Sensor Management Research Articles

Control based sensor management for a multiple radar monitoring scenario

In this paper we consider the problem of monitoring illuminations of multiple emitters by one electronic receiver located on a moving platform. The emitters exhibit a quasi-periodic radiation pattern, each in a different frequency band and with a different illumination period and illumination time. The goal is to tune the receiver to the appropriate frequency band at the appropriate time so as to maximize the probability of detection of each illumination of each radar (expressed as a quality of service (QoS) metric). This problem can be viewed as two subproblems: (1) generating tuning requests by particular frequency bands and (2) scheduling the receiver to satisfy the requests. In our scenario the number of requests from all the bands exceeds the physical capability of one receiver (overload) and thus selection is needed. The goal is to push the limits on the overload while still maintaining a required level of QoS metric. This can be achieved by controlling both the generation of the tuning requests and the scheduling. According to the control theory metaphor of software development, we map this problem onto a control architecture with one system-level controller and a collection of emitter-level controllers (one per emitter). We show that our control based sensor manager has significant advantages over a scheduler without feedback in terms of both the overload and the QoS metric. We also discuss design issues of such sensor management systems.

Sensor management policies to provide application QoS

Wireless sensor networks are uniquely characterized by tight energy and bandwidth constraints. These networks should be designed to provide enough data to their application so that a reliable description of the environment can be derived, while operating as energy-efficiently as possible and at the same time meeting bandwidth constraints. These goals are typically contradicting and must be balanced at the point where the application is best satisfied. In this paper, we address the problem of maximizing lifetime for a wireless sensor network while meeting a minimum level of application quality of service. This maximization is achieved by jointly scheduling active sensor sets and finding paths for data routing. significantly increased through optimized several heuristic methods. Simulation results show that several heuristic policies can achieve near optimal network lifetime.

Perception management: an emerging concept for information fusion

Abstract The state-of-art of sensor management has been advanced to the extent where high-level information plays an increasingly important role. Since situation state and associated request for information are device independent, a question arises as whether the term (sensor management) is sufficient enough to encompass the functions on the information level. Recognizing the essential need of intelligent agents to perceive the environment to take appropriate actions, this letter proposes the concept of perception management. It refers to controlling the process of data acquisition from the external world to enhance percepts obtained. The content of perception management is outlined and its relationship with sensor management is also discussed.

2P1-1F-F2 小型ヒューマノイド morph3 に搭載された多数のセンサ及びそのセンサ管理システム

138個のセンサを搭載する小型ヒューマノイドmorph3を開発した。本稿では, 搭載されているセンサの役割とそれらの多くのセンサから得られた情報を処理, 収集するためのシステムについて解説する。

Software Development Cost Estimation for Infrastructure Systems

Modern infrastructure systems, ranging from transportation to water and sewer systems, are becoming increasingly dependent on software. The role it plays in these systems is primarily to integrate and manage elements that support system operations. In other words, software has transformed what were previously considered to be largely static systems to active, dynamic systems. In general, infrastructure system software is characterized by an emphasis on the following core functions: (1) sensor management; (2) data analysis; (3) data management; and (4) equipment control interfaces. As the nature of infrastructure systems changes, the tools available to support their design and management must change as well. The construction engineering community has developed sound techniques and resources to support the estimation of costs for physical construction of infrastructure systems. However, such tools are not readily available to support the cost estimation of the software component of infrastructure system development and construction. In this research effort, a widely used software engineering cost-estimation technique, the construction cost model (COCOMO), was examined to determine if it is effective for infrastructure system application. This examination included a sample application of COCOMO to an infrastructure system software project and a critical analysis of the technique itself. The examination revealed that COCOMO is extremely sensitive to small variations in an estimator’s judgment, and that the foundation of the COCOMO model is poorly suited for infrastructure system application. As a result, it is recommended that a research and development program be initiated to create specific tools to support the cost estimation of infrastructure system software development.

Algorithms for optimal scheduling and management of hidden Markov model sensors

The author considers a hidden Markov model (HMM) where a single Markov chain is observed by a number of noisy sensors. Due to computational or communication constraints, at each time instant, one can select only one of the noisy sensors. The sensor scheduling problem involves designing algorithms for choosing dynamically at each time instant which sensor to select to provide the next measurement. Each measurement has an associated measurement cost. The problem is to select an optimal measurement scheduling policy to minimize a cost function of estimation errors and measurement costs. The optimal measurement policy is solved via stochastic dynamic programming. Sensor management issues and suboptimal scheduling algorithms are also presented. A numerical example that deals with the aircraft identification problem is presented.

Soft computing applications in aircraft sensor management and flight control law reconfiguration

A sensor management system based on soft computing techniques has been developed and implemented in the flight control system of a small commercial aircraft. Unlike in the conventional sensor management system, the signals from sensors are assigned weights based on fuzzy membership functions and the consolidated signal is computed as a weighted average. This approach improves the quality of the consolidated signal and reduces transients due to sensor failures. This soft voting is extended to soft flight control law reconfiguration. In addition, a virtual sensor has been introduced as an arbitrator which enables the isolation of the failed sensor in the duplex operation and the detection of a sensor failure in the simplex operation. The effectiveness of the proposed methods is demonstrated by using an extensive simulation model of a small commercial aircraft, developed by airframe and control system manufacturers on the basis of an existing business jet. Furthermore, the system has been successfully evaluated and compared to standard techniques by means of pilot-in-the-loop simulations on the Research Flight Simulator of the National Aerospace Laboratory in The Netherlands. This application, developed within a Brite/EuRam research project, is characterized by the effective combination of novel soft computing techniques with standard, well proven methods of the aircraft industry. The properties of the conventional sensor management system have been retained, with the additional advantage that the quality of the consolidated signal is improved, the failure-induced transients are reduced, and the consolidated signal remains available up to the last valid sensor.

Presentation and description of two classification methods using data fusion based on sensor management

The application of multi-sensor fusion, which aims at recognizing a state among a set of hypotheses for object classification, is of major interest with regard to the performance improvement brought by the sensor complementarity. Nevertheless, this needs to take into account the most accurate information and take advantage of the statistical learning of the previous measurements acquired by sensors. When previous learning is not representative of real measurements provided by the sensors, the classical probabilistic fusion methods lack performance. The Dempster–Shafer theory is then introduced to face this disadvantage by integrating further information which is the context of the sensor acquisitions. In this paper, we propose a model formalism for the sensor reliability in a context that leads to two methods of integration when all the hypotheses, associated to the objects of the scene acquired by sensors, are previously learned: the first one models the integration of this further information in the fusion rule as degrees of trust and the second models the sensor reliability directly as probability mass. These two methods are based on the theory of fuzzy events. Simulations of typical cases are developed in order to define the respective validity domains of these two methods. Afterwards, we are interested in the development of these two methods in the case where the previous learning is unavailable for an object and a global method of contextual information integration can be deduced.

A Monitoring Sensor Management System for Grid Environments

Large distributed systems such as Computational Grids require a large amount of monitoring data be collected for a variety of tasks such as fault detection, performance analysis, performance tuning, performance prediction, and scheduling. Ensuring that all necessary monitoring is turned on and that data is being collected can be a very tedious and error-prone task. We have developed an agent-based system to automate the execution of monitoring sensors and the collection of event data.

Sensor management – what, why and how

Abstract This paper presents a comprehensive discussion and systematic classification of the sensor management (SM) with respect to its roles, concepts, architectures and techniques. The SM is put in perspective with the discussion of the sensing system framework. The role of SM was presented based on the different level of its functionality. The architectures discussed in this paper include centralized, decentralized and hierarchical methods. SM techniques such as scheduling and decision-making techniques are discussed. A generic SM framework is also presented.

INFORMATION BLENDING IN VIRTUAL ASSOCIATIVE NETWORKS: A NEW PARADIGM FOR SENSOR INTEGRATION

Research reported in this article is motivated, in part, by current U.S. military programs aimed at the development of efficient data integration and sensor management methods capable of handling large sensor suites and achieving robust target recognition performance in real time scenarios. Modern sensor systems have shown good recognition abilities against a few isolated targets. However, these capabilities decline steeply when multiple sensors are acting against large target groups under realistic conditions requiring dynamic allocation of the sensor resources and efficient on-line integration and disambiguation of multiple sensor outputs. Neural networks and other sensor integration technologies have been inspired by cognitive models attributing human perceptual integration to parallel processing and convergence of simultaneous data streams. This article explores a different model emphasizing serial processing and association of consecutive memory traces in the Long Term Memory (LTM) into a globally connected memory structure called a Virtual Associative Network (VAN). Information integration in VAN is called blending. Target representation is constructed dynamically from the segments of virtual net matched serially against the input segments in the Short Term Memory (STM). This article will elaborate the concept of blending, reference its biological foundations, explain the difference between information blending and conventional sensor fusion techniques, and demonstrate blending applications in a large scale sensor management task.

Ultra-reliable real-time control systems-future trends

Today's aircraft use ultra-reliable real-time controls for demanding functions such as Fly-By-Wire (FBW) flight control. Future aircraft, spacecraft and other vehicles will require greater use of these types of controls for functions that currently are allowed to fail, fail to degraded operation, or require human intervention in response to failure. Fully automated and autonomous functions will require ultra-reliable control. But ultra-reliable systems are very expensive to design and require large amounts of on-board equipment. This paper will discuss how the use of low-cost sensors with digital outputs, digitally commanded fault-tolerant actuation devices and interconnecting networks of low-cost data buses offer the promise of more affordable ultra-reliable systems. Specific technologies and concepts to be discussed include low-cost automotive and industrial data buses, "smart" actuation devices with integral fault masking capabilities, management of redundant sensors, and the fault detection and diagnosis of the data network. The advantages of integrating the control and distribution of electrical power with the control system will be illustrated. The design, installation, and upgrade flexibility benefits provided by an all-digital and shared network approach will be presented. The economic benefits of systems that can operate following failure and without immediate repair will be reviewed. The inherent ability of these redundant systems to provide effective built-in test and self-diagnostics capabilities will be described. The challenges associated with developing ultra-reliable software for these systems and the difficulties associated with exhaustive verification testing will be presented as will additional development hurdles that must be overcome.

Sensor measurement scheduling: an enhanced dynamic, preemptive algorithm

This paper presents an enhanced architecture for a sensor measurement scheduler as well as a dynamic sensor scheduling algorithm called the On-line, Greedy, Urgency-driven, Preemptive Scheduling Algorithm (OGUPSA). The premise is that the function of sensor management can be partitioned into the two tasks of information management, essentially an information to measurement mapping, and a sensor scheduler which takes the measurement requests along with their priorities and optimally maps them to a set of sensors. OGUPSA was developed using the three main scheduling policies of Most-Urgent-First to pick a task, EarliestCompleted-First to select a sensor, and Least-Versatile-First to resolve ties. By successive application of these policies. OGUPSA dynamically allocates, schedules, and distributes a set of measurement tasks from an information manager among a set of sensors. OGUPSA can detect the failure of a measurement task to meet a deadline and improves the dynamic load balance among all sensors while being a polynomial time algorithm. One of the key components of OGUPSA is the information in the applicable sensor table. This table is the mechanism that is used to assign requested tasks to specific sensors. Subject terms: sensor scheduling, sensor management, resource scheduling, sensors

Target identification with Bayesian networks in a multiple hypothesis tracking system

A multisensor fusion algorithm that integrates model-based target identification (ID) and multiple-hypothesis tracking (MHT) is de- scribed. The algorithm augments the target state representation to in- clude ID information and to manipulate the new target state. The major innovation of the algorithm is using Bayesian networks to modify the hypothesis-evaluation process by taking into account target ID. This re- search was conducted as part of a larger effort to design a decision- theoretic sensor management system. In the system, two types of sen- sors, electronically scanned radar (ESA) and IR search and track (IRST) are assumed to be available. The ESA radar is modeled to have search and update capabilities as well as three radar identification modes: ultra- high-resolution radar (UHRR), radar signal modulation (RSM), and radar electronic support mode (RESM). A Bayesian network is used to model the detection and observation processes for these ID techniques and compute the association likelihoods between measurements and tracks. © 1997 Society of Photo-Optical Instrumentation Engineers.

Discrimination gain to optimize detection and classification

A method for managing agile sensors to optimize detection and classification based on discrimination gain is presented. Expected discrimination gain is used to determine threshold settings and search order for a collection of discrete detection cells. This is applied in a low signal-to-noise environment where target-containing cells must be sampled many times before a target can be detected or classified with high confidence. The goal of sensor management is interpreted here to be to direct sensors to optimize the probability densities produced by a data fusion system that they feed. The use of discrimination is motivated by its interpretation as a measure of the relative likelihood for alternative probability densities. This is studied in a problem where a single sensor can be directed at any detection cell in the surveillance volume for each sample. Bayes rule is used to construct a recursive estimator for the cell target probabilities. The expected discrimination gain is predicted for each cell using its current target probability estimates. This gain is used to select the optimal cell for the next sample. The expected discrimination gains can be maintained in a binary search tree structure for computational efficiency. The computational complexity of this algorithm is proportional to the height of the tree which is logarithmic in the number of detection cells. In a test case for a single 0 dB Gaussian target, the error rate for discrimination directed search was similar to the direct search result against a 6 dB target.

Systematic methods for knowledge acquisition and expert system development (for combat aircraft)

Nine cooperating rule-based systems, collectively called AUTOCREW which were designed to automate functions and decisions associated with a combat aircraft's subsystems, are discussed. The organization of tasks within each system is described; performance metrics were developed to evaluate the workload of each rule base and to assess the cooperation between the rule bases. Simulation and comparative workload results for two mission scenarios are given. The scenarios are inbound surface-to-air-missile attack on the aircraft and pilot incapacitation. The methodology used to develop the AUTOCREW knowledge bases is summarized. Issues involved in designing the navigation sensor selection expert in AUTOCREW's NAVIGATOR knowledge base are discussed in detail. The performance of seven navigation systems aiding a medium-accuracy inertial navigation system (INS) was investigated using Kalman filter covariance analyses. A navigation sensor management (NSM) expert system was formulated from covariance simulation data using the analysis of variance (ANOVA) method and the ID3 algorithm. >

Quantitative knowledge acquisition for expert systems

A common problem in the design of expert systems is the definition of rules from data obtained in system operation or simulation. Whilte it is relatively easy to collect data and to log the comments of human operators engaged in experiments, generalizing such information to a set of rules has not previously been a straightforward task. This paper presents a statistical method for generating rule bases from numerical data, motivated by an example based on aircraft navigation with multiple sensors. The specific objective is to design an expert system that selects a satisfactory suite of measurements from a dissimilar, redundant set, given an arbitrary navigation geometry and possible sensor failures. This paper describes the systematic development of a Navigation Sensor Management (NSM) Expert System from Kalman Filter covariance data. The development method invokes two statistical techniques: Analysis of Variance ( ANOVA) and the ID3 algorithm. The ANOVA technique indicates whether variations of problem parameters give statistically different covariance results, and the ID3 algorithm identifies the relationships between the problem parameters using probabilistic knowledge extracted from a simulation example set. ANOVA results show that statistically different position accuracies are obtained when different navigation aids are used, the number of navigation aids is changed, the trajectory is varied, or the performance history is altered. By indicating that these four factors significantly affect the decision metric, an appropriate parameter framework was designed, and a simulation example base was created. The example base contained over 900 training examples from nearly 300 simulations. The ID3 algorithm was then applied to the example base, yielding classification “rules” in the form of decision trees. The NSM expert system consists of seventeen decision trees that predict the performance of a specified integrated navigation sensor configuration. The performance of these decision trees was assessed on two arbitrary trajectories, and the performance results are presented using a predictive metric. The test trajectories used to evaluate the system's performance show that the NSM Expert adapts to new situations and provides reasonable estimates of sensor configuration performance.

Data Fusion and Decision Support for Command and Control

The problems and benefits of integrated data fusion and decision support are discussed across the spectrum of antisubmarine, tactical air, and land warfare. Then a general architecture of activities is proposed. This architecture includes data association, multitarget tracking, data combination, sensor management, situation assessment, and alternative analysis.

Extension of the midvalue selection technique for redundancy management of inertial sensors

In many redundant inertial sensor applications, the primary goal is absolute tolerance to some specified number of failures. To achieve this tolerance, a sufficient performance margin must be provided to cover the worst-case degradation that can occur. The worst-case degradation depends upon the particular algorithm used. An optimal algorithm can be defined implicitly by taking that value which minimizes an appropriately chosen worst-case performance index as the estimate. In the important case of first-failure tolerance with five skewed sensors, a simple realization has been discovered which is the direct generalization of midvalue selection to three dimensions.

Systems analysis of operational data from a multiphasic screening center

Operational data from a multiphasic test center operated by a city health department was subjected to systems analysis to derive implications for improved sensor management. The actual data process used facilities in Richmond, Va., Portland, Ore., and Washington, D. C., and required several weeks to complete assembly. The process had 3808 subjects at an average flow rate of 1.49 subjects per minute. Of this group, 1530 subjects accepted an offer of a free electrocardiogram to be given a week later, but only 82.1 percent actually took the later test. The data analysis was confined to 361 males past the age of 40, but was further restricted to those 252 with complete records. A simulated data process with capability for on-line test modification to assemble partial records and to give retest if necessary was studied. In addition to control of patient apathy and early treatment of suspect individuals, additional benefits may be possible. All abnormal X-rays were found in subjects whose other data considered as an ensemble predicted suspected trouble. The complete system viewpoint implemented by suitable processors may permit more effective use of extremely scarce talent, such as that of radiologists.