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UAS Continuous Time Spectrum Situational Awareness


OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Trusted AI and Autonomy The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: “UAS Continuous Time Spectrum Situational Awareness,” research topic is to apply the advantages of continuous time signal processing over traditional DSP for advanced Spectrum Situational Awareness. Shannon’s sampling theorem [1] limits current spectrum situational awareness systems. Continuous time signal processing [2]-[5] is not limited by Shannon’s sampling theorem and provides significant advantages [2]-[6] and [9]-[14] over conventional digital signal processing. DESCRIPTION: Advanced Spectrum Situational Awareness is required for improved threat detection and Future Tactical UAS applications. UAS swarms require accurate timing and position information for navigation and information fusion. A recent example showing the capabilities for swarm navigation and control was the opening display at the Tokyo 2020 Olympic Games [15]. A drone swarm created a rotating globe over Olympic stadium. Advanced UAS sensor networks will provide data and sensor fusion for the future transparent battlefield [16]-[20]. Transparent Battlefield requires advanced spectrum situational awareness to enable first identification of threats and prevent adversaries from gaining an advantage. Warfighter Network needs to operate in a contested environment using advanced spectrum situational awareness and spectrum management. For commanders, spectrum situational awareness, transparent battlefield and warfighter networks provide a decisive decision and time advantage over peer adversaries. “Continuous Time Spectrum Situational Awareness” research topic seeks to bring the advantages of continuous time systems over conventional digital systems to Spectrum Situational Awareness and swarm sensor and fusion networks. Continuous time (CT) digital signal processing (DSP) is an emerging subfield of signal processing [2]-[6]. CT is asynchronous (no clock) like analog signal processing [2]-[6]. Continuous time [2]-[6] has the time domain properties of analog signal processing with the benefits of digital signal processing without discrete time limitations, quantization error, and Shannon sampling limitations [2]-[4]. Another benefit of CT systems is adaptive sampling. For a 2.5 second electrocardiogram (ECG) data set, a continuous-time, 32-level, level crossing ADC only requires 225 samples compared to 1250 samples for conventional digital signal processing. Fewer samples result in less data processing and lower energy. The medical community has recognized the benefits of improved accuracy and energy savings for processing ECG signals with CT systems [13]-[15]. Continuous time systems were first developed in the 1950’s for control system applications. In 1962, Inose, et al. [7] developed the asynchronous delta-sigma (ΔΣ) analog-to-digital converter. A much more accurate ΔΣ demodulator technique was developed by Lazar and Tóth [8] in 2004. In 2003, Tsividis published his research work on the benefits of continuous time systems: no quantization error, no discrete time lag, and no frequency aliasing [2]-[3]. In a continuous time system, “quantization” occurs when the input signal exactly equals a threshold level, resulting in no inherent quantization error. Schell and Tsividis developed a 16-level (4-bit equivalent), continuous-time ADC with better than 100 dB signal-to-noise-and distortion ratio (SNDR) using offline reconstruction [9]-[10]. Kurchuk et al. develop a GHz speed continuous time analog-to-digital converter in 2012 [11]. Jungwirth and Crowe [12] developed a continuous time pipeline analog-to-digital converter and continuous time software reconfigurable radio architecture. Machine learning/artificial intelligence concepts can be applied to continuous time systems for signal analysis and signal processing. Neural networks based on analog signal processing concepts can be directly mapped into continuous time systems. Spiking neural networks are similar to continuous time systems. The continuous time properties (1) sample frequency is proportional to the slope of the input signal (compressive sensing) and (2) vector outputs (time stamp, and amplitude level) may be very beneficial for deep neural networks and signal processing. This SBIR is a multidiscipline research effort, and researchers from several fields are required. Research team should include at a minimum researchers with significant experience in continuous time systems, spectrum estimation, signal processing, UAV swarms, sensor fusion, and machine learning/artificial intelligence. “Continuous Time Spectrum Situational Awareness,” research topic is to apply the advantages of continuous time signal processing over traditional DSP for advanced Spectrum Situational Awareness. This effort is to support spectrum situational awareness for PEO Aviation, Long Range Precision Fires and Air and Missile Defense Army Modernization Priorities, the Microelectronics Technology Focus Area and long-term development areas of Transparent Battlefield, Warfighter Network, and Gaining Decision Advantage. PHASE I: Conventional DSP systems are based on quantized, discrete time (digital). For the Phase I proposal, research team shall describe the feasibility (1)-(6) of developing a continuous time spectrum situational awareness system for UAS applications. (1) multidiscipline research team (2) advantages of continuous time spectrum situational awareness over conventional DSP. (3) benefits of adaptive sampling and sample rate is proportional to the slope of the input signal (4) conversion between continuous time and DSP. (5) how machine learning/artificial intelligence, convolutional neural networks, etc. can be applied and take advantage of continuous time systems. (6) propose a Future Tactical UAS application for continuous time spectrum situational awareness. For the phase I effort, the offeror shall demonstrate the feasibility and performance benefits of continuous time systems for spectrum situational awareness. Offeror shall develop models, simulations, prototypes, etc. to determine technical feasibility (1)-(6) of developing continuous time spectrum situational awareness. Offer shall develop test cases for comparing CT-DSP to DSP. PHASE II: Research team shall develop a Continuous Time Spectrum Situational Awareness System for Future Tactical UAS. Research team shall deliver a year 1 report and a year 2 report describing system architecture and test results. Offeror shall deliver to the government point of contact for test and evaluation: 1 prototype continuous time situational awareness system including all codes, software, etc. and licenses for all development tools to build and use the system. Research team shall provide 3 days of on-site training for the system. PHASE III DUAL USE APPLICATIONS: Offer shall commercialize CTDSP technology for both government and commercial application spaces. The development of continuous-time digital signal processing will enable significant leap-ahead technology for signal processing to support communications, remote sensing, and control. These technologies offer potential benefits across several fields including communications, telecom and sensor networks for both military and civilian applications. REFERENCES: 1. “Nyquist–Shannon sampling theorem,” accessed 11/25/2022. 2. Y. Tsividis, “Continuous-time digital signal processing,” Electronics Letters 39(21), 1551 (2003). 3. Y. Tsividis, "Event-Driven Data Acquisition and Digital Signal Processing—A Tutorial", IEEE Transactions on Circuits and Systems—II: Express Briefs, Vol. 57, No. 8 (2010). 4. M. Kurchuk, Signal Encoding and Digital Signal Processing in Continuous Time, Dissertation, Columbia University, 2011. 5. F. Aeschlimann, E. Allier, L. Fesquet, and M. Renaudin, “Asynchronous FIR filters: towards a new digital processing chain,” in 10th International Symposium on Asynchronous Circuits and Systems, 2004. Proceedings., Apr. 2004, pp. 198–206, doi: 10.1109/ASYNC.2004.1299303. 6. Z. Zhao and A. Prodic, “Continuous-Time Digital Controller for High-Frequency DC-DC Converters,” IEEE Transactions on Power Electronics, vol. 23, no. 2, pp. 564–573, Mar. 2008. 7. H. Inose, Y. Yasuda, J. Murakami, "A Telemetering System by Code Manipulation – ΔΣ Modulation", IRE Trans on Space Electronics and Telemetry, Sep. 1962, pp. 204-209. 8. A. A. Lazar and L. T. Toth, "Time encoding and perfect recovery of bandlimited signals," 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2003, pp. VI-709, doi: 10.1109/ICASSP.2003.1201780. 9. C. Vezyrtzis and Y. Tsividis, “Processing of signals using level-crossing sampling,” Jun. 2009, pp. 2293–2296. doi:10.1109/ISCAS.2009.5118257 10. M. Kurchuk, C. Weltin-Wu, D. Morche, and Y. Tsividis, “Event-Driven GHz-Range Continuous-Time Digital Signal Processor with Activity-Dependent Power Dissipation”, IEEE Journal of Solid-State Circuits, vol. 47, no. 9, pp. 2164-2173, September 2012. 11. P. Jungwirth and W. Crowe, “CT Pipeline Level Crossing ADC and Continuous Time Software Reconfigurable Radio,” ARL Tech Report 9497, June 2022. 12. A Antony, et al., Asynchronous Adaptive Threshold Level Crossing ADC for Wearable ECG Sensors. J Med Syst 43, 78 (2019). 13. X. Zhang and Y. Lian, "A 300-mV 220-nW Event-Driven ADC With Real-Time QRS Detection for Wearable ECG Sensors," in IEEE Transactions on Biomedical Circuits and Systems, vol. 8, no. 6, pp. 834-843, Dec. 2014, doi: 10.1109/TBCAS.2013.2296942 14. T. Marisa, et al., “Pseudo asynchronous level crossing ADC for ECG signal acquisition,” IEEE Transactions on Biomedical Circuits and Systems 11 (2) (Apr. 2017) 267–278. 15. 2020 Olympics: “Earth-Shaped Drone Display Flies Above Tokyo 2020 Olympics Opening Ceremony,” accessed 12/7/2022. 16. Y. Zhou, B. Rao and W. Wang, "UAV Swarm Intelligence: Recent Advances and Future Trends," in IEEE Access, vol. 8, pp. 183856-183878, 2020, doi: 10.1109/ACCESS.2020.3028865. 17. Z. Xiaoning, "Analysis of military application of UAV swarm technology," 2020 3rd International Conference on Unmanned Systems (ICUS), 2020, pp. 1200-1204, doi: 10.1109/ICUS50048.2020.9274974. 18. L. Giacomossi et al., "Autonomous and Collective Intelligence for UAV Swarm in Target Search Scenario," 2021 Latin American Robotics Symposium (LARS), 2021 Brazilian Symposium on Robotics (SBR), and 2021 Workshop on Robotics in Education (WRE), 2021, pp. 72-77, doi: 10.1109/LARS/SBR/WRE54079.2021.9605450. 19. M. R. Brust and B. M. Strimbu, "A networked swarm model for UAV deployment in the assessment of forest environments," 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2015, pp. 1-6, doi: 10.1109/ISSNIP.2015.7106967. 20. S. James, R. Raheb and A. Hudak, "UAV Swarm Path Planning," 2020 Integrated Communications Navigation and Surveillance Conference (ICNS), 2020, pp. 2G3-1-2G3-12, doi: 10.1109/ICNS50378.2020.9223005. KEYWORDS: Continuous time systems, Spectrum Situational Awareness, UAS
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