The Dempster-Shafer Theory, a generalization of the Bayesian theory, is based on the idea of belief and as such can handle ignorance. When all of the required information is available, many data fusion methods provide a solid approach. Yet, most do not have a good way of dealing with ignorance. In the absence of information, these methods must then make assumptions about the sensor data. However, the real data may not fit well within the assumed model. Consequently, the results are often unsatisfactory and inconsistent. The Dempster-Shafer Theory is not hindered by incomplete models or by the lack of prior information. Evidence is assigned based solely on what is known, and nothing is assumed. Hence, it can provide a fast and accurate means for multi- sensor fusion with ignorance. In this research, we apply the Dempster-Shafer Theory in target tracking and in gait analysis. We also discuss the Dempster- Shafer framework for fusing data from a Global Positioning System (GPS) and an Inertial Measurement Unit (IMU) sensor unit for precise local navigation. Within this application, we present solutions where GPS outages occur.

关键词：光机电；传感器；融合；DS证据方法

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This report documents the study of baseline sensor technology for enabling condition based maintenance plus in Army ground vehicles. The sensor study was driven from Failure Mode Effects Analysis (FMEA) conducted on four high cost driver components in Army ground vehicles by Tank Automotive Research, Development and Engineering Center (TARDEC). The four high cost driver components in Army ground vehicles as identified by TARDEC are engines, transmissions, batteries, and alternators. This report provides an assessment of current ground vehicle sensor systems and new baseline sensor technologies that may be used to support prognostic/diagnostic fault mode coverage including structural and component health monitoring for enabling condition based maintenance plus (CBM +) strategies to increase the operational availability of Army ground vehicles.

关键词：光机电；传感器；地面车辆；汽车零部件；蓄电池

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Designing a high frequency (HF) magnetic field direction finding (DF) array for use onboard a military aircraft is the challenge that drives the effort of the research presented. The frequency range of interest, 2-32 MHz, has a maximum wavelength (150 meters) that exceeds the maximum length of any platform in the USAF inventory. The large wavelengths in the HF range make it difficult to accurately estimate from which direction a magnetic field is emitting. Accurate DF estimates are necessary for search and rescue operations and geolocating RF emitters of interest. The primary goal of this research is to characterize the performance of the MGL-S8A (Multi-Gap loop) B-Dot sensor. Although the sensors are designed to operate at frequencies above 5 GHz, their small size and potential to accurately detect magnetic fields in the 2-32 MHz range make them likely to be one type of an ensemble of sensors in the design of a HF DF array. The sensors are characterized in the azimuthal angles of 0, 45, -45, 90, and -90 degrees. Each sensor is characterized using two different types of magnetic field generators: a transverse electromagnetic (TEM) cell and a Helmholtz coil. The TEM cell generates a consistent magnetic field that acts as the input to the B-Dot sensor. The second type of magnetic field generator used, which is the secondary objective of this research, is a Helmholtz coil. An ideally designed Helmholtz coil is intended to be an inexpensive alternative to help in the characterization of B-Dot sensors in the HF range. The sensors can accurately measure electromagnetic (EM) fields in the HF range. Although the detection capability of the sensors is good, small differences between the 0 and 45 degree measurements may make it difficult for the sensors to be used in a DF array.

关键词：光机电；传感器；磁场；高频

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This technical report is duplicative documentation of the approved doctorial thesis of one of the co-authors. The research was funded in part by the U.S. Army Aviation and Missile Research, Development, and Engineering Center (AMRDEC) for the development of micromechanical sensors under the Army Technology Objective (ATO) of Sensor, Warhead, and Fuzing Technology Integrated for Combined Effects (SWFTICE). Particular technical progress at AMRDEC within this report includes resonant array processing (Chapter 3), electrets integration with Microelectromechanical Systems (MEMS) with localized heater fabrication for wafer bonding and microcharging grids for in-situ charging using microcoronas (Chapter 4), and processing of MEMS transducers (Chapter 5).

关键词：光机电；微机电系统；传感器；探测器；导弹；加热器；光谱

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The Dual Optical Embedded Dust Sensor (DOEDS) is designed for the sensitive, accurate detection of particles for preventive health monitoring of the AGT1500 engine and M1 Abrams/Ground Combat Vehicles (GCVs). DOEDS is a real- time sensor that uses an innovative combination of optical particle sensing technologies and mechanical packaging in a rugged, compact and non-intrusive optical design. The optical sensor, implementing both a single particle sensor and a mass sensor, can operate in harsh environments (up to 400 deg F) to meet the particle size, size distribution, mass concentration, and response time criteria. The sensor may be flush- or inline-mounted in multiple engine locations and environments.

关键词：光机电；传感器；光学内嵌；车辆预警

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Obtaining accurate non-cooperative geolocation is vital for persistent surveillance of a hostile emitter. Current research for developing a small, cheap and energy efficient sensor network for non-cooperative geolocation measurements via received signal strength (RSS) is limited. Most existing work focuses on simulating a non-cooperative network (NN) and in doing so, simulated models often ignore localization errors caused from the hardware processing raw RSS data and often model environment-dependent errors as random. By comparing real-time measured non-cooperative geolocation data to a simulated system a more accurate model can be developed. This thesis discusses the development and performance of a small, low cost, low complexity, and energy efficient sensor network that can locate a NN via RSS. The main focus of this research effort is designing a Poor Man's Spectrum Analyzer (PMSA) to locate a wireless device in a non-cooperative network (NN) that is transmitting in the Industrial, Scientific and Medical (ISM) radio band of 2.403 GHz to 2.48 GHz by measuring the emitter's received signal strength (RSS).

关键词：光机电；传感器；发射器；无线方向查找器

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Emerging applications of wireless sensor networks (WSNs) require real-time quality of service (QoS) guarantees to be provided by the network. Traditional analysis work only focuses on the first-order statistics, such as the mean and the variance of the QoS performance. However, due to unique characteristics of WSNs, a cross-layer probabilistic analysis of QoS performance is essential. In this dissertation, a comprehensive cross-layer probabilistic analysis framework is developed to investigate the probabilistic evaluation and optimization of QoS performance provided by WSNs. In this framework, the distributions of QoS performance metrics are derived, which are natural tools to discover the probabilities to achieve given QoS requirements. Compared to first-order statistics, the distribution of these metrics reveals the relationship between the performance of QoS-based operations and the probability to achieve the performance.

关键词：光机电；传感器；无线网络；QoS分析

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This report documents the idea or concept of operating an existing Silicon-on-Insulator (SOI) Micro-ElectroMechanical Systems (MEMS) gyroscopic sensor previously developed in the early-2000s under MEMS-Based Angular Rate Sensor (MBARS) and MicroControlled Array Sensors (mCAS). This report serves as documented evidence for future use of this open-sourced idea of the measurement of two-axis of linear acceleration from a previous sensor originally designed to operate as a single-axis rotation sensor.

关键词：光机电；传感器；硅绝缘体；微机电系统

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No abstract available.

关键词：光机电；传感器；人工神经网络；海洋

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Modern nuclear facilities, such as reprocessing plants, present inspectors with significant challenges due in part to the sheer amount of equipment that must be safeguarded. The Sandia-developed and patented Knowledge Generation system was designed to automatically analyze large amounts of safeguards data to identify anomalous events of interest by comparing sensor readings with those expected from a process of interest and operator declarations. This paper describes a demonstration of the Knowledge Generation system using simulated accountability tank sensor data to represent part of a reprocessing plant. The demonstration indicated that Knowledge Generation has the potential to address several problems critical to the future of safeguards. It could be extended to facilitate remote inspections and trigger random inspections. Knowledge Generation could analyze data to establish trust hierarchies, to facilitate safeguards use of operator-owned sensors.

关键词：光机电；传感器；数据集；知识生成系统

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