Results for “Received Signal Strength Indicators (RSSI)”

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↗

NASA NTRS2004

Directional Radio-Frequency Identification Tag Reader

A directional radio-frequency identification (RFID) tag reader has been designed to facilitate finding a specific object among many objects in a crowded room. The device could be an adjunct to an electronic inventory system that tracks RFID-tagged objects as they move through reader-equipped doorways. Whereas commercial RFID-tag readers do not measure directions to tagged objects, the device is equipped with a phased-array antenna and a received signal-strength indicator (RSSI) circuit for measuring direction. At the beginning of operation, it is set to address only the RFID tag of interest. It then continuously transmits a signal to interrogate that tag while varying the radiation pattern of the antenna. It identifies the direction to the tag as the radiation pattern direction of peak strength of the signal returned by the tag. An approximate distance to the tag is calculated from the peak signal strength. The direction and distance can be displayed on a screen. A prototype containing a Yagi antenna was found to be capable of detecting a 915.5-MHz tag at a distance of approximately equal to 15 ft (approximately equal to 4.6 m).

Medelius, Pedro J.↗

NASA NTRS2015

Systems and Methods for RFID-Enabled Dispenser

Methods, apparatuses and systems for radio frequency identification (RFID)-enabled information collection are disclosed, including an enclosure, a collector coupled to the enclosure, an interrogator, a processor, and one or more RFID field sensors, each having an individual identification, disposed within the enclosure. In operation, the interrogator transmits an incident signal to the collector, causing the collector to generate an electromagnetic field within the enclosure. The electromagnetic field is affected by one or more influences. RFID sensors respond to the electromagnetic field by transmitting reflected signals containing the individual identifications of the responding RFID sensors to the interrogator. The interrogator receives the reflected signals, measures one or more returned signal strength indications ("RSSI") of the reflected signals and sends the RSSI measurements and identification of the responding RFID sensors to the processor to determine one or more facts about the influences. Other embodiments are also described.

Fink, Patrick W.↗

NASA NTRS2016

Systems and Methods for RFID-Enabled Pressure Sensing Apparatus

Methods, apparatuses and systems for radio frequency identification (RFID)-enabled information collection are disclosed, including an enclosure, a collector coupled to the enclosure, an interrogator, a processor, and one or more RFID field sensors, each having an individual identification, disposed within the enclosure. In operation, the interrogator transmits an incident signal to the collector, causing the collector to generate an electromagnetic field within the enclosure. The electromagnetic field is affected by one or more influences. RFID sensors respond to the electromagnetic field by transmitting reflected signals containing the individual identifications of the responding RFID sensors to the interrogator. The interrogator receives the reflected signals, measures one or more returned signal strength indications ("RSSI") of the reflected signals and sends the RSSI measurements and identification of the responding RFID sensors to the processor to determine one or more facts about the influences. Other embodiments are also described.

Fink, Patrick W.↗

NASA NTRS2016

System and Method for RFID-Enabled Information Collection

Methods, apparatuses and systems for radio frequency identification (RFID)-enabled information collection are disclosed, including an enclosure, a collector coupled to the enclosure, an interrogator, a processor, and one or more RFID field sensors, each having an individual identification, disposed within the enclosure. In operation, the interrogator transmits an incident signal to the collector, causing the collector to generate an electromagnetic field within the enclosure. The electromagnetic field is affected by one or more influences. RFID sensors respond to the electromagnetic field by transmitting reflected signals containing the individual identifications of the responding RFID sensors to the interrogator. The interrogator receives the reflected signals, measures one or more returned signal strength indications ("RSSI") of the reflected signals and sends the RSSI measurements and identification of the responding RFID sensors to the processor to determine one or more facts about the influences. Other embodiments are also described.

Fink, Patrick W.↗

NASA NTRS2017

Systems and Methods for RFID-Enabled Pressure Sensing Apparatus

Methods, apparatuses and systems for radio frequency identification (RFID)-enabled information collection are disclosed, including an enclosure, a collector coupled to the enclosure, an interrogator, a processor, and one or more RFID field sensors, each having an individual identification, disposed within the enclosure. In operation, the interrogator transmits an incident signal to the collector, causing the collector to generate an electromagnetic field within the enclosure. The electromagnetic field is affected by one or more influences. RFID sensors respond to the electromagnetic field by transmitting reflected signals containing the individual identifications of the responding RFID sensors to the interrogator. The interrogator receives the reflected signals, measures one or more returned signal strength indications ("RSSI") of the reflected signals and sends the RSSI measurements and identification of the responding RFID sensors to the processor to determine one or more facts about the influences. Other embodiments are also described.

Fink, Patrick W.↗

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↗