Expert System Applications to the Cockpit of the '90s

There is a strong requirement for a new generation of avionics systems with a more integrated hardware and software structure. This integrated avionics system will use significant increases in computer automation with more innovative signal processing, sensor fusion and expert system software to reduce pilot workload, while improving total system performance and reliability. Expert system software packages will be implemented within the core architecture of these next generation integrated avionics systems to assist the pilot. The expert systems will consider the pertinent information available from the ``sensor' subsystems to assess the current situation. The expert systems then consult their knowledge base and rule base software structures to determine alternative reactions to the perceived situation. Then pending upon the critical of the function, situation and reaction, the expert system could either execute the most favorable reaction or display the suggested alternative courses of action to the pilot. This paper addresses the requirement, the enabling technologies and the potential structure of this next generation of avionics. It concludes with two examples of the potential of future avionics expert systems. The two examples are 1) A Navigation and Route Planning Expert and 2) A Threat Assessment and Threat Reaction Expert. Significant things are happening in technology at an accelerating pace that enable the development of this new generation of avionics.     

平面几何智能辅导系统的设计与实现

平面几何智能辅导系统是一个适用面较广的家教辅导专家系统,由于几何问题的复杂性,使得系统的实现变得繁杂。本文首先简单介绍了系统的整体流程,具体讨论了系统的核心模块(规则库)的实现及其知识表示方法。分类是系统实现的关键技术。本系统已能成功地解决一般几何证明问题,满足不同层次学生的平面几何辅导需要。     

Evaluating Sensor Orientations for Navigation Performance and Failure Detection

A study of the constraints imposed by the choice of particular orientations of redundant inertial sensors upon navigation system performance and the detection and identification of sensor failures in fault-tolerant inertial measurement units is presented. Figures of merit are derived for systematically evaluating alternative sensor orientations. The volume of the ellipsoid associated with the covariance matrix of estimation errors is used to rank sensor orientations in terms of navigation performance. Distance measures commonly used in hypothesis testing are used to rank sensor orientations in terms of the detectability of sensor failures. The application of these results is illustrated for configurations of four 2-degree-of-freedom gyroscopes.     

Hierarchical adaptive Kalman filtering for interplanetary orbitdetermination

A modular and flexible approach to adaptive Kalman filtering has recently been introduced using the framework of a mixture-of-experts regulated by a gating network. Each expert is a Kalman filter modeled with a different realization of the unknown system parameters. The unknown or uncertain parameters can include elements of the state transition matrix, observation mapping matrix, process noise covariance matrix, and measurement noise covariance matrix. The gating network performs on-line adaptation of the weights given to individual filters based on performance. The mixture-of-experts approach is extended here to a hierarchical architecture which involves multiple levels of gating. The proposed architecture provides a multilevel hypothesis testing capability. The utility of the hierarchical architecture is illustrated via the problem of interplanetary navigation (Mars Pathfinder) using simulated radiometric data. It serves as a useful tool for assisting navigation teams in the process of selecting the parameters of the navigational filter over various operating regimes. It is shown that the scheme has the capability of detecting changes in the system parameters and switching filters appropriately for optimal performance. Furthermore, the expectation-maximization (EM) algorithm is shown to be applicable in the proposed framework     

DORIS (Diagnostic Oriented Rockwell Intelligent System)

DORIS is an expert system developed for aiding in the fault diagnosis process. DORIS performs two major tasks: one as an expert consultant to the end-user, and second as an aid to the knowledge engineer via knowledge acquisition tools. DORIS is coded in LISP without third party expert system development tools. System features include a user-friendly interface, knowledge bases, forward and backward chaining inference systems, an explanation system, and a rule maintenance system. DORIS has been applied as a prototype in two areas: fault diagnosis of a Serial Digital Multiplex Assembly, and procedrual control for navigational sequencing.     

民用航空发动机航线维修专家系统设计分析

为了缩短民用航空发动机航线维修的时间,提高航线维修效率,降低航班延误率,针对民用航空发动机航线维修工作的特点,提出了基于故障特征利用模糊技术建立专家系统的模糊知识库,并结合智能检索技术建立民用航空发动机航线维修专家系统的方法,该方法能够提高专家系统解决问题的准确性和时效性。重点阐述了专家系统模糊知识库构建和智能检索技术的逻辑思路。相比于以关键故障特征为知识点建立的数据库,模糊知识库包含了更全面的故障特征知识。智能检索基于概念或语义检索,相比于关键词检索,对自然语言有更好地理解,便于航线维修工作人员使用。     

Road profile recognition for autonomous car navigation and Navstar GPS support

We discuss autonomous car navigation based on updating dead reckoning (DR) by road profile recognition (RPR). The navigation system requires sensors to detect changes in altitude and driving direction which are installed in modern cars for different purposes (e.g. ABS sensors). The layout of the navigation system is discussed and simulations are carried out over driving distances of approximately 150 km on the basis of realistic road data and ordinary sensor accuracies. Positioning errors of lower than 10 m (standard deviation) are observed. To achieve this accuracy the synchronization error between measured and mapped data must be continually estimated. The introduced navigation method is ideal to complete present commercial car navigation systems using Navstar GPS.     

Covariance control for multisensor systems

As the profusion of different sensors improves the capabilities of tracking platforms, tracking objectives can move from simply trying to achieve the most with a limited sensor suite to developing the ability to achieve more specific tracking goals, such as reducing the uncertainty in a target estimate enough to accurately fire a weapon at a target or to ensure that a mobile robot does not collide with an obstacle. Multisensor manager systems that balance tracking performance with system resources have traditionally been ill-suited for achieving such specific control objectives. This work extends the methods developed in single-sensor management schemes to a multisensor application using an approach known as covariance control, which selects sensor combinations based on the difference between the desired covariance matrix and that of the predicted covariance of each target.     

Sequential Piecewise Recursive Filter for GPS Low-Dynamics Navigation

The design, implementation, and performance of a real-time estimation algorithm, referred to in this paper as the sequential piecewise recursive (SPWR) algorithm, for the global-positioning system (GPS) low-dynamics navigation system is described. The SPWR algorithm for this application was implemented in single precision arithmetic (32 bit, floating point). Numerical results are presented covariance and filter gains at a slower rate than the state measurement update, and it uses U-D factor formulation to perform covariance computations. The SPWR algorithm saves real-time processing requirements without appreciable degradation of filter performance. Another important feature of the SPWR algorithm is that it incorporates pseudorange and delta-range data from each GPS satellite sequentially for navigation solution. The SPWR algorithm, for this application, was implemented in single precision arithmetic (32 bit, floating point). Numerical results are presented.     

Propagation and System Accuracy Impact of Major Sensor Errors on a Strapdown Aircraft Navigator

A strapdown system is considered as an unaided inertial navigator aboard an aircraft. Presented here are simulation results detailing the propagation of navigation errors (in nautical miles) due to strapdown sensor errors for four trajectories. They indicate the type of performance that can be expected from a strapdown system utilizing good ?off the shelf? gyros and accelerometers, and dramatically illustrate the improvements necessary in these components to obtain navigation performance comparable to that available from a good gimballed inertial system. The total navigation error for each trajectory is broken down to show the contribution from each of the various error sources. This breakdown quickly reveals which are the critical error sources for a given trajectory class, and also points up the relationship that exists between each individual error source, aircraft maneuvers, and the resulting navigation error. Several of the error mechanisms are discussed at length and a set of linearized differential equations which can be used to analyze error propagations is presented. These results should be of particular interest to the system designer who is faced with the problem of specifying the sensor error parameters necessary to meet mission performance requirements. With an analysis similar to the one presented here, but structured around his own expected mission trajectories, the designer should be able to confidently predict system accuracies and intelligently perform tradeoffs on the critical system parameters.     

Fuzzy corrections in a GPS/INS hybrid navigation system

A new concept regarding GPS/INS integration, based on artificial intelligence, i.e. adaptive neuro-fuzzy inference system (ANFIS) is presented. The GPS is used as reference during the time it is available. The data from GPS and inertial navigation system (INS) are used to build a structured knowledge base consisting of behavior of the INS in some special scenarios of vehicle motion. With the same data, the proposed fuzzy system is trained to obtain the corrected navigation data. In the absence of the GPS information, the system will perform its task only with the data from INS and with the fuzzy correction algorithm. This paper shows, using Matlab simulations, that as long as the GPS unavailability time is no longer than the previous training time and for the scenarios a priori defined, the accuracy of trained ANFIS, in absence of data from a reference navigation system, is better than the accuracy of stand-alone INS. The flexibility of model is also analyzed.     

基于工程图的飞机压延模设计CAD专家系统

基于工程设计ＣＡＤ专家系统特点,建立了ＡＤＤＥＳ专家系统（飞机压延模ＣＡＤ专家系统）。在建立ＤＩＥ编码系统基础上,使用特征编码等方法建立用户模型。系统中机构具有求解部分问题的能力,系统将机构层次地、分布式地组织,综合采用黑板结构、元推理等方法控制系统的问题求解策略,从而解决因知识库庞大而引起的效率低以及多专家系统合作等问题。     

State interpolation for on-board navigation systems

Common concepts for autonomous on-board navigation systems rely on the numerical integration of a spacecraft trajectory between subsequent measurements of a navigation sensor such as GPS. In combination with a Kalman filter, a predicted state vector becomes available at discrete, but not necessarily equidistant time steps. When used for real-time attitude control or geo-coding of image data, the on-board navigation system has to provide continuous dense output at equidistant time steps, which usually conflicts with the natural stepsize of the relevant integration methods and the non-equidistant measurement times. To cope with this problem, the integrator has to be supplemented by an interpolation scheme of compatible order and accuracy.After presenting a representative formulation of an on-board navigation system and deriving related timing and accuracy requirements, suitable Runge–Kutta methods and associated interpolants are selected and evaluated. Promising results are obtained for the classical RK4 method in combination with Richardson extrapolation and 5th-order Hermite interpolation. The 5th-order Fehlberg method with interpolation due to Enright and, for drag-free scenarios, the 5th-order Runge–Kutta–Nystrom method with 5th-order Hermite interpolation provide a good performance in terms of position interpolation. However, as both methods exhibit significant errors for the velocity interpolation, they are not recommended for use with the outlined navigation filter.     

Wavelet de-noising for IMU alignment

Inertial navigation system (INS) is presently used in several applications related to aerospace systems and land vehicle navigation. An INS determines the position, velocity, and attitude of a moving platform by processing the accelerations and angular velocity measurements of an inertial measurement unit (IMU). Accurate estimation of the initial attitude angles of an IMU is essential to ensure precise determination of the position and attitude of the moving platform. These initial attitude angles are usually estimated using alignment techniques. Due to the relatively low signal-to-noise ratio of the sensor measurement (especially for the gyroscopes), the initial attitude angles may not be computed accurately enough. In addition, the estimated initial attitude angles may have relatively large uncertainties that may affect the accuracy of other navigation parameters. This article suggests processing the gyro and accelerometer measurements with multiple levels of wavelet decomposition to remove the high frequency noise components. The proposed wavelet de-noising method was applied on a navigational grade inertial measurement unit (LTN90-100). The results showed that accurate alignment procedure and fast convergence of the estimation algorithm, in addition to reducing the estimation covariance of the three attitude angles, could be obtained.     

空间平台机动变轨自主导航研究

针对空间平台在高轨道机动变轨过程中自主导航的需求,采用了基于Kalman滤波器的捷联惯导与星敏感器的组合导航方案。结合Kalman滤波中协方差更新的误差分配分析方法,分析了影响空间平台状态估计误差的主要因素。采用适用于高轨道的球谐重力模型,运用STK工具包设计了变轨机动轨迹,将该轨迹应用于组合导航方案的仿真验证。仿真结果表明,量测噪声是影响空间平台姿态精度的主要因素,加速度计零偏对变轨过程速度精度有决定性影响,改善两者的精度可以实现空间平台机动变轨的高精度自主导航。     

Expert Systems in the Fighter of the 1990s

In the Advanced Tactical Fighter (ATF) to be deployed in the 1990s, the role of expert systems will enhance mission success. This paper discusses the utilization of two expert systems for handling multisensor data fusion and situation assessment. In multisensor data fusion, each sensor operates over a different region of the surveillance volume asynchronously and provides different measurements. In some instances, more than one sensor may yield the same measurement but with a different measurement accuracy. In this regard, the paper describes, in layman's terms, a system block diagram for processing the autonomous sensor track files and the possible need for a ``smart' fusion processor. This expert system is shown to manage the sensor outputs in both the temporal and spatial domains to maximize target identification confidence as well as kinematic state vector accuracy. The paper delineates the features needed by the fusion expert in order to assign a quality factor to each composite track file entry. A second expert system uses the output from fusion and other mission-related data to formulate the best picture of the surveillance volume at hand. This second expert system will show how historical data and real-time sensor data are merged for purposes of display parameters to the pilot, weapon cueing, countermeasures response management, and feedback to the fusion expert processor for individual sensor communication and data collection direction. The paper concludes with a tabular summary of the subprocesses of which these two expert systems may consist.     

基于知识库的路径规划辅助系统研究

为降低飞机驾驶员在路径规划任务中的工作负荷,提出了基于知识库的路径规划辅助系统结构。采用基于网格的启发式A*搜索技术完成路径规划任务。利用知识库中的规则进行推理,调节启发函数的加权因子,提高求解最优性,并调节规划网格大小,获得最佳求解速度和精度。知识库中的知识规则根据具体条件的累积和计算,在训练及使用中不断得以补充,减少不确定因素对驾驶员的影响。通过系统仿真验证,表明了该方案和算法的有效性。     

基于5G的通导融合位置认证系统性能分析

随着无人化智能移动装备在工业、交通等安全敏感领域的普及应用，民用导航定位系统中的定位安全问题日益突出。位置认证是对终端的物理位置声明进行认证的过程，是导航定位安全技术的重要组成部分。基于第五代(5G)移动通信网络的通导融合位置认证系统具有覆盖范围广、认证精度高、用户容量大、建设运维成本低等多重优势。本文首先介绍了多基站位置认证系统的检测判决原理，提出了漏检平均距离的定义作为位置认证系统精度的量化评价指标。在此基础上，通过数值仿真分析了信号带宽、基站同步误差、信噪比对位置认证系统精度的影响，并利用5G信道模型评估了典型场景下的位置认证系统的性能。结果表明，在具备3个以上的视距基站时，基于5G的通导融合位置认证系统可以实现米级的位置认证精度。     

基于5G的通导融合位置认证系统性能分析

随着无人化智能移动装备在工业、交通等安全敏感领域的普及应用,民用导航定位系统中的定位安全问题日益突出。位置认证是对终端的物理位置声明进行认证的过程,是导航定位安全技术的重要组成部分。基于第五代(5G)移动通信网络的通导融合位置认证系统具有覆盖范围广、认证精度高、用户容量大、建设运维成本低等多重优势。本文首先介绍了多基站位置认证系统的检测判决原理,提出了漏检平均距离的定义作为位置认证系统精度的量化评价指标。在此基础上,通过数值仿真分析了信号带宽、基站同步误差、信噪比对位置认证系统精度的影响,并利用5G信道模型评估了典型场景下的位置认证系统的性能。结果表明,在具备3个以上的视距基站时,基于5G的通导融合位置认证系统可以实现米级的位置认证精度。     

Fast converging adaptive processor or a structured covariance matrix

The use of adaptive linear techniques to solve signal processing problems is needed particularly when the interference environment external to the signal processor (such as for a radar or communication system) is not known a priori. Due to this lack of knowledge of an external environment, adaptive techniques require a certain amount of data to cancel the external interference. The number of statistically independent samples per input sensor required so that the performance of the adaptive processor is close (nominally within 3 dB) to the optimum is called the convergence measure of effectiveness (MOE) of the processor. The minimization of the convergence MOE is important since in many environments the external interference changes rapidly with time. Although there are heuristic techniques in the literature that provide fast convergence for particular problems, there is currently not a general solution for arbitrary interference that is derived via classical theory. A maximum likelihood (ML) solution (under the assumption that the input interference is Gaussian) is derived here for a structured covariance matrix that has the form of the identity matrix plus an unknown positive semi-definite Hermitian (PSDH) matrix. This covariance matrix form is often valid in realistic interference scenarios for radar and communication systems. Using this ML estimate, simulation results are given that show that the convergence is much faster than the often-used sample matrix inversion method. In addition, the ML solution for a structured covariance matrix that has the aforementioned form where the scale factor on the identity matrix is arbitrarily lower-bounded, is derived. Finally, an efficient implementation is presented.