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Results for “Received Signal Strength Indicator”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Frequency Emitter Geolocation Using Signal Strength Fingerprinting Informed By 3D Propagation Modeling

This work focuses on the problem of RF geolocation in complex multipath environments. Using 3D electromagnetic propagation modeling to characterize environments of interest will enable more accurate RF geolocation. Specifically, a path-loss radio map can be generated in simulation for use in received signal strength indicators (RSSI) fingerprinting, or pattern matching. RSSI fingerprinting is an example of data-based method that takes site specific information into account which should allow for better performance than other model-based methods that use a generalized model of electromagnetic propagation. This modeling capability will also be used to evaluate the relative performance of RSSI fingerprinting, pathloss model based RSSI methods such as differential received signal strength circles (DRSS), RSSI joint gaussian estimation, and time-difference of arrival (TDOA). New methods using this simulation derived electromagnetic characterization could improve the efficacy of currently deployed and future RF spectral monitoring solutions. Wireless InSite developed by Remcom is used as the simulation tool of choice in this work. An indoor location is simulated with a grid of fixed receivers and a grid of transmit locations. Using the output of the Wireless InSite simulation the response from a given transmit location to a given receive location can be generated. During the first year of the project various geolocation methods evaluated on purely synthetic, but realistic, data. The second year focused on testing and validating the efficacy of simulation informed RF geolocation using two physical testbeds. This work has shown that data-based approaches are more accurate than model-based ones at the expensive of requiring measured or simulated site-specific training data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Autonomous Wireless Technology Detection in Seamless IoT Applications

The ever-increasing use of Internet of Things (IoT) devices results in the implementation of multiple wireless technologies that would not only cater their data rate requirements but also support various applications. To optimize the energy efficiency and security of the wireless transmission, it is imperative to identify the wireless technologies in various IoT implementations. Many of the existing approaches are based on measuring only the receiving signal strength indicator (RSSI). However, such approaches may not work well because of transmit power control and complex channel variations among different wireless technologies. In this article, we propose an autonomous wireless detection scheme that considers multiple distinguishable physical (PHY)-layer settings for real-time identification of wireless technologies for real-time applications. Specifically, the proposed scheme relies on the PHY-layer measurements of the targeted spectrum. Transmission settings, such as bandwidth, carrier frequency, and RSSI are estimated from the raw in-phase and quadrature-phase (I/Q) measurements. In addition, a symbol-level extraction scheme is implemented to extract unique features of modulation settings. These aforementioned features are applied to a machine learning process to identify the received wireless technologies. Compared with raw I/Q measurements, the extracted features are much simplified and, thus, the machine learning classifier can be designed with a simple structure for fast processing on IoT nodes. Finally, the proposed schemes are primarily evaluated theoretically, followed by implementing them on a USRP software-defined radio (SDR)-based hardware testbed. The evaluation results demonstrate high accuracy in the real-time detection of different wireless technologies for seamless IoT applications.

42 ENGINEERING↗
DOE OSTI2024

Overview of RFID Applications Utilizing Neural Networks

As Radio Frequency Identification (RFID) methods continue to evolve to higher levels of complexity, one form of machine learning is making its appearance. The use of Neural Networks (NN) in the RFID field is steadily increasing, and in the fields of localization and activity recognition, promising results are being shown from a variety of research. RFID applications fall primarily under two types of problems including regression and classification. We analyze RIFD localization techniques which fall under regression, and activity recognition which falls under classification. Many works don’t classify themselves as activity recognition methods, but because they fall under the classification category, we still consider them as activity recognition techniques. This research overviews the Neural Network models in the localization field based on whether they can perform independently of the environment in which they were tested. For activity recognition and accessory fields, the major methods involve tag-based and tag-free approaches. In conclusion, after the models are surveyed, a comparison study is given to examine what may be the cause for increased accuracy between different Neural Network models.

42 ENGINEERING↗
DOE OSTI2023

Indoor Wireless Localization of Uncooperative Sources Using a Ray Tracing Model

Indoor geolocation of radio frequency (RF) trans- mitters is challenging due to site-specific multipath effects, especially when the sources are uncooperative. A source being uncooperative means that a-priori the transmit time, location, and power are unknown. It is proposed to use a site-specific ray tracing model to generate realistic indoor RF responses, perform geolocation using multiple methods, and compare their performance. This work will look at two types of geolocation algorithms: (1) time-difference of arrival (TDOA) and (2) re- ceived signal strength Indicator (RSSI) fingerprinting or pattern- matching-based methods. An indoor space will be simulated with a grid of fixed receivers and a grid of transmit locations using Remcom’s Wireless InSite. Using the output of the Wireless InSite simulation the response from any given transmit location can be generated and used to evaluate geolocation performance.

indoor source localization, Wireless InSite, re- c↗
DOE OSTI2020

Introductory guide to backgrounds in XPS spectra and their impact on determining peak intensities

Photoelectron and Auger peaks are central to most of the important uses of x-ray photoelectron spectroscopy (XPS), and thus, they receive the most attention in many types of analysis. Quantitative chemical analysis using XPS requires the assessment of the intensities of the photoemission peaks of the elements detected. Determination of peak intensities requires separation of the photoelectron peak signals from the background on which the peaks rest. For the determination of peak area intensities, the background is subtracted from overall signal intensity. The spectral background is also critical when peak fitting is used to determine intensities of overlapping peaks, and the model of background used in this process can impact the results. In addition to the impact on quantitative analysis, information about the depth distribution of elements in the near surface region can often be obtained by visual inspection of the background and quantified using appropriate modeling. Here, this introductory guide provides some basic information about backgrounds in the XPS analysis, describes the types of background models that are commonly used, suggests some of their strengths and weaknesses, and provides examples of their use and misuse. Although the fundamental nature of some components of the background signals in XPS is not understood, indicating that none of the models in use are fully correct and the area is subject to active research, appropriate good practices have been established for most routine analysis. The guide describes good practices, identifies errors that frequently appear in the literature, and uses examples to demonstrate the impacts of background selections on determinations of peak intensities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗