Data fusion of decentralized local tracker outputs

An approach for fusing offboard track-level data at a central fusion node is presented. The case where the offboard tracker continues to update its local track estimate with measurement and system dynamics models that are not necessarily linear is considered. An algorithm is developed to perform this fusion at a central node without having access to the offboard measurements, their noise statistics, or the location of the local estimator. The algorithm is based on an extension of results that were originally established for linear offboard trackers. A second goal of this work is to develop an inequality constraint for selecting the proper sampling interval for the incoming state estimates to the fusion node. This interval is selected to allow use of conventional Kalman filter algorithms at the fusion node without suffering error performance degradation due to processing a correlated sequence of track state estimates     

On optimal track-to-track fusion

Track-to-track fusion is an important part in multisensor fusion. Much research has been done in this area. Chong et al. (1979, 1986, 1990) among others, presented an optimal fusion formula under an arbitrary communication pattern. This formula is optimal when the underlying systems are deterministic, i.e., the process noise is zero, or when full-rate communication (two sensors exchange information each time they receive new measurements) is employed. However, in practice, the process noise is not negligible due to target maneuvering and sensors typically communicate infrequently to save communication bandwidth. In such situations, the measurements from two sensors are not conditionally (given the previous target state) independent due to the common process noise from the underlying system, and the fusion formula becomes an approximate one. This dependence phenomena was also observed by Bar-Shalom (1981) where a formula was derived to compute the cross-covariance of two track estimates obtained by different sensors. Based on this results a fusion formula was subsequently derived (1986) to combine the local estimates which took into account the dependency between the two estimates. Unfortunately, the Bayesian derivation made an assumption that is not met. This work points out the implicit approximation made and shows that the result turns out to be optimal only in the ML (maximum likelihood) sense. A performance evaluation technique is then proposed to study the performance of various track-to-track fusion techniques. The results provide performance bounds of different techniques under various operating conditions which can be used in designing a fusion system.     

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     

基于PTE的多雷达航迹融合算法

针对杂波背景下的多雷达航迹融合时局部估计误差互协方差矩阵未知的问题,提出基于目标存在概率（PTE）的航迹融合算法,提升了正确航迹率和跟踪精度。首先,通过综合概率数据关联得到单接收站的目标航迹估计集合和对应的目标存在概率。然后,在局部估计误差互协方差矩阵未知的条件下,基于PTE信息提出不带记忆的综合广义凸组合航迹融合算法。进而将前一帧的融合状态进行反馈,提出带记忆的综合广义凸组合航迹融合算法。仿真验证了所提算法的有效性。     

Performance evaluation of track fusion with information matrixfilter

In a multisensor environment, each sensor detects multiple targets and creates corresponding tracks. Fusion of tracks from these, possibly dissimilar, sensors yields more accurate kinematic and attribute information regarding the target. Two methodologies have been employed for such purpose, which are: measurement fusion and state vector fusion. It is well known that the measurement fusion approach is optimal but computationally inefficient and the state vector fusion algorithms are more efficient but suboptimal, in general. This is so because the state vector estimates to be fused obtained from two sensors, are not conditionally independent in general due to the common process noise from the target being tracked. It is to be noted that there are three approaches to state vector fusion, which are: weighted covariance, information matrix, and pseudomeasurement. This research is restricted solely to performance evaluation of the information matrix form of state vector fusion. Closed-form analytical solution of steady state fused covariance has been derived as a measure of performance using this approach. Note that the results are derived under the assumptions that the two sensors are synchronized and no misassociation or merged measurement is considered in the study. Results are compared with those using Monte Carlo simulation, which was used in the past to predict fusion system performance by various authors. These results provide additional insight into the mechanism of track fusion and greatly simplify evaluation of fusion performance. In addition, availability of such a solution facilitates the trade-off studies for designing fusion systems under various operating conditions     

Interacting multiple model methods in target tracking: a survey

The Interacting Multiple Model (IMM) estimator is a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation schemes. The main feature of this algorithm is its ability to estimate the state of a dynamic system with several behavior modes which can “switch” from one to another. In particular, the IMM estimator can be a self-adjusting variable-bandwidth filter, which makes it natural for tracking maneuvering targets. The importance of this approach is that it is the best compromise available currently-between complexity and performance: its computational requirements are nearly linear in the size of the problem (number of models) while its performance is almost the same as that of an algorithm with quadratic complexity. The objective of this work is to survey and put in perspective the existing IMM methods for target tracking problems. Special attention is given to the assumptions underlying each algorithm and its applicability to various situations     

The modeling and estimation of asynchronous multirate multisensor dynamic systems

An asynchronous data fusion problem based on a kind of multirate multisensor dynamic system is studied. The system is observed by multirate sensors independently, with the state model known at the finest scale. Under the assumption that the sampling rates of sensors decrease successively by any positive integers, the discrete dynamic system models are established based on each single sensor and an asynchronous multirate multisensor state fusion estimation algorithm is presented. Theoretically, the estimate is proven to be unbiased and the optimal in the sense of linear minimum covariance, the fused estimate is better than the Kalman filtering results based on each single sensor, and the accuracy of the fused estimate will decrease if any of the sensors' information is neglected. The feasibility and effectiveness of the algorithm are shown through simulations.     

基于改进粒子群算法的航空发动机状态变量建模

为了克服现有航空发动机状态变量建模过程中的不足,采用了一种改进粒子群算法建立航空发动机状态变量模型。首先改进了粒子群算法,提出一种每个粒子根据自身适应值动态调整其惯性系数方法来平衡搜索性能;对群体最优位置进行实时的代内更新以提高搜索速度;为避免陷入局部最优,在最优个体附近进行随机搜索。其次利用该算法建立航空发动机状态变量模型,根据航空发动机在稳态点处的线性化模型应与在该同一稳态工作点处的非线性模型响应一致的原则构造适应值函数,仿真结果表明所建立的状态变量模型不论是稳态过程还是动态过程都与非线性模型响应基本一致,建模精度较高,建立过程简便。     

Centralized and distributed multisensor integration withuncertainties in communication networks

Algorithms in which each sensor is represented in a local coordinate system and the communication networks between sensors have uncertainties are considered. The algorithms are general and can be applied to various integration tasks. The effects of the communication network uncertainties are minimized in the local estimation and central fusion processes. In the centralized multisensor integration, the local measurements and local measurement models are transferred to the central coordinate system and the optimal integration is obtained at the central process. In contrast, the local measurements, together with the previous central estimate transmitted from the communication network, are locally processed in the distributed multisensor integration algorithm. Because the distributed algorithm uses the communication networks twice, more errors are introduced, so that when the uncertainties are large, the centralized algorithm is preferred. Although the algorithms are developed in the three-dimensional coordinate system, with straightforward extension they can be applied to N-dimensional coordinate systems     

Track-to-track fusion with dissimilar sensors

An analysis is described of a kinematic state vector fusion algorithm when tracks are obtained from dissimilar sensors. For the sake of simplicity, it is assumed that two dissimilar sensors are equipped with nonidentical two-dimensional optimal linear Kalman filters. It is shown that the performance of such a track-to-track fusion algorithm can be improved if the cross-correlation matrix between candidate tracks is positive. This cross-correlation is introduced by noise associated with target maneuver that is common to the tracking filters in both sensors and is often neglected. An expression for the steady state cross-correlation matrix in closed form is derived and conditions for positivity of the cross-correlation matrix are obtained. The effect of positivity on performance of kinematic track-to-track fusion is also discussed     

受地效影响飞机起飞着陆运动模型的参数辨识

利用基于最小模型误差法和线性不连续跳跃多重打靶法建立的非线性辨识法，辨识了飞机起飞着陆过程的非线性动态模型。对于包含复杂非线性项的动态系统，本方法可以从实际试验测量的系统非线性数据，确定飞机处于地面效应影响运动过程的系统模型，而不需要预先详细描述系统的非线性形式。算例表明该方法对于原始近似动态系统的状态估计是足够精确的。     

Decoding techniques in state estimation for dynamic systems withpast histories

States of dynamic models with a higher order memory are estimated using both a stack sequential decoding algorithm and the Viterbi decoding algorithm (VDA), without higher dimensional dynamic system representation. This results in memory reduction for state estimate implementation. It is found that state estimation with a stack sequential decoding algorithm is faster and more practical than the state estimation with the Viterbi decoding algorithm, even though the estimates obtained by the Viterbi decoding algorithm are superior     

Fault Detection in Dynamic Systems via Decision Fusion

Due to the growing demands for system reliability and availability of large amounts of data, efficient fault detection techniques for dynamic systems are desired. In this paper, we consider fault detection in dynamic systems monitored by multiple sensors. Normal and faulty behaviors can be modeled as two hypotheses. Due to communication constraints, it is assumed that sensors can only send binary data to the fusion center. Under the assumption of independent and identically distributed (1ID) observations, we propose a distributed fault detection algorithm, including local detector design and decision fusion rule design, based on state estimation via particle filtering. Illustrative examples are presented to demonstrate the effectiveness of our approach.     

基于双模型滤波算法的环控系统换热器故障诊断

 在建立飞机环控系统数学模型的基础上,提出采用双模型滤波方法进行参数估计、状态预测和故障诊断,提高飞机环控系统故障诊断的快速性和准确性。如果采用最小二乘算法,参数估计是静态的,故障诊断延迟一般较大;采用单模型扩展Kalman滤波算法,虽然能够实现动态估计,但不能同时兼顾稳态过程和过渡过程（突发故障）的参数估计,导致误差较大。为了解决上述难题,针对飞机环控系统换热器故障诊断,提出两模型滤波算法。该算法由两个滤波器组成,分别用于跟踪系统的稳态和过渡过程。由于采用了两滤波器模型分别匹配不同的系统特征,能够改善飞机环控系统不同状态下的参数估计和状态预测性能,从而提高系统故障诊断的精度和速度。仿真结果证实了该算法的有效性。     

Observer-type Kalman innovation filter for uncertain linear systems

An observer-type of Kalman innovation filtering algorithm to find a practically implementable "best" Kalman filter, and such an algorithm based on the evolutionary programming (EP) optima-search technique, are proposed, for linear discrete-time systems with time-invariant unknown-but-hounded plant and noise uncertainties. The worst-case parameter set from the stochastic uncertain system represented by the interval form with respect to the implemented "best" filter is also found in this work for demonstrating the effectiveness of the proposed filtering scheme. The new EP-based algorithm utilizes the global optima-searching capability of EP to find the optimal Kalman filter and state estimates at every iteration, which include both the best possible worst case Interval and the optimal nominal trajectory of the Kalman filtering estimates of the system state vectors. Simulation results are included to show that the new algorithm yields more accurate estimates and is less conservative as compared with other related robust filtering schemes     

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...     

一种带随机偏差的分布式信息融合滤波算法

在通信、计算机、信号处理、自动控制中,对于带有未知的干扰和偏差的随机系统的状态估计已经广泛出现。在现实环境中,不同的传感器可能受到不同的干扰影响。研究随机系统的状态估计问题在实际应用中具有重要的意义。对带有随机偏差的线性随机系统,将系统转换为多模型结构的特殊情况。利用最小方差的最优加权融合估计算法,获得了分布式信息融合滤波算法。通过仿真可以看出,分布式信息融合算法要比局部估计算法具有更高的精度,算法具有分布式结构,这使其具有更好的鲁棒性和可靠性。     

Blind adaptive decision fusion for distributed detection

We consider the problem of decision fusion in a distributed detection system. In this system, each detector makes a binary decision based on its own observation, and then communicates its binary decision to a fusion center. The objective of the fusion center is to optimally fuse the local decisions in order to minimize the final error probability. To implement such an optimal fusion center, the performance parameters of each detector (i.e., its probabilities of false alarm and missed detection) as well as the a priori probabilities of the hypotheses must be known. However, in practical applications these statistics may be unknown or may vary with time. We develop a recursive algorithm that approximates these unknown values on-line. We then use these approximations to adapt the fusion center. Our algorithm is based on an explicit analytic relation between the unknown probabilities and the joint probabilities of the local decisions. Under the assumption that the local observations are conditionally independent, the estimates given by our algorithm are shown to be asymptotically unbiased and converge to their true values at the rate of O(1/k/sup 1/2/) in the rms error sense, where k is the number of iterations. Simulation results indicate that our algorithm is substantially more reliable than two existing (asymptotically biased) algorithms, and performs at least as well as those algorithms when they work.     

A quantization architecture for track fusion

Many practical multi-sensor tracking systems are based on some form of track fusion, in which local track estimates and their associated covariances are shared among sensors. Communication load is a significant concern, and the goal of this paper is to propose an architecture for low-bandwidth track fusion. The scheme involves intelligent scalar and vector quantization of the local state estimates and of the associated estimation error covariance matrices. Simulation studies indicate that the communication saving can be quite significant, with only minor degradation of track accuracy.     

Relay selection based on MAP estimation for cooperative communication with outdated channel state information

In this paper, we consider an amplify-and-forward (AF) cooperative communication system when the channel state information (CSI) used in relay selection differs from that during data transmission, i.e., the CSI used in relay selection is outdated. The selected relay may not be actually the best for data transmission and the outage performance of the cooperative system will deteriorate. To improve its performance, we propose a relay selection strategy based on maximum a posteriori (MAP) estimation, where relay is selected based on predicted signal-to-noise ratio (SNR). To reduce the computation complexity, we approximate the a posteriori probability density of SNR and obtain a closed-form predicted SNR, and a relay selection strategy based on the approximate MAP estimation (RS-AMAP) is proposed. The simulation results show that this approximation leads to trivial performance loss from the perspective of outage probability. Compared with relay selection strategies given in the literature, the outage probability is reduced largely through RS-AMAP for medium-to-large transmitting powers and medium-to-high channel correlation coefficients.