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Multi-level approach to sensor management

Multi-level approach to sensor management

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sensor fusion: architectures, algorithms, and applications iv

作者： Stromberg, Dan Swedish Defence Research Establishment Sweden

The main issue considered is how to configure a sensor control system for fighter-based ESA-oriented multifunction and multisensor capability. The main structure should be simple, robust and stable, but it is also important to consider the relationship between fine sensor parameters and the large scaled sensor allocation mechanism. An important ontology to tracking and sensor management is given in [1]. Control of complex sensor control mechanisms, especially when an electronically steered array antenna (ESA) is present, requires however applicable software architectures. Two proposals will be given for that, agent modelling and multilevel sensor management architecture. Agent modelling may help to keep the principles of system clean, simple and robust, while the multi-level system architecture will help to organize the relationship between distributed processes in the system. For example, several sensor-oriented tracking processes may be used for the same target, but also other process types may be distributed, such as situation awareness and sensor management. Multilevel architectures will also facilitate a subdivision of the manufacturing process between different subcontractors.

关键词： sensor data fusion

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Real time sensor validation and fusion for distributed autonomous sensors

Real time sensor validation and fusion for distributed auton...

引用

conference on Multisensor, Multisource Information fusion

作者： Yuan, XJ Li, XS Buckles, B Tulane Univ EECS Dept New Orleans LA 70118 USA

ISBN: (纸本)0819453579

Multi-sensor data fusion has found widespread applications in industrial and research sectors. The purpose of real time multi-sensor data fusion is to dynamically estimate an improved system model from a set of different data sources, i.e., sensors. This paper presented a systematic and unified real time sensor validation and fusion framework (RTSVFF) based on distributed autonomous sensors. The RTSVFF is an open architecture which consists of four layers - the transaction layer, the process fusion layer, the control layer, and the planning layer. This paradigm facilitates distribution of intelligence to the sensor level and sharing of information among sensors, controllers, and other devices in the system. The openness of the architecture also provides a platform to test different sensor validation and fusion algorithms and thus facilitates the selection of near optimal algorithms for specific sensor fusion application. In the version of the model presented in this paper, confidence weighted averaging is employed to address the dynamic system state issue noted above. The state is computed using an adaptive estimator and dynamic validation curve for numeric data fusion and a robust diagnostic map for decision level qualitative fusion. The framework is then applied to automatic monitoring of a gas-turbine engine, including a performance comparison of the proposed real-time sensor fusion algorithms and a traditional numerical weighted average.

关键词： real time sensor validation and fusion distributed sensor system design dynamic validation curve file based knowledge system confidence weighted averaging robust diagnostic maps

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Robust multi-source sound localization using temporal power fusion

Robust multi-source sound localization using temporal power ...

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conference on sensor fusion - architectures, algorithms, and applications V

作者： Aarabi, P Stanford Univ Dept Comp Sci Stanford CA 94305 USA

ISBN: (纸本)0819440809

In the past several years, many different algorithms have attempted to address the problem of robust multi-source time difference of arrival (TDOA) estimation, which is necessary for sound localization. Different approaches, including general cross correlation, multiple signal classification (MUSIC), and the maximum likelihood (ML) approach, have made different trade-offs between robustness and efficiency. A new approach presented here offers a much more efficient yet robust mechanism for TDOA estimation. This approach iteratively uses small sound signal segments to compute a local crosscorrelation based TDOA estimate. All of the different local estimates are combined to form the probability density function of the TDOA. Because the power of the secondary sources will be greater than the others for a certain set of the local signal segments, the TDOA corresponding to these sources will be associated with a peak in the TDOA probability density function. This way, the TDOAs of several different sources, along with their signal strength can be estimated. A real-time implementation of the proposed approach is used to show its improved accuracy and robustness. The system was consistently able to correctly localize sound sources with SNRs as low as 3 dB.

关键词： multi-source sound localization direction of arrival estimation beamforming temporal information fusion

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Fuser design for thick film pH sensor electrodes using empirical data

Fuser design for thick film pH sensor electrodes using empir...

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conference on sensor fusion - architectures, algorithms, and applications VI

作者： Wellington, SJ Atkinson, JK Sion, RP Southampton Inst Sch Comp & Digital Commun Southampton SO14 0RD Hants England

ISBN: (纸本)0819444812

An array of thick film pH sensor electrodes has been fused using two separate fuser designs: the feedforward neural network and Nadaraya-Watson kernel estimator. In both cases the fuser is based on empirical data rather than analytical sensor models. Complementary sensor responses have been obtained by fabricating sensors using different metal oxides. This approach provides some immunity to interference caused by the ionic composition of the solution being sensed. The Nadaraya-Watson estimator is shown to provide a useful alternative to the feedforward neural network for multisensor fusion where sensor distributions are unknown. Indicative test results are provided for the measurement of pH in printing ink. The results confirm that the fused results are more accurate than those obtained using the single best sensor, or simple fusion schemes such as averaging or majority voting.

关键词： pH electrode thick film sensor multisensor fusion Nadaraya-Watson artificial neural network

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Neuro-Dynamic Programming for adaptive fusion complexity control

Neuro-Dynamic Programming for adaptive fusion complexity con...

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conference on sensor fusion - architectures, algorithms, and applications III

作者： Ross, KN Chaney, RD ALPHATECH Inc Burlington MA 01803 USA

ISBN: (纸本)0819431931

The prodigious amount of information provided by surveillance systems and other information sources has created unprecedented opportunities for achieving situation awareness. Because the mission's and user's needs are constantly evolving, fusion control strategics must adapt to these changing requirements. However, the optimal control problem with the desired adaptive control capabilities is enormously complex. Therefore, we solve the adaptive fusion control problem approximately using a methodology called Neuro-Dynamic Programming (NDP) that combines elements of dynamic programming, simulation-based reinforcement learning, and statistical inference techniques. This work demonstrates the promise of using NDP for adaptive fusion control by using it to allocate computational resources to Bayesian belief networks that use a variety of data types (e.g., SAR, MTI, ELINT, and terrain databases) to track and identify clusters of vehicles. We have significantly extended previous work by using NDP to adapt the fusion process itself in addition to deciding which clusters should get their inference updated. fusion within the Bayesian networks was adapted by using NDP to select the subset of available data to be used when updating the inference. We also extended previous work by using a dynamic scenario with moving vehicles for training and testing models.

关键词： adaptive fusion control Neuro-Dynamic Programming Bayesian networks reinforcement learning

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Cognitive engineering in algorithm development for multisensor data fusion in military applications

Cognitive engineering in algorithm development for multisens...

引用

conference on Multisensor, Multisource Information fusion

作者： Kight, AC Narayanan, S Wright State Univ Dept Biomed Ind & Human Factors Engn Dayton OH 45435 USA

ISBN: (纸本)0819453579

In battlefield situations, human operators are bombarded with substantial amounts of information and expected to make near-instantaneous decisions. The large amounts of information, coupled with short decision times and the need to reduce the potential of making incorrect decisions, create the possibility for information overload. This problem is especially prominent in military applications involving imagery from multiple sensors. Computer-based algorithms for fusing pertinent sets of imagery have proven somewhat useful for alleviating this problem. However, little research has been done on designing multisensor data fusion systems using principles of cognitive engineering, which involves the consideration of human cognition during the design process. The design of a sensor fusion system using principles from cognitive engineering would create a more natural relationship between human and machine, and would thus be extremely effective in reducing operator error in military situations. This paper explores the need for integrating human reasoning and cognition in algorithm development for multisensor fusion applications.

关键词： Algorithm development Data fusion Defense technologies Cognition Image fusion Error analysis Image sensors sensor fusion sensors

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Image fusion with the hybrid evolutionary algorithm and response analysis

Image fusion with the hybrid evolutionary algorithm and resp...

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conference on Multisensor, Multisource Information fusion

作者： Maslov, iv CUNY Dept Comp Sci Grad Ctr New York NY 10016 USA

ISBN: (纸本)0819457981

Information fusion is a rapidly developing research area aimed at creating methods and tools capable of augmenting security and defense systems with the state-of-the-art computational power and intelligence. An important part of information fusion, image fusion serves as the basis for a fully automatic object and target recognition. Image fusion maps images of the same scene received from different sensors into a common reference system. Using sensors of different types gives rise to a problem of finding a set of invariant features that help overrun the imagery difference caused by the different types of sensors. The paper describes an image fusion method based on the combination of the hybrid evolutionary algorithm and image local response. The latter is defined as an image transform R(V) that maps an image into itself after a geometric transformation A(V) defined by a parameter vector V is applied to the image. The transform R(V) identifies the dynamic content of the image, i.e. the salient features that are most responsive to the geometric transformation A(V). Moreover, since R(V) maps the image into itself, the result of the mapping is largely invariant to the type of the sensor used to obtain the image. Image fusion is stated as the global optimization problem of finding a proper transformation A(V) that minimizes the difference between the images subject to fusion. Hybrid evolutionary algorithm can be applied to solving the problem. Since the search for the optimal parameter vector V is conducted in the response space rather than in the actual image space, the differences in the sensor types can be significantly alleviated.

关键词： information fusion image fusion target recognition optimization hybrid evolutionary algorithm response analysis

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Level 5: User refinement to aid the fusion process

Level 5: User refinement to aid the fusion process

引用

conference on Multisensor, Multisource Information fusion - architectures, algorithms and applications 2003

作者： Blasch, EP Plano, S Wright State Univ Dept Elect Engn Dayton OH 45435 USA

ISBN: (纸本)0819449598

The revised JDL fusion model Level 4 "process refinement" covers a broad spectrum of actions such as sensor management and control. A limitation of Level 4 is the purpose of control - whether it be for user needs or system operation. Level 5, User Refinement, is a modification to the Revised JDL model that distinguishes between machine process refinement and user refinement. User refinement can either be human control actions or refinement of the user's cognitive model. In many cases, fusion research concentrates on the machine and does not take full advantage of the human as not only a qualified expert to refine the fusion process, but also as customer for whom the fusion system is designed. Without user refinement, sensor fusion is incomplete, inadequate, and the user neglects its worthiness. To capture user capabilities, we. explore the concept of user refinement through decision and action based on situational leadership models. We develop a Fuse-Act Situational User Refinement (FASUR) model that details four refinement behaviors: Neglect, Consult, Rely, and Interact and five refinement functions: Planning, Organizing, Coordinating, Directing, and Controlling. Process refinement varies for different systems and different user information needs. By designing a fusion system with a specific user in mind, vis 'Level 5', a fusion architecture can meet user's information needs for varying situations, extend user sensing capabilities for action, and increase the human-machine interaction.

关键词： sensors Data modeling Data fusion sensor fusion Cognitive modeling Control systems Telecommunications Systems modeling Environmental sensing Surveillance

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Discrete dynamic approach to the multisensory multitrack fusion

Discrete dynamic approach to the multisensory multitrack fus...

引用

sensor fusion: architectures, algorithms, and applications iv

作者： Tomasik, Jerzy A. Universite Blaise Pascal Aubiere France

The present paper explores the dynamic level of information sensory fusion which is to be appropriate for hardware implementations. We associate to multitracking sensors their abstractions, being discrete time multihead state circuits. We presume sensors are to be independent from each other and there are no direct interfaces between them. The fusion is achieved by sensor-to-sensor track association which is controlled by the global state transition system. We investigate synchronous and asynchronous fusion models over common and distributed resource spaces and we compare the recognition capacities of these and some other models, like Turing Machines, stack automata etc. Then the fusioned circuits are applied to analyze arithmetical predicates, social games and an unsolved `Syracuse Conjecture'.

关键词： sensor data fusion

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Pulse coupled neural network sensor fusion

Pulse coupled neural network sensor fusion

引用

conference on sensor fusion - architectures, algorithms, and applications II

作者： Johnson, JL Schamschula, MP Inguva, R Caulfield, HJ USA AMCOM Redstone Arsenal AL 35809 USA

ISBN: (纸本)0819428256

Perception is assisted by sensed impressions of the outside world but not determined by them. The primary organ of perception is the brain and, in particular, the cortex. With that in mind, we have sought to see how a computer-modeled cortex - the PCNN or Pulse Coupled Neural Network - performs as a sensor fusing element. In essence, the PCNN is comprised of an array of integrate-and-fire neurons with one neuron for each input pixel. In such a system, the neurons corresponding to bright pixels reach firing threshold faster than the neurons corresponding to duller pixels. Thus, firing rate is proportional to brightness. In PCNNs, when a neuron fires it sends some of the resulting signal to its neighbors. This linking can cause a near-threshold neuron to fire earlier than it would have otherwise. This leads to synchronization of the pulses across large regions of the image. We can simplify the three-dimensional PCNN output by integrating out the time dimension. Over a long enough time interval, the resulting two-dimensional (x,y) pattern IS the input image. The PCNN has taken it apart and put it back together again. The shorter-term time integrals are interesting in themselves and will be commented upon in the paper. The main thrust of this paper is the use of multiple PCNNs mutually coupled in various ways to assemble a single two-dimensional pattern or fused image. Results of experiments on PCNN image fusion and an evaluation of its advantages are our primary objectives.

关键词： Image fusion Neurons sensors Data modeling RGB color model Neural networks Modulation Pulse generators sensor fusion Image processing

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