多传感器数据融合与航迹预测

多传感器数据融合技术是未来军事电子领域一个重要趋势.根据6个观测雷达的观测数据进行了数据融合算法的研究.在提取目标航迹对时,对每个雷达的数据依据一定的判定条件(时间变化,角度变化在一定范围内等),分别提取出不同的目标航迹对.在提取同一目标的航迹对时,先将目标航迹的一些异常点弃除,然后把时间重合的两段航迹提取出来,通过样条插值进行时间配准,共提取出多条相关的航迹组有3组.在使用雷达探测目标时,由于技术条件和方法等的限制,使雷达数据存在各种误差.利用卡尔曼滤波自适应算法估计出观测位置的噪声方差,对雷达偏差进行修正后,采用联合卡尔曼滤波算法对多条航迹进行融合,接着利用ARMA模型预测目标在未来10秒内的轨迹,最后,对目标在被锁定后的轨迹做出预测,结合导弹的爆炸范围求得导弹击中飞机的概率约为49.54%.     

A robust tracking method with adaptive local spatial sparse representation

In this paper, a robust visual tracking method is proposed based on local spatial sparse representation. In the proposed approach, the learned target template is sparsely and compactly expressed by forming local spatial and trivial samples dynamically. An adaptive multiple subspaces appearance model is developed to describe the target appearance and construct the candidate target templates during the tracking process. An effective selection strategy is then employed to select the optimal sparse solution and locate the target accurately in the next frame. The experimental results have demonstrated that our method can perform well in the complex and noisy visual environment, such as heavy occlusions, dramatic illumination changes, and large pose variations in the video. Copyright © 2015 John Wiley & Sons, Ltd.     

多雷达系统数据融合与航迹预测

为了便于准确定位传感器网络,分布式的单雷达系统首先进行各自的数据处理,包括:地理坐标换算至平面直角坐标;剔除孤立的异常点迹;采用模糊c-均值聚类方法和"动态分区",将单雷达数据中属于同目标的相似点迹归类集合;根据雷达观测和目标运动的特征,在每个点迹集合中设计门限滤波和相关矩阵检验,提取完整连续的目标运动的航迹;结合各航迹特征进行种类分析.接下来对属于不同雷达的航迹两两比较,找出有相交时间段的航迹,采用三次样条对两条航迹的进行内插和外推,再通过模糊综合函数对这两条航迹给出一个相似性度量,并取阈值为0.85.最后得出雷达间各航迹匹配关系.通过该雷达所观测到的航迹的稳定程度来近似估计其观察精度.首先对每一条航迹进行分段拟合得到其剩余方差,然后直接用每一条航迹的剩余方差来衡量雷达的观察精度,最后我们得出雷达的精度排序29107728,7724253720252539.对航迹融合,我们首先采用D-S证据理论并利用分析得到的雷达精度,对表示同一目标的航迹对进行融合.其次试图运用卡尔曼滤波对航迹进行融合:思路一是设法离线估计出噪声矩阵,得出系统噪声方差矩阵和观测噪声方差矩阵,从而用于标准卡尔曼滤波方程;思路二是探究较为实用的自适应滤波,兼顾Sage-Husa自适应滤波算法的高精度与强跟踪自适应滤波算法的可靠性,采用了一种混合算法给出收敛的估计.最终给出了雷达7728和2910的融合算例以及10秒钟的预测轨迹.最后,我们将导弹拦截飞机建模为三维的追逃问题,建立了运动学关系方程,最终归结为最小能量导引律问题.采用"模糊T-S线性模型"以及RH控制方法和伴随技术,在目标作对抗性机动条件下,获得了一个有效拦截的导引律.还对多雷达系统平均处理周期、数据融合系统的航迹处理周期进行了分析,对雷达网络实时性做了评价.     

Pattern recognition based Kalman filter for indoor localization using TDOA algorithm

In this paper, we will present a motion pattern recognition based Kalman filter (PRKF), and apply it to the time difference of arrival (TDOA) algorithm of indoor localization. The state matrix in Kalman filter (KF) is determined by the motion pattern which the target node is supposed to act, and this will bring new system error if the assumption is not correct. Considering this, we first create three fuzzy sets using three KFs whose state matrix stand for different motion patterns, then linearly combined the memberships of a target node of the fuzzy sets. Finally, simulation results show that the PRKF can enhance the localization accuracy about more than 20%.     

基于粒子滤波算法的航迹误差分析

当前针对飞行预测的研究主要采用的是kalman算法,在解决非线性问题时存在着只能近似线性的而不够精确的问题.采用近年来受到广泛关注的粒子滤波算法,针对RNAV航路进行分析,结论中得到了对飞行误差仿真分析并对比了卡尔曼滤波仿真效果,证实了粒子滤波在航迹预测中更好的准确性.     

Unscented fuzzy-controlled current statistic model and adaptive filtering for tracking maneuvering targets

A novel adaptive algorithm for tracking maneuvering targets is proposed. The algorithm is implemented with fuzzy-controlled current statistic model adaptive filtering and unscented transformation. A fuzzy system allows the filter to tune the magnitude of maximum accelerations to adapt to different target maneuvers, and unscented transformation can effectively handle nonlinear system. A bearing-only tracking scenario simulation results show the proposed algorithm has a robust advantage over a wide range of maneuvers and overcomes the shortcoming of the traditional current statistic model and adaptive filtering algorithm.     

Peer-to-Peer环境下基于分布式Kalman滤波的信用管理

Peer-to-Peer(P2P)环境下的信用管理对鼓励节点间的资源共享和抵制恶意节点的行为有重要的作用.针对目前P2P信用管理系统中全局信用值需要迭代计算,网络通信开销较大等问题,借鉴多传感器目标跟踪中的信息融合思想,提出了一种新的P2P环境下信用管理机制.建立了节点信用变化方程和测量方程,给出了节点全局信用值的分布式Kalman滤波估计方法,并讨论了恶意节点抑制问题.理论分析和仿真计算表明本文方法的信用值计算精度高、收敛速度快、资源开销小、对动态节点的适应性强并具有很好的可扩充性.     

基于方根分解形式的带衰减因子UKF算法应用

UKF作为一种新的非线性滤波方法已在目标跟踪问题中得到应用,在状态的时间更新阶段直接使用非线性模型,不引入线性化误差,而且不必计算Jacobians矩阵.提出了一种基于方根分解形式的带有衰减因子的UKF算法(SRDMA-UKF),算法的方根形式增加了数字稳定性和状态协方差的半正定性.通过衰减因子的引入加强对当前测量数据的利用,减小历史数据对滤波的影响.仿真实验结果表明,该算法与UKF算法相比具有更好的滤波性能.     

地震动瞬时谱估计的UnscentedKalman滤波方法

用时变ARMA模型描述地震动时程,提出了采用Unscented Kalman滤波技术实现地震动瞬时谱估计的思路．算例分析表明,Unscented Kalman滤波方法较Kalman滤波方法适用范围广,具有较高的时间和频率分辨率,能够更好地跟踪地震动的局部特性,适合处理非线性模型或有突变特性的模型的辨识问题．不同阶数ARMA模型的估计结果还表明,以往被忽略的ARMA模型的理论频率分辨力对地震动瞬时谱估计精度有重要影响,应作为一个参考指标在ARMA模型的判阶中加以考虑．     

A new fuzzy based algorithm for solving stereo vagueness in detecting and tracking people

This paper describes a system capable of detecting and tracking various people using a new approach based on colour, stereo vision and fuzzy logic. Initially, in the people detection phase, two fuzzy systems are used to filter out false positives of a face detector. Then, in the tracking phase, a new fuzzy logic based particle filter (FLPF) is proposed to fuse stereo and colour information assigning different confidence levels to each of these information sources. Information regarding depth and occlusion is used to create these confidence levels. This way, the system is able to keep track of people, in the reference camera image, even when either stereo information or colour information is confusing or not reliable. To carry out the tracking, the new FLPF is used, so that several particles are generated while several fuzzy systems compute the possibility that some of the generated particles correspond to the new position of people. Our technique outperforms two well known tracking approaches, one based on the method from Nummiaro et al. [1] and other based on the Kalman/meanshift tracker method in Comaniciu and Ramesh [2]. All these approaches were tested using several colour-with-distance sequences simulating real life scenarios. The results show that our system is able to keep track of people in most of the situations where other trackers fail, as well as to determine the size of their projections in the camera image. In addition, the method is fast enough for real time applications.     

Track initiation with ant colony optimization

Since the issue of track initiation belongs to the NP-hard problem in the bearings-only multi-sensor-multi-target tracking system, a novel proposed track initiation technique is proposed in this paper. The proposed track initiation technique is based upon an ant colony optimization (ACO) algorithm, a kind of heuristic optimization method. Observing that each target is of the characteristic of uniform rectilinear motion, we develop a new cost function derived from the thought of Hough transform. Numerical simulation results show that the proposed ACO-based track initiation method not only meets the requirement of real time, but also performs better than other traditional techniques, especially in the scenario that all targets move in parallel.     

Comparison of adaptive filters for gas turbine performance monitoring

Kalman filters are widely used in the turbine engine community for health monitoring purpose. This algorithm has proven its capability to track gradual deterioration with a good accuracy. On the other hand, its response to rapid deterioration is either a long delay in recognising the fault, and/or a spread of the estimated fault in several components. The main reason of this deficiency lies in the transition model of the parameters that assumes a smooth evolution of the engine’s condition. The aim of this contribution is to compare two adaptive diagnosis tools that combine a Kalman filter and a secondary system that monitors the residuals. This auxiliary component implements on one hand a covariance matching scheme and on the other hand a generalised likelihood ratio test to improve the behaviour of the diagnosis tool with respect to abrupt faults.     

Fuzzy variant of a statistical test point Kalman filter

In this paper, we propose the conceptual use of fuzzy clustering techniques as iterative spatial methods to estimate a posteriori statistics in place of the weighted averaging scheme of the Unscented Kalman filter. Specifically, instead of a linearization methodology involving the statistical linear regression of the process and measurement functions through some deterministically chosen set of test points (sigma points) contained within the “uncertainty region” around the state estimate, we present a variant of the Unscented transformation involving fuzzy clustering techniques which will be applied to the test points yielding “degrees of membership” in which Gaussian shapes can be “fit” using a least squares scheme. Implementation into the Kalman methodology will be shown along with simple state and parameter estimation examples.     

Hybrid estimation algorithms

The optimal, mean-square estimate of the state of a hybrid system is difficult to determine because the equations of state evolution are nonlinear and non-Gaussian. When there is a direct, albeit noisy, measurement of the modal state, it is possible to derive a useful approximation to the optimal estimator. This simplified algorithm is tested on a target tracking problem, and is seen to be superior to the conventional extended Kalman filter.This research was partially supported by a grant from the Hughes Aircraft Company and by the MICRO Program of the State of California under Project No. 91-156.     

Multidimensional assignment formulation of data association problems arising from multitarget and multisensor tracking

The ever-increasing demand in surveillance is to produce highly accurate target and track identification and estimation in real-time, even for dense target scenarios and in regions of high track contention. The use of multiple sensors, through more varied information, has the potential to greatly enhance target identification and state estimation. For multitarget tracking, the processing of multiple scans all at once yields high track identification. However, to achieve this accurate state estimation and track identification, one must solve an NP-hard data association problem of partitioning observations into tracks and false alarms in real-time. The primary objective in this work is to formulate a general class of these data association problems as multidimensional assignment problems to which new, fast, near-optimal, Lagrangian relaxation based algorithms are applicable. The dimension of the formulated assignment problem corresponds to the number of data sets being partitioned with the constraints defining such a partition. The linear objective function is developed from Bayesian estimation and is the negative log posterior or likelihood function, so that the optimal solution yields the maximum a posteriori estimate. After formulating this general class of problems, the equivalence between solving data association problems by these multidimensional assignment problems and by the currently most popular method of multiple hypothesis tracking is established. Track initiation and track maintenance using anN-scan sliding window are then used as illustrations. Since multiple hypothesis tracking also permeates multisensor data fusion, two example classes of problems are formulated as multidimensional assignment problems.This work was partially supported by the Air Force Office of Scientific Research through AFOSR Grant Numbers AFOSR-91-0138 and F49620-93-1-0133 and by the Federal Systems Company of the IBM Corporation in Boulder, CO and Owego, NY.     

基于单传感器多模型的多分辨目标跟踪融合算法

Multiresolutional signal processing has been employed in image processing and computer vision to achieve improved performance that cannot be achieved using conventional signal processing techniques at only one resolution level[1,2,5,6]. In this paper,we have associated the thought of multiresolutional analysis with traditional Kalman filtering and proposed A new fusion algorithm based on singular Sensor and Multipale Models for maneuvering target tracking.     

Hybrid Estimation Algorithms,Part 2

The equations of state evolution of a hybrid system are nonlinear and generate non-Gaussian sample paths. For this reason, the optimal, mean-square estimate of the state is difficult to determine. In an earlier paper (Ref. 1), a useful approximation to the optimal estimator was derived for the case where there is a direct, albeit noisy, measurement of the modal state. Although this algorithm has proven serviceable, it is restricted to applications in which the base-state path is continuous. In this paper, the result is extended to the case in which there are base-state discontinuities of a particular sort. The algorithm is tested on a target tracking problem and is shown to be superior to both the extended Kalman filter and the estimator derived in Ref. 1.     

An affine scaling interior trust-region method combining with line search filter technique for optimization subject to bounds on variables

This paper proposes and analyzes an affine scaling trust-region method with line search filter technique for solving nonlinear optimization problems subject to bounds on variables. At the current iteration, the trial step is generated by the general trust-region subproblem which is defined by minimizing a quadratic function subject only to an affine scaling ellipsoidal constraint. Both trust-region strategy and line search filter technique will switch to trail backtracking step which is strictly feasible. Meanwhile, the proposed method does not depend on any external restoration procedure used in line search filter technique. A new backtracking relevance condition is given which is weaker than the switching condition to obtain the global convergence of the algorithm. The global convergence and fast local convergence rate of this algorithm are established under reasonable assumptions. Preliminary numerical results are reported indicating the practical viability and show the effectiveness of the proposed algorithm.     

Multiresolutional Maneuvering Target Tracking Fusion Algorithm Based on Singular Sensor and Multipale Models

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