Recursive temporal-spatial information fusion with applications totarget identification

Centralized/distributed recursive algorithms for temporal-spatial information integration using the Dempster-Shafer technique are developed. Compared with the Bayesian approach, the Dempster-Shafer technique has the strong capability of handling information uncertainties, which are particularly desirable in many applications. In the centralized integration algorithm, all information is pooled into the central processor and integrated. In contrast, the distributed integration algorithm shares the computational burden among the local processors, which increases the computational efficiency. The developed algorithms are effectively applied to a target identification problem with three sensors: identification of friend-foe-neutral (IFFN), electronic support measurement (ESM), and infrared search and track (IRST)     

多传感器融合状态估计方法综述

随着传感器网络技术的发展,多传感器融合状态估计凭借其鲁棒性、灵活性、可扩展性以及便于故障检测等优点,长期受到国内外学者的广泛关注,并取得了大量研究成果。数据融合的方法为融合状态估计奠定了理论基础,也是早期研究的主要方向,从20世纪70年代到20世纪末,相继发展出了集中式和分散式滤波架构及相应算法。无线通信技术的成熟以及一致性算法的出现使得分布式状态估计的研究进入了快车道,自2005年以来,大量基于一致性的分布式滤波算法被提出,其中不乏实用的经典方法和优秀的开创性方法。旨在梳理多传感器融合状态估计的发展,探究从数据融合到分布式滤波的内在联系,并对一些经典方法进行了总结。     

基于最近邻的集中式多传感器多目标跟踪算法

经典的集中式多传感器多目标跟踪算法通常计算量较大,经常难以满足系统的实时性要求,工程上实现起来比较困难,为进一步扩大集中式多传感器的应用范围,使其在对算法实时性要求较高、跟踪精度要求较小的实际场合中广泛应用。文章基于最近邻域思想,研究了并行处理结构的集中式多传感器最近邻域算法,并从算法跟踪精度、实时性、有效跟踪率3个方面对其与经典的顺序多传感器联合概率数据互联算法进行了仿真比较。经仿真验证,并行处理结构的集中式多传感器最近邻域算法实时性提高了60%以上,且在跟踪背景杂波适中的情况下能够有效跟踪目标。     

非线性系统中多传感器目标跟踪融合算法研究

 研究了在非线性系统中 ,基于转换坐标卡尔曼滤波器的多传感器目标跟踪融合算法。通过分析得出 :在非线性系统的多传感器目标跟踪中 ,基于转换坐标卡尔曼滤波器 ( CMKF)的分布融合估计基本可以重构中心融合估计。仿真实验也证明了此结论。由此可见分布的 CMKFA是非线性系统中较优的分布融合算法     

Update with out-of-sequence measurements in tracking: exact solution

In target tracking systems measurements are typically collected in "scans" or "frames" and then they are transmitted to a processing center. In multisensor tracking systems that operate in a centralized manner, there are usually different time delays in transmitting the scans or frames from the various sensors to the center. This can lead to situations where measurements from the same target arrive out of sequence. Such "out-of-sequence" measurement (OOSM) arrivals can occur even in the absence of scan/frame communication time delays. The resulting "negative-time measurement update" problem, which is quite common in real multisensor systems, was solved previously only approximately in the literature. The exact state update equation for such a problem is presented. The optimal and two suboptimal algorithms are compared on a number of realistic examples, including a GMTI (ground moving target indicator) radar case.     

通信和测量受限条件下异构多UAV分布式协同目标跟踪方法

研究了通信和测量受限的异构多无人机(UAV)网络化分布式协同目标观测与跟踪问题.该分布式UAV系统采用长机一僚机异构型网络结构,以实现在电子静默和战术隐身条件下扩大探测和打击纵深.提出改进的一致性信息滤波(ICF)算法,实现通信和测量范围内各UAV节点的分布式信息融合.由于一致性算法的收敛性与网络拓扑结构的连通性密切相...     

Fusion of distributed extended forgetting factor RLS state estimators

For single-target multisensor systems, two fusion methods are presented for distributed recursive state estimation of dynamic systems without knowledge of noise covariances. The estimator at every local sensor embeds the dynamics and the forgetting factor into the recursive least squares (RLS) method to remedy the lack of knowledge of noise statistics, developed before as the extended forgetting factor recursive least squares (EFRLS) estimator. It is proved that the two fusion methods are equivalent to the centralized EFRLS that uses all measurements from local sensors directly and their good performance is shown by simulation examples.     

Efficient multisensor fusion using multidimensional dataassociation

We present the development of a multisensor fusion algorithm using multidimensional data association for multitarget tracking. The work is motivated by a large scale surveillance problem, where observations from multiple asynchronous sensors with time-varying sampling intervals (electronically scanned array (ESA) radars) are used for centralized fusion. The combination of multisensor fusion with multidimensional assignment is done so as to maximize the “time-depth” in addition to “sensor-width” for the number S of lists handled by the assignment algorithm. The standard procedure, which associates measurements from the most recently arrived S-1 frames to established tracks, can have, in the case of S sensors, a time-depth of zero. A new technique, which guarantees maximum effectiveness for an S-dimensional data association (S⩾3), i.e., maximum time-depth (S-1) for each sensor without sacrificing the fusion across sensors, is presented. Using a sliding window technique (of length S), the estimates are updated after each frame of measurements. The algorithm provides a systematic approach to automatic track formation, maintenance, and termination for multitarget tracking using multisensor fusion with multidimensional assignment for data association. Estimation results are presented for simulated data for a large scale air-to-ground target tracking problem     

Removal of Out-of-Sequence Measurements from Tracks

In multisensor tracking systems that operate in a centralized or distributed information processing architecture, measurements from the same target obtained by different sensors can arrive at the processing center out of sequence due to system latencies. In order to avoid either a delay in the output or the need for reordering and reprocessing entire sequences of measurements, such latent measurements have to be processed by the tracking filter as out-of-sequence measurements (OOSM). Recent work developed a "one-step" procedure for incorporating OOSM with multiple-time-step latency into the tracking filter, which, while suboptimal, was shown to yield results very close to those obtained by reordering and reprocessing an entire sequence of measurements. The counterpart of this problem is the need to remove (revocate) measurements that have already been used to update a track state. This can happen in real-world systems when such measurements are reassigned to another track. Similarly to the problem of update with an OOSM, it is desired to carry out the removal of an earlier measurement without recomputing the track estimate (and the data association) using possibly a long sequence of subsequent measurements one at a time. A one-step algorithm is presented for this problem of removing a multistep OOSM.     

Hybrid partition- and network-level scheduling design for distributed integrated modular avionics systems

Distributed Integrated Modular Avionics (DIMA) develops from Integrated Modular Avionics (IMA) and realizes distributed integration of multiple sub-function areas. Time-triggered network provides effective support for time synchronization and information coordination in DIMA systems. However, inconsistency between processing resources and communication network destroys the time determinism benefiting from partitions and time-triggered mechanism. To ensure such time determinism and achieve guaranteed real-time performance, system design should collectively provide a global communication scheme for messages in network domain and a corresponding execution scheme for partitions in processing domain. This paper firstly establishes a general DIMA model which coordinates partitioned processing and time-triggered communication, and then proposes a hybrid scheduling algorithm using Mixed Integer Programming to produce feasible system schemes. Furthermore, incrementally integrating new functions causes upgrades or reconfigurations of DIMA systems and will generate integration cost. To control such cost, this paper further develops an optimization algorithm based on Maximum Satisfiability Problem and guarantees that the scheduling design for upgraded DIMA systems inherit their original schemes as much as possible. Finally, two typical cases, including a simple fully connected DIMA system case and an industrial DIMA system case, are constructed to illustrate our DIMA model and validate the effectiveness of our hybrid scheduling algorithms.     

神经网络在广义经典分配问题中的应用

在两节点分布式多传感器系统中,一些航迹关联算法可以化为广义经典分配问题。广义经典分配问题是一个组合优化问题。当目标数目较多时,很难得到问题的最优解,而且其计算量容易呈现指数爆炸现象。文章提出了用Hopfield神经网络和平均场网络解决此问题的方法。仿真结果表明,采用文章提出的人工神经网络模型求解广义经典分配问题,不仅使航迹关联具有较高的关联正确率,而且计算时间不会出现指数爆炸现象。仿真结果还表明,平均场网络相比Hopfield神经网络更易于得到问题的最优解。     

基于神经网络的广义经典分配航迹关联算法

在序贯航迹关联算法的基础上提出了一种广义经典分配航迹关联算法。此算法实际上是求约束条件下的函数最小值问题,属于组合优化问题,其计算复杂度随着目标数的增加而发生爆炸现象。根据Hop field神经网络模型解决此类问题的能力对此广义经典问题进行了求解。仿真实验结果表明,广义经典分配算法能够有效地解决航迹关联问题,而且用神经网络求解此问题降低了计算复杂性,并具有很高的关联正确率。     

自主多无人机的分散化协同控制

协同前提是无人机(UAV)平台间的通信和信息共享,无人机平台之间信息和计算是高度分布的,无人机平台的运动以及通信拓扑的变化,使得集中式协调控制结构很难实现。以最小通信量为基础的分散协同控制具有可扩展性、异构性和动态可重构性等特点,可靠性和鲁棒性较好。针对多无人机平台分散化协同的特点和要求,建立了集中和分散相结合的多无人机平台协同控制系统结构,集中式任务管理系统主要完成目标分配、通信管理和编队管理功能,分散式协同部分主要实现局部任务规划、协调策略及协调控制等功能。分别以多机协同目标跟踪、多机和多编队一致性协调、多机协同编队控制与重构等多无人机平台分散化协同控制技术为应用对象,探讨了分散化协调机制、策略、控制及其与信息之间关系。给出了部分算法的仿真结果。     

基于合同机制的多UCAV分布式协同任务控制

 针对集中式控制的不足,采用基于合同机制的分布式控制方法对多无人作战飞机（UCAV）协同任务控制问题开展研究。设计了有限中央控制下的分布式控制体系结构,以支持UCAV在任务控制站的监控下实现不同级别上的自治。在通过合同网协议实现动态任务分配的基础上,建立条件合同机制对任务执行过程进行协调,提高了多UCAV协同作战的效能。采用时间约束网络对UCAV的任务计划进行量化表达,保证了多UCAV战术行为配合的准确实现。仿真结果验证了方法的有效性。     

One-step solution for the multistep out-of-sequence-measurement problem in tracking

In multisensor target tracking systems measurements from the same target can arrive out of sequence. Such "out-of-sequence" measurement (OOSM) arrivals can occur even in the absence of scan/frame communication time delays. The resulting problem - how to update the current state estimate with an "older" measurement - is a nonstandard estimation problem. It was solved first (suboptimally, then optimally) for the case where the OOSM lies between the two last measurements, i.e, its lag is less than a sampling interval - the 1-step-lag case. The real world has, however, OOSMs with arbitrary lag. Subsequently, the suboptimal algorithm was extended to the case of an arbitrary (multistep) lag, but the resulting algorithm required a significant amount of storage. The present work shows how the 1-step-lag algorithms can be generalized to handle an arbitrary (multistep) lag while preserving their main feature of solving the update problem without iterating. This leads only to a very small (a few percent) degradation of MSE performance. The incorporation of an OOSM into the data association process is also discussed. A realistic example with two GMTI radars is presented. The consistency of the proposed algorithm is also evaluated and it is found that its calculated covariances are reliable.     

IMM estimator with out-of-sequence measurements

In multisensor tracking systems that operate in a centralized information processing architecture, measurements from the same target obtained by different sensors can arrive at the processing center out of sequence. In order to avoid either a delay in the output or the need for reordering and reprocessing an entire sequence of measurements, such measurements have to be processed as out-of-sequence measurements (OOSMs). Recent work developed procedures for incorporating OOSMs into a Kalman filter (KF). Since the state of the art tracker for real (maneuvering) targets is the interacting multiple model (IMM) estimator, the algorithm for incorporating OOSMs into an IMM estimator is presented here. Both data association and estimation are considered. Simulation results are presented for two realistic problems using measurements from two airborne GMTI sensors. It is shown that the proposed algorithm for incorporating OOSMs into an IMM estimator yields practically the same performance as the reordering and in-sequence reprocessing of the measurements. Also, it is shown how the range rate from a GMTI sensor can be used as a linear velocity measurement in the tracking filter.     

某涡扇发动机分布式控制系统设计与总线性能

针对国内某型涡扇发动机,以该机目前采用的集中式FADEC为基础,依据地域分散原则,设计了一种发动机部分分布式控制系统结构,并对发动机控制系统中的传输信号进行了合理划分;对发动机采用分布式控制进行了总线通信需求分析;选择时间触发CAN总线作为发动机分布式控制系统通信总线,并进行了通信方案设计;利用TrueTime构建了分布式控制仿真系统,研究了网络时延、掉包对发动机性能的影响.研究表明:在250kbit/s和1Mbit/s带宽下,总线利用率分别为50%和12.34%,满足通信要求;250kbit/s带宽下所设计时间触发系统的网络诱导时延为12ms,高压转子转速上升时间延迟0.02s,而网络随机掉包使发动机响应明显变慢,设计时需要加以考虑.      

Consensus based on learning game theory with a UAV rendezvous application

Multi-agent cooperation problems are becoming more and more attractive in both civilian and military applications. In multi-agent cooperation problems, different network topologies will decide different manners of cooperation between agents. A centralized system will directly control the operation of each agent with information flow from a single centre, while in a distributed system, agents operate separately under certain communication protocols. In this paper, a systematic distributed optimization approach will be established based on a learning game algorithm.The convergence of the algorithm will be proven under the game theory framework. Two typical consensus problems will be analyzed with the proposed algorithm. The contributions of this work are threefold. First, the designed algorithm inherits the properties in learning game theory for problem simplification and proof of convergence. Second, the behaviour of learning endows the algorithm with robustness and autonomy. Third, with the proposed algorithm, the consensus problems will be analyzed from a novel perspective.     

Federated square root filter for decentralized parallel processors

An efficient, federated Kalman filter is developed for use in distributed multisensor systems. The design accommodates sensor-dedicated local filters, some of which use data from a common reference subsystem. The local filters run in parallel, and provide sensor data compression via prefiltering. The master filter runs at a selectable reduced rate, fusing local filter outputs via efficient square root algorithms. Common local process noise correlations are handled by use of a conservative matrix upper bound. The federated filter yields estimates that are globally optimal or conservatively suboptimal, depending upon the master filter processing rate. This design achieves a major improvement in throughput (speed), is well suited to real-time system implementation, and enhances fault detection, isolation, and recovery capability     

Crosslink slot assignment in LEO networks

The problem of crosslink time slot assignment in low Earth orbiting (LEO) satellite networks is addressed. A crosslink channel connects two neighboring satellites through half-duplexed directional antennas. Coordination among the crosslinks between a satellite and all its neighbors in a dynamic LEO network is a delicate problem and rarely addressed in the literature. The problem is solved here using bipartite graphs. Two versions of slot assignment algorithm are given: distributed and centralized.