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Modern-day radar is used extensively in applications such as autonomous driving, robotics, air traffic control, and maritime operations. The commonality between the aforementioned examples is the underlying tracking filter used to process ambiguous detections and track multiple targets. In this paper, we present a Software-Defined Radio-based radar testbed that leverages controllable and repeatable large-scale wireless channel emulation to evaluate diverse radar applications experimentally without the complexity and expense of field testing. Through over-the-air (OTA) and emulated evaluation, we demonstrate the capa-bilities of this testbed to perform multiple-target tracking (MTT) via Joint Probabilistic Data Association (JPDA) filtering. This testbed features the use of flexible sub-6 GHz or mmWave operation, electromagnetic ray tracing for site-specific emulation, and software reconfigurable radar waveforms and processing. Although the testbed is designed generalizable, for this paper we demonstrate its capabilities using an advanced driver-assistance system radar application. 

Data files were used in support of the research paper titled “Mitigating RF Jamming Attacks at the Physical Layer with Machine Learning" which has been submitted to the IET Communications journal.

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All data was collected using the SDR implementation shown here: https://github.com/mainland/dragonradio/tree/iet-paper. Particularly for antenna state selection, the files developed for this paper are located in 'dragonradio/scripts/:'

• 'ModeSelect.py': class used to defined the antenna state selection algorithm
• 'standalone-radio.py': SDR implementation for normal radio operation with reconfigurable antenna
• 'standalone-radio-tuning.py': SDR implementation for hyperparameter tunning
• 'standalone-radio-onmi.py': SDR implementation for omnidirectional mode only

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Authors: Marko Jacovic, Xaime Rivas Rey, Geoffrey Mainland, Kapil R. Dandekar
Contact: krd26@drexel.edu

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Top-level directories and content will be described below. Detailed descriptions of experiments performed are provided in the paper.

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classifier_training: files used for training classifiers that are integrated into SDR platform

• 'logs-8-18' directory contains OTA SDR collected log files for each jammer type and under normal operation (including congested and weaklink states)
• 'classTrain.py' is the main parser for training the classifiers
• 'trainedClassifiers' contains the output classifiers generated by 'classTrain.py'

post_processing_classifier: contains logs of online classifier outputs and processing script

• 'class' directory contains .csv logs of each RTE and OTA experiment for each jamming and operation scenario
• 'classProcess.py' parses the log files and provides classification report and confusion matrix for each multi-class and binary classifiers for each observed scenario - found in 'results->classifier_performance'

post_processing_mgen: contains MGEN receiver logs and parser

• 'configs' contains JSON files to be used with parser for each experiment
• 'mgenLogs' contains MGEN receiver logs for each OTA and RTE experiment described. Within each experiment logs are separated by 'mit' for mitigation used, 'nj' for no jammer, and 'noMit' for no mitigation technique used. File names take the form *_cj_* for constant jammer, *_pj_* for periodic jammer, *_rj_* for reactive jammer, and *_nj_* for no jammer. Performance figures are found in 'results->mitigation_performance'

ray_tracing_emulation: contains files related to Drexel area, Art Museum, and UAV Drexel area validation RTE studies.

• Directory contains detailed 'readme.txt' for understanding.
• Please note: the processing files and data logs present in 'validation' folder were developed by Wolfe et al. and should be cited as such, unless explicitly stated differently. 
  • S. Wolfe, S. Begashaw, Y. Liu and K. R. Dandekar, "Adaptive Link Optimization for 802.11 UAV Uplink Using a Reconfigurable Antenna," MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM), 2018, pp. 1-6, doi: 10.1109/MILCOM.2018.8599696.

results: contains results obtained from study

• 'classifier_performance' contains .txt files summarizing binary and multi-class performance of online SDR system. Files obtained using 'post_processing_classifier.'
• 'mitigation_performance' contains figures generated by 'post_processing_mgen.'
• 'validation' contains RTE and OTA performance comparison obtained by 'ray_tracing_emulation->validation->matlab->outdoor_hover_plots.m'

tuning_parameter_study: contains the OTA log files for antenna state selection hyperparameter study

• 'dataCollect' contains a folder for each jammer considered in the study, and inside each folder there is a CSV file corresponding to a different configuration of the learning parameters of the reconfigurable antenna. The configuration selected was the one that performed the best across all these experiments and is described in the paper.
• 'data_summary.txt'this file contains the summaries from all the CSV files for convenience.

 

We present a step-by-step approach for designing a recofigurable Alford loop antenna (RALA). The design of an 3.5 GHz RALA is shown. The antenna is fabricated using a 1.6 mm thick double-sided FR4 substrate. We sweep antenna geometrical parameters and show the effect on antenna input impedance, reflection coefficient (S 11 ), and radiation patterns. The final antenna structure resonates at 3.5 GHz with eight directional and one omnidirectional radiation patterns. We also present a simplistic control circuit responsible for activating the antenna elements. Tri-state impedance matching- a major challenge in the design of RALA is also discussed and analyzed along with a proposed method for mitigation. 3D radiation patterns of the RALA was measured using an EMScan and a maximum gain of 4.5 dBi is found. 

Data files were used in support of the research paper titled "“Experimentation Framework for Wireless
Communication Systems under Jamming Scenarios" which has been submitted to the IET Cyber-Physical Systems: Theory & Applications journal. 

Authors: Marko Jacovic, Michael J. Liston, Vasil Pano, Geoffrey Mainland, Kapil R. Dandekar
Contact: krd26@drexel.edu

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Top-level directories correspond to the case studies discussed in the paper. Each includes the sub-directories: logs, parsers, rayTracingEmulation, results. 

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logs:    - data logs collected from devices under test
    - 'defenseInfrastucture' contains console output from a WARP 802.11 reference design network. Filename structure follows '*x*dB_*y*.txt' in which *x* is the reactive jamming power level and *y* is the jaming duration in samples (100k samples = 1 ms). 'noJammer.txt' does not include the jammer and is a base-line case. 'outMedian.txt' contains the median statistics for log files collected prior to the inclusion of the calculation in the processing script. 
    - 'uavCommunication' contains MGEN logs at each receiver for cases using omni-directional and RALA antennas with a 10 dB constant jammer and without the jammer. Omni-directional folder contains multiple repeated experiments to provide reliable results during each calculation window. RALA directories use s*N* folders in which *N* represents each antenna state. 
    - 'vehicularTechnologies' contains MGEN logs at the car receiver for different scenarios. 'rxNj_5rep.drc' does not consider jammers present, 'rx33J_5rep.drc' introduces the periodic jammer, in 'rx33jSched_5rep.drc' the device under test uses time scheduling around the periodic jammer, in 'rx33JSchedRandom_5rep.drc' the same modified time schedule is used with a random jammer. 

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parsers:    - scripts used to collect or process the log files used in the study
        - 'defenseInfrastructure' contains the 'xputFiveNodes.py' script which is used to control and log the throughput of a 5-node WARP 802.11 reference design network. Log files are manually inspected to generate results (end of log file provides a summary). 
        - 'uavCommunication' contains a 'readMe.txt' file which describes the parsing of the MGEN logs using TRPR. TRPR must be installed to run the scripts and directory locations must be updated. 
        - 'vehicularTechnologies' contains the 'mgenParser.py' script and supporting 'bfb.json' configuration file which also require TRPR to be installed and directories to be updated. 

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rayTracingEmulation:    - 'wirelessInsiteImages': images of model used in Wireless Insite
            - 'channelSummary.pdf': summary of channel statistics from ray-tracing study
            - 'rawScenario': scenario files resulting from code base directly from ray-tracing output based on configuration defined by '*WI.json' file 
            - 'processedScenario': pre-processed scenario file to be used by DYSE channel emulator based on configuration defined by '*DYSE.json' file, applies fixed attenuation measured externally by spectrum analyzer and additional transmit power per node if desired
            - DYSE scenario file format: time stamp (milli seconds), receiver ID, transmitter ID, main path gain (dB), main path phase (radians), main path delay (micro seconds), Doppler shift (Hz), multipath 1 gain (dB), multipath 1 phase (radians), multipath 1 delay relative to main path delay (micro seconds), multipath 2 gain (dB), multipath 2 phase (radians), multipath 2 delay relative to main path delay (micro seconds)
            - 'nodeMapping.txt': mapping of Wireless Insite transceivers to DYSE channel emulator physical connections required
            - 'uavCommunication' directory additionally includes 'antennaPattern' which contains the RALA pattern data for the omni-directional mode ('omni.csv') and directional state ('90.csv')

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results:    - contains performance results used in paper based on parsing of aforementioned log files