Dr. David M. Jenkins Profile

Dr. David M. Jenkins

at Pennsylvania State Univ

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Publications (2)

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Proceedings Article | 3 May 2019 Paper

Modified transmitted reference technique for multi-resolution radar timing and synchronization

Jason Young, Ram Narayanan, David Jenkins

Proceedings Volume 11003, 110031A (2019) https://doi.org/10.1117/12.2518616

KEYWORDS: Receivers, Transmitters, Signal processing, Modulation, Telecommunications, Radar, Signal to noise ratio, Error analysis, Wireless communications, Data communications

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A novel approach to radar signal design and processing, the modified transmitted reference (MTR) technique enables multi-resolution timing and synchronization properties for radar systems. The MTR approach performs parasitic processing by supplementing existing signals in the ambient environment. The MTR technique augments ambient signals by generating copies of the waveforms, modifying the copies, and broadcasting the copies. The MTR technique encodes timing information via the relative differences between the ambient and copy signals, rather than the absolute properties of the signal. The MTR method modifies the copies in the ambiguity function domain, thus producing signals with desirable features for radar applications while concurrently facilitating robustness to adverse radar channels. In this paper, the modified transmitted reference technique is developed, and an application of the technique to the problem of wireless beaconing is presented. Modified transmitted reference wireless beaconing employs spatially distributed, coordinated signal transceivers that broadcast and collect wide-bandwidth data. Each transceiver coordinates with all other nodes to emit a cooperative waveform that defines a modified transmitted reference beaconing transmission. At signal reception, modified transmitted reference receivers processes the signals reflected from objects in the environment using the approach presented herein. Modified transmitted reference wireless beaconing systems enable discovery and localization of objects of interest, and may find utility in a broad range of applications.

Proceedings Article | 3 May 2019 Paper

Non-cooperative emitter classification and localization with vector sensing and machine learning in indoor environments

Donald Hall, Ram Narayanan, David Jenkins, Erik Lenzing

Proceedings Volume 11003, 1100310 (2019) https://doi.org/10.1117/12.2519661

KEYWORDS: Feature extraction, Receivers, Machine learning, Polarization, Global system for mobile communications, Sensors, Received signal strength, Transmitters

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Advancements in wireless technology have led to an increased demand in the enhancement of wireless security, especially in indoor environments as GPS and cellular services degrade in performance. Recent developments in wireless security for indoor environments have focused mainly on developing radio frequency fingerprinting approaches through machine learning for device classification or localization. The work performed and discussed herein describes a developed system that can simultaneously perform device classification and localization in indoor environments using designed vector sensing antenna and artificial intelligence concepts. The devices evaluated are considered to be non-cooperative emitters that convey wideband code division multiple access (WCDMA) information found in universal mobile telecommunication systems (UMTS). However, the designed approaches can be extended to other protocols such as Global System for Mobile communications (GSM), Long-Term Evolution (LTE), and Code Division Multiple Access (CDMA). Device classification is performed in line-of-sight (LoS) scenarios with a developed vector sensor based on statistical features are extracted from the received power spectra and evaluated by two machine learning models, i.e. support vector machine (SVM) and weighted-K-nearest neighbor (WKNN). The final analysis experimentally validates the localization of the UMTS devices in an indoor environment by means of a comparison between dimensionally reduced features extracted from a short-time Fourier transform matrix along with three-dimensional received signal strength features, all acquired by the designed vector sensor antenna. Extension to other wireless protocols is assessed by evaluation of narrowband GSM signals for localization whilst being compared to the localization performance of the wideband UMTS non-cooperative emitters via WKNN.

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