is confronted by environmental errors, walls, Bayesian errors, and other obstacles, our proposed PDR system enables estimation of stride based on the vertical accelerometer data and orientation from sensor fusion technique of magnetic angular rate and gravity sensor data by Madgwick filter. This localization system is independent of the received signal strength-based fingerprinting system. In addition, to estimate the current floor level, we make use of barometer information. To collect ground truth accurately and efficiently a prototype is implemented with the benchmark. We perform the same distance estimation for four different pedestrians to evaluate the accuracy of the proposed system. The real indoor experimental results demonstrate that the proposed system performs well while tracking the test subject in a 2D scenario with low estimation error (< 2 m). The 3D evaluation of the system inside a multi-story building shows that high accuracy can be achieved for a short range of time without position update from external sources. Then we compared localization performance between our proposed system and an existing (extended Kalman filter based) system.

     

The research of gradation fusion algorithm based on multisensor asynchronous sampling system

This letter explores the distributed multisensor dynamic system, which has uniform sampling velocity and asynchronous sampling data for different sensors, and puts forward a new gradation fusion algorithm of multisensor dynamic system. As the total forecasted increment value between the two adjacent moments is the forecasted estimate value of the corresponding state increment in the fusion center, the new algorithm models the state and the forecasted estimate value of every moment. Kalman filter and all measurements arriving sequentially in the fusion period are employed to update the evaluation of target state step by step, on the condition that the system has obtained the target state evaluation that is based on the overall information in the previous fusion period. Accordingly, in the present period, the fusion evaluation of the target state at each sampling point on the basis of the overall information can be obtained. This letter elaborates the form of this new algorithm. Computer simulation demonstrates that this new algorithm owns greater precision in estimating target state than the present asynchronous fusion algorithm calibrated in time does.     

A Distributed Localization Scheme for Wireless Sensor Networks with Improved Grid-Scan and Vector-Based Refinement

Localization is a fundamental and essential issue for wireless sensor networks (WSNs). Existing localization algorithms can be categorized as either range-based or range-free schemes. Range-based schemes are not suitable for WSNs because of their irregularity of radio propagation and their cost of additional devices. In contrast, range-free schemes do not need to use received signal strength to estimate distances and only need simple and cheap hardware, and are thus more suitable for WSNs. However, existing range-free schemes are too costly and not accurate enough or are not scalable. To improve previous work, we present a fully distributed range-free localization scheme for WSNs. We assume that only a few sensor nodes, called anchors, know their locations, and the remaining (normal) nodes need to estimate their own locations by gathering nearby neighboring information. We propose an improved grid-scan algorithm to find the estimated locations of the normal nodes. Furthermore, we derive a vector-based refinement scheme to improve the accuracy of the estimated locations. Analysis, simulation, and experiment results show that our scheme outperforms the other range-free schemes even when the communication radius is irregular.     

伯努利滤波器在纯角度跟踪场景中的应用

所获得信息只包含角度信息的传感器被称为纯角度传感器，基于纯角度传感器的目标跟踪被称之为纯角度跟踪（Bearings-only Tracking，BOT）。BOT是目标跟踪领域的重要课题，在被动目标跟踪场景中能够发挥重要作用。伯努利滤波器（Bernoulli Filter，BF）是贝叶斯框架内的最优单目标滤波器，可以求得目标的存在概率和完整的后验概率密度函数，并判断目标出现和消失。作者将伯努利滤波器应用于纯角度跟踪场景下的单目标跟踪问题，提出了一种纯角度跟踪伯努利滤波器。在所提出的滤波器中，将目标相对于传感器的角度及其变化率作为状态矢量，用于估计目标是否存在；若目标存在，估计其状态信息。同时，还给出了所提出滤波器的粒子滤波（Particle Filter，PF）实现方法。仿真结果显示，与普通伯努利滤波器相比，所提出的纯角度跟踪伯努利滤波器能够更好地判断目标是否存在，同时滤波器对于目标估计的误差也更小。因此，所提出的滤波器具有更好的跟踪性能和更高的跟踪精度，能够有效应用于被动跟踪场景中。     

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.