不完全量测下Cramer-Rao下界与数据丢失位置的关系

随机探测/丢失序列的引入使得状态估计中的Cramer-Rao下界(Cramer-Rao lower bound, CRLB)具有随机性; 在探测概率小于1的不完全量测系统中, 针对CRLB与数据丢失位置(Location of missing data, LMD)之间呈现出的某种关联现象, 讨论了离散随机系统中LMD对CRLB的影响; 利用Lyapunov不等式, 给出了一定条件下变形CRLB与LMD满足单调递减函数关系这一新结论; 同时在给定探测率下, 给出了一组CRLB上下界计算方法, 数字仿真表明探测率越高, 上下界越接近理论CRLB.     

不完全量测下随机有偏离散系统的Cramér-Rao下界

针对含有不完全随机有偏测量序列的状态估计问题, 给出了统计意义下的修正递推估计误差方差Cramér-Rao下界(CRLB)求解算法. 首先建立了不完全随机有偏量测离散系统的数学模型, 进而推导了枚举的CRLB和统计意义的CRLB计算式, 该统计意义的CRLB为枚举CRLB的下界, 其计算量远小于枚举CRLB求解的计算量. 最后, 以给定探测概率和偏差发生率下的一类光电跟踪系统为例, 进行了数字仿真.     

不完全量测下基于事件触发机制的面目标跟踪系统CRLB

面目标跟踪系统状态估计问题中,附加的强非线性面目标扩展测量会增加系统的通信量和估计中心的计算量.为此,基于工程应用,提出一种不完全量测下的事件触发机制来控制面目标测量传输.从理论上推导了事件触发机制下面目标跟踪系统的理想（枚举）克拉美罗下界（Cramer-Rao lower bound,CRLB）和统计意义下的CRLB,该统计意义CRLB为理想CRLB的下界,计算复杂度远小于理想CRLB,便于工程应用.典型测试航路下的仿真结果表明:不完全量测下,面目标跟踪系统CRLB明显小于传统质点目标跟踪系统CRLB;同时,利用所提事件触发机制,可在大幅减少面目标跟踪系统通信量的同时保证系统的最优估计性能.     

Low-degree approximation of high-degree B-spline surfaces

In this paper, a method for approximate conversion of high degree Bezier and B-spline surfaces to lower degree representations is presented to facilitate the exchange of surface geometry between different geometric modeling systems. Building on previous work on curve approximation, the method uses adaptive sampling to compute approximation error and lofting of isoparametric curves to produce the approximating surface. In addition, a bound for the approximation accuracy is computed using convex hulls.     

Deriving a Fault Architecture to Guide Testing

Defect analysis of software components can be used to guide testing, with the goal of focusing on parts of the software that were fault-prone in earlier releases or earlier life cycle phases, such as development. We replicate a study that adapted a reverse architecting technique using defect reports to derive fault architectures. A fault architecture determines and visualizes components that are fault-prone in their relationships with other components, as well as those that are locally fault-prone. Our case study uses defect data from three releases of a large medical record system to identify relationships among system components, based on whether they are involved in the same defect report.We investigate measures that assess the fault-proneness of components and component relationships. Component relationships are used to derive a fault architecture. The resulting fault architecture indicates what the most fault-prone relationships are in a release. We also apply the technique in a new way. Not only do we derive fault architectures for each release, we derive fault architectures for the development, system test and post release phases within each release. Comparing across releases, makes it possible to see whether some components are repeatedly in fault-prone relationships. Comparing across phases, makes it possible to see whether development fault architectures can be used to identify those parts of the software that need to be tested more. We validate our predictions using system test data from the same release. We also use the development and system test fault architectures to identify fault-prone components after release, and validate our predictions using post release data.     

Application of structured total least squares for system identification and model reduction

The following identification problem is considered: Minimize the /spl lscr//sub 2/ norm of the difference between a given time series and an approximating one under the constraint that the approximating time series is a trajectory of a linear time invariant system of a fixed complexity. The complexity is measured by the input dimension and the maximum lag. The question leads to a problem that is known as the global total least squares problem and alternatively can be viewed as maximum likelihood identification in the errors-in-variables setup. Multiple time series and latent variables can be considered in the same setting. Special cases of the problem are autonomous system identification, approximate realization, and finite time optimal /spl lscr//sub 2/ model reduction. The identification problem is related to the structured total least squares problem. This paper presents an efficient software package that implements the theory. The proposed method and software are tested on data sets from the database for the identification of systems DAISY.     

H/sub /spl infin// filtering for singular systems

This note considers the H/sub /spl infin// filtering problem for linear continuous singular systems. The purpose is the design of a linear filter such that the resulting error system is regular, impulse-free and stable while the closed-loop transfer function from the disturbance to the filtering error output satisfies a prescribed H/sub /spl infin//-norm bound constraint. Without decomposing the original system matrices, a necessary and sufficient condition for the solvability of this problem is obtained in terms of a set of linear matrix inequalities (LMIs). When these LMIs are feasible, an explicit expression of a desired filter is given. Finally, an illustrative example is presented to demonstrate the applicability of the proposed approach.     

Robust H/sub /spl infin// control for uncertain discrete-time-delay fuzzy systems via output feedback controllers

This work investigates the problem of robust output feedback H/sub /spl infin// control for a class of uncertain discrete-time fuzzy systems with time delays. The state-space Takagi-Sugeno fuzzy model with time delays and norm-bounded parameter uncertainties is adopted. The purpose is the design of a full-order fuzzy dynamic output feedback controller which ensures the robust asymptotic stability of the closed-loop system and guarantees an H/sub /spl infin// norm bound constraint on disturbance attenuation for all admissible uncertainties. In terms of linear matrix inequalities (LMIs), a sufficient condition for the solvability of this problem is presented. Explicit expressions of a desired output feedback controller are proposed when the given LMIs are feasible. The effectiveness and the applicability of the proposed design approach are demonstrated by applying this to the problem of robust H/sub /spl infin// control for a class of uncertain nonlinear discrete delay systems.     

Magnetic compass fault detection method for GPS/INS/magnetic compass integrated navigation systems

This paper proposes a magnetic compass fault detection method for GPS/INS/Magnetic compass integrated navigation systems. The fault is assumed to be caused by the hard iron and soft iron effect and modeled as an abrupt change in the magnetic compass output. In order to detect the fault, a test statistic related with only azimuth error measurement is determined. When a fault is detected, the GPS/INS/Magnetic compass integrated navigation system is changed into a GPS/INS integrated navigation system mode. In order to show the validity of the proposed method, computer simulation and van testing are carried out. The simulation and van test results show that the proposed navigation system gives more accurate outputs than the GPS/INS/Magnetic compass without the proposed method.     

Quantitative analysis of faults and failures in a complex softwaresystem

The authors describe a number of results from a quantitative study of faults and failures in two releases of a major commercial software system. They tested a range of basic software engineering hypotheses relating to: the Pareto principle of distribution of faults and failures; the use of early fault data to predict later fault and failure data; metrics for fault prediction; and benchmarking fault data. For example, we found strong evidence that a small number of modules contain most of the faults discovered in prerelease testing and that a very small number of modules contain most of the faults discovered in operation. We found no evidence to support previous claims relating module size to fault density nor did we find evidence that popular complexity metrics are good predictors of either fault-prone or failure-prone modules. We confirmed that the number of faults discovered in prerelease testing is an order of magnitude greater than the number discovered in 12 months of operational use. The most important result was strong evidence of a counter-intuitive relationship between pre- and postrelease faults; those modules which are the most fault-prone prerelease are among the least fault-prone postrelease, while conversely, the modules which are most fault-prone postrelease are among the least fault-prone prerelease. This observation has serious ramifications for the commonly used fault density measure. Our results provide data-points in building up an empirical picture of the software development process     

多基地声纳系统中目标位置与速度联合估计

在多基地声呐系统中,为了利用时间和与多普勒频率量测同时估计运动目标的位置与速度,设计了一种闭式的估计器.其中,使用误差修正的方法,改善了传统的多步加权最小二乘估计器.该估计器只涉及线性加权最小二乘运算,在量测高斯噪声较小的情况下,均方误差可以达到克拉美罗下界(CRLB).通过计算机模拟对比了该估计器的均方误差与CRLB,并比较了其与传统多步加权最小二乘估计器的性能,结果表明:估计器的均方误差小于传统多步加权最小二乘估计器.     

Analogy-Based Practical Classification Rules for Software Quality Estimation

Software metrics-based quality estimation models can be effective tools for identifying which modules are likely to be fault-prone or not fault-prone. The use of such models prior to system deployment can considerably reduce the likelihood of faults discovered during operations, hence improving system reliability. A software quality classification model is calibrated using metrics from a past release or similar project, and is then applied to modules currently under development. Subsequently, a timely prediction of which modules are likely to have faults can be obtained. However, software quality classification models used in practice may not provide a useful balance between the two misclassification rates, especially when there are very few faulty modules in the system being modeled.This paper presents, in the context of case-based reasoning, two practical classification rules that allow appropriate emphasis on each type of misclassification as per the project requirements. The suggested techniques are especially useful for high-assurance systems where faulty modules are rare. The proposed generalized classification methods emphasize on the costs of misclassifications, and the unbalanced distribution of the faulty program modules. We illustrate the proposed techniques with a case study that consists of software measurements and fault data collected over multiple releases of a large-scale legacy telecommunication system. In addition to investigating the two classification methods, a brief relative comparison of the techniques is also presented. It is indicated that the level of classification accuracy and model-robustness observed for the case study would be beneficial in achieving high software reliability of its subsequent system releases. Similar observations are made from our empirical studies with other case studies.     

Adaptive fuzzy neuro-observer applied to low cost INS/GPS

A combined MEMS Inertial Navigation System (INS) with GPS is used to provide position and velocity data of land vehicles. Data fusion of INS and GPS measurements are commonly achieved through a conventional Extended Kalman filter (EKF). Considering the required accurate model of system together with perfect knowledge of predefined error models, the performance of the EKF is decreased due to unmodeled nonlinearities and unknown bias uncertainties of MEMS inertial sensors. Universal knowledge based approximators comprising of neural networks and fuzzy logic methods are capable of approximating the nonlinearities and the uncertainties of practical systems. First, in this paper, a new fuzzy neural network (FNN) function approximator is used to model unknown nonlinear systems. Second, the process of design and real-time implementation of an adaptive fuzzy neuro-observer (AFNO) in integrated low-cost INS/GPS positioning systems is proposed. To assess the long time performance of the proposed AFNO method, wide range tests of a real INS/GPS with a car vehicle have been performed. The unbiased estimation results of the AFNO show the superiority of the proposed method compared with the classic EKF and the adaptive neuro-observer (ANO) including a pure artificial neural network (ANN) function approximator.     

Building a robust integrity monitoring algorithm for a low cost GPS-aided-INS system

In this paper, the integrity of low cost GPS/INS systems is investigated to ensure the ability to obtain continuous high-integrity, high-accuracy vehicle state estimate under low-computational system requirement. The utilization of two fault detection and identification (FDI) techniques, the χ² (or sometimes referred to as chi-squared) gating function and the multiple model adaptive estimation (MMAE), is proposed to monitor the integrity of GPS measurements. A fault in GPS measurements is modeled with an increase in GPS measurements noise covariance matrix which may result from mistuning of filter’s noise parameters, interference, jamming, or multipath errors. These types of faults are covered by this work and are assumed to last for unconstrained period of time. ξ² FDI systems are computationally very inexpensive, have good fault detection ability and require no a priori knowledge on system dynamics. However, they are sensitive to filter tuning and fail to detect faults when the filter converges to them rather than rejecting them. Model-based approaches provide outstanding FDI ability. However, they are computationally demanding, require a priori knowledge on system model, sensitive to mismodeling errors, have finite convergence time, and compromise filter optimality under no-failure conditions. The proposed fusion algorithm guarantees integrity and does not affect filter’s optimality under no-failure conditions. Simulated and experimental tests were conducted to verify the accuracy of the proposed techniques. Results are presented at the end of the paper to highlight the performance characteristics of the proposed FDI system implementation.     

Fault tolerance analysis for stochastic systems using switching diffusion processes

This article models a class of stochastic systems with faults by switching diffusion processes (SDP), and analyses the fault tolerability of such stochastic systems in the sense of input-to-state stability of overall SDP. The fault tolerability relies on the trade-off among the fault occurrence transition rate, the frequency of switching, and the decreasing rate of Lyapunov functions along the solution of the system. Our results show that it may not be necessary to design the fault tolerant controller even though the stochastic system is not separately stable in the healthy and faulty situations, the stability of the overall system process is still guaranteed under some conditions. The proposed tools are illustrated through an example of a fault-prone manufacturing system.     

无线传感器网络中基于TDOA/FDOA的增强半正定松弛定位算法研究

本文在无线传感器网络定位问题中,考虑了基于到达时间差（Time-Difference-of-Arrival,TDOA）和到达频率差（Frequency-Difference-of-Arrival,FDOA）的移动未知目标定位问题,TDOA/FDOA联合定位可以有效利用传感器的位置和速度信息,提高了定位精度。本文在现有的半正定松弛（Semidefinite Relaxation, SDR）方法的基础上,提出了一种增强半正定松弛方法。通过挖掘现有半正定规划问题中优化变量之间的内在联系并将这些联系转化为凸约束,有效提高了现有半正定松弛方法的紧度,从而使估计的未知目标的位置和速度精度达到了克拉美-罗下界 (Cramer Rao lower bound,CRLB)。仿真结果表明,该方法的性能在大噪声时优于现有方法。     

Fuzzy neural network quadratic stabilization output feedback control for biped robots via H/sub /spl infin// approach

A novel fuzzy neural network (FNN) quadratic stabilization output feedback control scheme is proposed for the trajectory tracking problems of biped robots with an FNN nonlinear observer. First, a robust quadratic stabilization FNN nonlinear observer is presented to estimate the joint velocities of a biped robot, in which an H/sub /spl infin// approach and variable structure control (VSC) are embedded to attenuate the effect of external disturbances and parametric uncertainties. After the construction of the FNN nonlinear observer, a quadratic stabilization FNN controller is developed with a robust hybrid control scheme. As the employment of a quadratic stability approach, not only does it afford the possibility of trading off the design between FNN, H/sub /spl infin// optimal control, and VSC, but conservative estimation of the FNN reconstruction error bound is also avoided by considering the system matrix uncertainty separately. It is shown that all signals in the closed-loop control system are bounded.     

H/sub /spl infin// fuzzy output feedback control design for nonlinear systems: an LMI approach

Addresses the problem of stabilizing a class of nonlinear systems by using an H/sub /spl infin// fuzzy output feedback controller. First, a class of nonlinear systems is approximated by a Takagi-Sugeno (TS) fuzzy model. Then, based on a well-known Lyapunov functional approach, we develop a technique for designing an H/sub /spl infin// fuzzy output feedback control law which guarantees the L/sub 2/ gain from an exogenous input to a regulated output is less or equal to a prescribed value. A design algorithm for constructing an H/sub /spl infin// fuzzy output feedback controller is given. In contrast to the existing results, the premise variables of the H/sub /spl infin// fuzzy output feedback controller are not necessarily to be the same as the premise variables of the TS fuzzy model of the plant. A numerical simulation example is presented to illustrate the theory development.     

多无源传感器的在线配准

为估计无源传感器的角度偏差,在只有角度测量信息的前提下,提出了多无源传感器的在线配准方法.该方法运用几何知识,由传感器测量的多个角度数据得到目标的估计距离,通过估计距离进行在线滤波器的初始化,进而实时估计传感器的角度偏差;并给出了配准模型的Cramer-Rao下限(CRLB).Monte-Carlo仿真表明:该方法能有效地估计无源传感器的角度偏差,同时得到目标航迹.     

Error analysis of non-collaborative wireless localization in circular-shaped regions

High-accuracy wireless localization is a promising technique for many location-aware applications, such as location-aware billing, manufacturing automation, and disaster rescue. In this paper, we investigate the performance for non-collaborative wireless localization using both analytical and numerical approaches. Based on a proposed hybrid multilateration approach, which combines the advantages of the linear and nonlinear least-squares methods, we can estimate the target location and then evaluate the localization performance by the expected localization error and expected Cramer-Rao lower bound (CRLB) in the entire target region. From theoretical analysis, we derive the CRLB for the location estimate from the target and landmark locations, and we prove that if the landmark layout forms a regular polygon, then the CRLB has the same value at the center and the circumcircle of the polygon. In particular, for three landmarks, this value is actually the minimum CRLB. By Monte Carlo simulation, we show that the proposed hybrid localization approach can estimate the target location effectively. Moreover, the localization performance is significantly improved when more landmarks are used, and the best performance is obtained when landmarks are evenly spaced along the boundary of the region. In addition, we show that when a large number of landmarks are used, the minimum CRLB can be used to approximate the expected CRLB, while the latter is difficult to obtain exactly. Moreover, a localization experiment is conducted using active radio frequency identification (RFID) devices, and it is found that the results are consistent with those obtained from the theoretical analysis and simulation.