Distributed Spectrum Operations: Sensing/Geolocation & Communications

This effort comprises two main efforts: distributed spectrum sensing for emitter identification and geolocation, and distributed array beamforming for low-cost, low-power, long-range communications and radar. Distributed sensing is a receive-only operation and uses our custom-designed RadioHound spectrum sensing platform while distributed beam-forming requires transceivers which are realized using X-microwave modules and implement a wideband (100 MSPS complex DAC/ADC) SDR tunable from S- through X-band. Both of these efforts are in collaboration with the US Army Research Laboratory operating under a Cooperative Agreement to jointly explore hardware and algorithms for distributed sensor/radio operation at the tactical edge.

Distributed Array Beamforming for Long-range Communications and Radar

Using two-way time-transfer methods and time-reversal signal processing we are developing distributed arrays of radios with phase memory in order to realize distributed array operations in a frequency hopping environment. This effort includes hardware development and demonstration with custom hardware developed with X-microwave modules, as well a simulation framework to model complicated electromagnetic scenarios.

Distributed Spectrum Sensing

Through a collaboration with the US Army Research Laboratory and the Notre Dame Wireless Institute we have developed a suite a low-cost, low-power, and compact spectrum sensors called RadioHound. The goal of this effort is to design the hardware and algorithms necessary for an ultra-low-cost ($1), low-power (100's milliwatts) distributed sensor network for electromagnetic emitter geolocation and pervasive spectrum sensing. In collaboration with the Army Research Laboratory we have deployed a collection of 18 portable, weather-proof spectrum sensor packages interconnected through MANET (mobile ad-hoc network) radios which facilitate rapidly deployed, mobile experiments both indoor and outdoor. Sensors run on the RaspberryPi SBC with a custom RF HAT expansion board, the BeagleBone Black with a custom RF CAPE expansion board, and the software is even copatible with the Ettus SDR platforms. The in-house developed RF expansion boards provide frequency tuning from 25 MHz to 6 GHz and report all data back to a cloud-connected database and for processing with cloud-connected algorithmic signal processing compute paltforms. 


In the limit of sensor simplicity we are targeting a $1, 100 milliwatt sensor based on a 1-bit RSSI (power) meter. The sensor omits all components except the fundamental frequency conversion operation to save cost and power and instead relies upon increased density of deployment made possible by such "disposable" sensors. The figure below is a simulation of our simple spectrum sensor vision. A network of one-bit RSSI-based sensors combined with contour-map-based processing is able to effectively localize emitters with a modest increase in sensor density over high dynamic range sensor hardware but is able to save orders of magnitude in cost and power. The below figure shows a map of the San Jose valley with six emitters (red 'X'), iso-power contours based on a network of one-bit sensors, and the estimated emitter location based on centroid computations. Because the sensor is ~$1 and ~100 milliwatts, a sensor density can be 3x the density of ideal infinite dynamic range sensors but the total network is much lower cost and much lower power. Each sensor can be considered "disposable" at this level. The sensors in the simulation omit LNAs, use starved-LO single-diode mixers, permit image folding, and use single-bit threshold detectors. Backhaul connection (data exfiltration) is considered a separate system.


Using the RadioHound platform we have also developed low-complexity machine learning classification algorithms for physical layer fingerprinting of radio emitters. We have also used a custom simulation framework to explore the salient features of radio emitters in order to aide in low-complexity, but highly accurate classification. RadioHound has been deployed on drones in order to perform waveform identification in crowded spectrum environments. 

Relevant Publications:

[1]  J.C. Merritt IV and J.D. Chisum, ``High-Speed Cross-Correlation for Spectrum Sensing and Direction Finding of Time-Varying Signals,'' IEEE Sensors J., vol. 18, no. 15, pp. 6161-6168, August 1, 2018. https://ieeexplore.ieee.org/abstract/document/8385152/

[2]  N. Kleber, C. Dietlein, and J. Chisum, ``Cooperative Cross-Correlation Algorithm To Optimize Linearity of Fused RF Sensor Measurements,'' IEEE Sensors J., vol. 20, no. 7, pp. 3766-3776, published Dec 13, 2019, printed Apr 1, 2020. https://ieeexplore.ieee.org/abstract/document/8932572

[3]  J. Merritt IV, C. Dietlein, J. Chisum, ``Collaborative and Responsive Sensors for Low-cost Spectrum Sensing and Geolocation,'' in Proc. 9th NATO Mil. Sens. Symp. (SET-241), Quebec City, Canada, Jun 2, 2017. https://www.sto.nato.int/publications/STO\%20Meeting\%20Proceedings/STO-MP-SET-241/MP-SET-241-13-4.pdf

[4]  N. Kleber, A. Termos, G. Martinez, J. Merritt, B. Hochwald, J. Chisum, A.D. Striegel, J.N. Laneman, ``RadioHound: A Pervasive Sensing Platform for Sub-6 GHz Dynamic Spectrum Monitoring'', Proc. of the 2017 IEEE Int. Symp. on Dynamic Spectrum Access Networks (DySPAN), Baltimore MD, pp. 1-2, Mar 6-9, 2017. https://ieeexplore.ieee.org/document/7920764      

Funding: US Army Research Laboratory award #W911NF-16-2-0140, Dept. of Navy and Kostas Research Institute for Homeland Security award #555007.