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     

网络攻击下无人机信息物理系统的自适应状态估计

针对网络攻击下无人机信息物理系统(CPS)的安全状态估计问题,提出了一种基于自适应方差极小化的递推状态估计器(AVMRE)。通过将针对控制输入和传感器数据的恶意攻击分别建模为状态和量测方程中的未知干扰项,建立了未知干扰解耦状态递推估计器,实现滤波误差中的量测未知干扰解耦,利用滤波残差设计自适应调整因子对估计误差上界进行极小化,应用最小方差估计准则求解出算法中的量测增益反馈矩阵。同时引入事件触发机制,使得系统在保持一定估计精度的情况下节省通信资源。此外,给出了滤波误差指数有界性的充分条件。无人机飞行模型仿真验证了本文算法相比传统算法的有效性和优越性。     

Relay selection based on MAP estimation for cooperative communication with outdated channel state information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(CSI) used in relay selection differs from that during data transmission, i.e., the CSI used in relay selection is outdated.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.     

双重未知干扰解耦的多传感器系统偏差校正与状态估计

具有未知干扰输入的随机系统状态估计问题广泛存在于控制、通信、信号处理和故障诊断等领域,但目前的研究成果大多局限于单传感器动态离散系统。针对状态方程含有未知干扰、量测方程含有未知偏差的多传感器系统状态估计问题开展了研究,提出了一种双重未知干扰解耦下的最小方差无偏估计滤波器。首先,建立量测偏差通用演化模型;然后,解耦偏差演化模型中的未知输入,设计最小方差无偏估计器对量测干扰偏差进行估计;之后,利用估计出的量测偏差来校正动态系统测量值;最后,根据量测偏差校正后的系统模型设计最优状态观测器,获得具有最小方差无偏的状态估计。径向飞行控制系统的例子验证了所提方法的有效性,与相关方法的仿真对比反应了所提方法的优越性。     

Data fusion algorithm based on state and attribute parameter

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Performance evaluation of digital beamforming strategies for satellite communications

Smart antennas are becoming one of the promising technologies to meet the rapidly increasing demands for more capacity of satellite communication systems. A main component in a smart antenna system is beamforming. Because of the limitations of analog beamforming, digital beamforming will be employed in future satellite communication systems. We evaluate the performance of various digital beamforming strategies proposed in the literature for satellite communications: 1) single fixed beam/single user, 2) single fixed beam/multiple users, 3) single adaptive beam/single user, and 4) single Chebyshev dynamic beam/multiple users. Multiple criteria including coverage, system capacity, signal-to-interference-plus-noise ratio (SINR), and computation complexity are used to evaluate these satellite communication beamforming strategies. In particular, a Ka-band satellite communication system is used to address the various issues of these beamforming strategies. For the adaptive beamforming approach, subarray structure is used to obtain the weights of a large 2D antenna array, and a globally convergent recurrent neural network (RNN) is proposed to realize the adaptive beamforming algorithm in parallel. The new subarray-based neural beamforming algorithm can reduce the computation complexity greatly, and is more effective than the conventional least mean square (LMS) beamforming approach. It is shown that the single adaptive beam/single user approach has the highest system capacity.     

Problem characterization in tracking/fusion algorithm evaluation

The performance of a tracking/fusion algorithm depends very much on the complexity of the problem. This paper presents an approach for evaluating tracking/fusion algorithms that consider the difficulty of the problem. Evaluation is performed by characterizing the performance of the basic functions of prediction and association. The problem complexity is summarized by means of context metrics. Two context metrics for characterizing prediction and association difficulty are normalized target mobility and normalized target density. These metrics should be presented along with the performance metrics. The context metrics also support more efficient generation of input data for performance evaluation. Simple tests for evaluating basic tracking algorithm functions are presented     

Performance evaluation of a cascaded logic for track formation inclutter

The authors present a Markov-chain-based performance evaluation technique for two-stage sliding-window cascaded logic (2/2×m /n) for track formation in a cluttered environment. The main features of this technique are that it avoids the need for extensive simulations and it is more realistic than previous methods, accounting for the association gate size variation. The gates are obtained from a Kalman filter and fully account for the transient observed for the cascaded logic following the two-point initiation from its first stage. Numerical examples of performance evaluation are given along with a logic design example     

基于模块服役状态的盒式连接可重构型架稳定性评估方法

盒式连接可重构型架的稳定性对飞机部件的装配质量有着重要影响。为了解决当前稳定性评估方法在实施成本及有效性上的不足,从型架模块化特征出发提出基于模块服役状态的盒式连接可重构型架稳定性评估方法。首先,分析了盒式连接可重构型架服役过程,指出该过程是由作业期与空置期交替组合而成,并给出了服役稳定性及服役稳定度定义;然后,构建了基于型架关键测量特性的服役状态评价模型,实现了盒式连接可重构型架服役状态退化过程的描述及利用实测数据的空置期服役状态评估;同时,针对作业期内难以甚至无法获取测量数据的问题,提出基于多源稳定性影响因素分析并利用稳定性熵来表征服役状态退化程度的策略;最终融合空置期与作业期的评估结果构建了型架稳定性综合评价模型,相比传统的定检方法能够更简捷、有效地完成某一时间段内型架服役稳定性评估。最后,以某型垂尾盒式连接可重构装配型架后梁定位器模块为实验对象,验证了所提出方法的有效性。     

Performance limits of track-to-track fusion versus centralized estimation: theory and application [sensor fusion]

Track-to-track fusion is an important part in distributed multisensor-multitarget tracking. The centralized and distributed tracking configurations were studied in (H.Chen et al., Proc. of SPIE Conf. on Signal and Data Processing of Small Targets, vol. 4048, 2000) using simulated air-to-air scenarios, and in (K.C. Chang, et al, IEEE Transact. on Aerospace and Electronic Systems, vol. 33, no. 4, pp. 1271-1276, 1997) with analytical results based on /spl alpha/-/spl beta/ filters. The current work generalizes the results in the latter to the cases with more than 2 sensors. As the number of sensors increases, the performance of the distributed tracker is shown to degrade compared with the centralized estimation even when the optimal track-to-track fusion is used. An approximate track-to-track fusion is presented and compared with the optimal track-to-track fusion with performance curves for various numbers of sensors. These performance curves can be used in designing a fusion system where certain trade-offs need to be considered. Finally, these results are compared with simulation results for a realistic air-to-air encounter scenario.     

Performance evaluation of multi-sensor classification systems

A common problem in classification is to use one/more sensors to observe repeated measurements of a target's features/attributes, and in turn update the targets' posterior classification probabilities to aid in target identification. This paper addresses the following questions: 1. How do we quantify the classification performance of a sensor? 2. What happens to the posterior probabilities as the number of measurements increase? 3. Will the targets be classified correctly? While the Kalman filter allows for off-line estimation of kinematic performance (covariance matrix), a comparable approach for studying classification accuracy has not been done previously. We develop a new analytical approach for computing the long-run classification performance of a sensor and also present recursive formulas for efficient calculation of the same. We show that, under a minimal condition, a sensor will eventually classify all targets perfectly. We also develop a methodology for evaluating the classification performance of multi-sensor fusion systems involving sensors of varying quality. The contributions of this paper are 1. A simple metric to quantify a sensor's ability to discriminate between the targets being identified, and its use in comparing multiple sensors, 2. An approximate formula based on this metric to compute off-line estimates of the rate of convergence toward perfect classification, and the number of measurements required to achieve a desired level of classification accuracy, and 3. The use of this metric to evaluate classification performance of multi-sensor fusion systems.     

Comparison of two-sensor tracking methods based on state vectorfusion and measurement fusion

There are two approaches to the two-sensor track-fusion problem. Y Bar-Shalom and L. Campo (ibid., vol.AES-22, 803-5, Nov. 1986) presented the state vector fusion method, which combines state vectors from the two sensors to form a new estimate while taking into account the correlated process noise. The measurement fusion method or data compression of D. Willner et al. (1976) combines the measurements from the two sensors first and then uses this fused measurement to estimate the state vector. The two methods are compared and an example shows the amount of improvement in the uncertainty of the resulting estimate of the state vector with the measurement fusion method     

Sajben扩压器复杂流动中湍流模型的性能评估

用8个常用的湍流模型对Sajben扩压器中跨声速流动进行了数值模拟,评估了Spalart-Allmaras, 标准k-ε, RNG (re-normalization group) k-ε,realizable k-ε,标准k-ω,SST(shear stress transport) k-ω,v2-f,Reynolds stress共8个湍流模型对激波/湍流边界层相互作用的模拟预测能力.通过与实验数据比较发现:SST k-ω模型和v2-f模型比其他模型模拟的更准确,其中SST k-ω模型比v2-f更能准确地预测壁面压力,然而对于分离点、再附点以及分离区长度v2-f比SST k-ω预测得更准确.      

区间数据整体估计方法

提出了一种区间数据整体估计方法, 给出整体分布参数的最佳线性无偏估计量及其协方差矩阵, 得到了正态分布、Weibull分布等位置-尺度分布族区间数据百分位值和百分率的置信限估计.该方法可以将不同条件下的试验数据作为一个整体进行统计推断, 将传统的只适用于完全数据的回归分析推广到工程中常见的区间数据的情况.      

基于分层置信规则库的惯导系统性能评估方法

为解决惯导系统（INS）性能评估所面临的高价值样本缺失、评估指标多、系统复杂等问题,提出一种基于分层置信规则库（Hierarchical BRB）的惯导系统性能评估方法。将专家知识与监测数据进行有效融合,提高了惯导系统的性能评估精度。首先,针对惯导系统结构构建分层BRB模型,同时将系统内部器件产生组合误差考虑在模型中。其次,为降低专家知识不确定性对初始模型评估精度的影响,采用基于投影算子的协方差矩阵自适应优化策略（P-CMA-ES）构建优化模型,通过监测数据对模型参数进行微调。最后,以某型捷联惯导系统的性能评估为例,验证了所提方法的有效性。     

Performance evaluation of track initiation in dense targetenvironments

This paper describes an analysis of track initiation performance in very high target density environments. The analysis of tracking performance performed previously by S. Mori et al. (1992) yielded very simple expressions to predict average track purity in terms of tracking parameters. These results were, however, obtained mainly for the track continuation phase in which tracks are more or less well established. We extend these results to the track initiation phase     

基于LTSA融合降维法的滚动轴承可靠性评估方法

为了保障滚动轴承在给定工况下安全平稳地运行,提出了一种基于局部切空间排列(LTSA)和威布尔比例故障率模型(WPHM)的滚动轴承可靠性评估方法.该方法提取滚动轴承整个寿命周期的时域、频域、时频域及统计等不同分析域上的特征指标,从中筛选出包含滚动轴承运行状态的特征指标,构建出高维多域特征集;利用局部切空间排列算法对高维多...     

Performance of multiple-model filters and parameter-sensitivity analysis for likelihood evaluation with shock variance models

The performance of multiple-model filtering algorithms is examined for shock-variance models, which are a form of linear Gaussian switching models. The primary aim is to determine whether existing multiple-model filters are suitable for evaluating measurement likelihoods in classification applications, and under what conditions such classification models are viable. Simulation experiments are used to empirically examine the likelihood-evaluation performance of suboptimal merging and pruning algorithms as the number of state hypotheses per time step (i.e., algorithm order) increases. The second-order generalized pseudo-Bayes or (GPB(2)) algorithm is found to provide excellent performance relative to higher order GPB algorithms through order five. Likelihoods from fixed-size pruning (FSP) algorithms with increasing numbers of state hypotheses are used to validate the GPB likelihoods, and convergence of the FSP likelihoods to the GPB values is observed. These results suggest that GPB(2) is a reasonable approximation to the unrealizable optimal algorithm for classification. In all cases except very-low-noise situations, the interacting multiple model (IMM) algorithm is found to provide an adequate approximation to GPB(2). Sensitivity of likelihood estimates to certain model parameters is also investigated via a mismatch analysis. As a classification tool, the discrimination capabilities of the measurement likelihoods are tested using an idealized forced-choice experiment, both with ideal and with mismatched models     

Hypothesis management for information fusion

The efficient management of large collections of fusion hypotheses presents a critical challenge for scaling high-level information fusion systems to solve large problems. We motivate this challenge in the context of two Alphatech research projects, and discuss several partial solutions. A recurring theme is the exploitation of space-efficient, factored representations of multiple hypotheses to enable an efficient search for good hypotheses.     

Performance evaluation of GPS augmentation using Quasi-Zenith Satellite System

The current global positioning system (GPS) provides limited availability and capability for a country like Japan where mountainous terrain and urban canyons do not allow a clear skyline to the horizon. At present, the Japanese Quasi-Zenith Satellite System (QZSS) is under investigation through a cooperative effort between the government and the private sector. QZSS is considered a multi-function satellite system, as it is able to provide communication, broadcasting, and positioning services for mobile users in a specified region with a high elevation angle. The additional GPS compatible signals from QZSS can remarkably improve the availability, accuracy, and capability of GPS positioning. This work focuses on the performance of GPS augmentation using the proposed QZSS. The QZSS satellite constellation and signal structure are briefly reviewed. Positioning with pseudo-range and carrier phase are discussed. The performance of GPS augmentation using QZSS in the Asian-Pacific and Australian area is studied using software simulations. The results are presented using the number of visible satellites as a measure of availability, GDOP as a measure of accuracy, and ambiguity success rate as a measure of capability of carrier-phase-based positioning with spatial and temporal variations. The results show that the QZSS will improve not only the availability and accuracy of GPS positioning, but will also enhance the capability of the GPS carrier-phase-based positioning in Japan and neighboring regions.