Low SWaP-C Radar for Urban Air Mobility

A method is developed and tested for extending the range of low-cost radar chipsets for use in urban air mobility (UAM) vehicles. The method employs weak-signal correlation techniques and long measurement intervals to achieve a 1 km range. Low-cost radar is an enabling technology for vertical take-off and landing (VTOL) aircraft envisioned for large-scale deployment in urban areas. These aircraft must be autonomously piloted to make them economically feasible, but autonomous systems have yet to match a human pilot's ability to detect and avoid (DAA) obstacles. Visible light cameras are useful for this application, but cameras alone are insufficient, as they are fundamentally unable to resolve range. Existing commercial radar units would suffice for DAA, but their large size weight, power, and cost (SWaP-C) militates against their application to UAM. The technique detailed in this paper is a fused camera-radar solution that exploits the camera's excellent angular resolution to guide radar signal processing so that signals arriving from a camera-detected target are combined constructively. Such guided processing significantly extends the range of low SWaP-C radar chipsets, making them useful for DAA. An analysis of the fused technique's robustness to target velocity uncertainty is presented, along with experimental results indicating that a typically-sized VTOL aircraft would be detectable at a range of 1 km.

[1]  P. Walsh,et al.  HIGH PERFORMANCE INTEGRATED 24 GHz FMCW RADAR TRANSCEIVER CHIPSET FOR AUTO AND INDUSTRIAL SENSOR APPLICATIONS , 2015 .

[2]  Aaron Mcfadyen,et al.  A Survey of autonomous vision-based See and Avoid for Unmanned Aircraft Systems , 2016 .

[3]  Mark A. Richards.  Noncoherent Integration Gain , and its Approximation , 2010 .

[4]  Timothy Molloy,et al.  Below Horizon Aircraft Detection Using Deep Learning for Vision-Based Sense and Avoid , 2019, 2019 International Conference on Unmanned Aircraft Systems (ICUAS).

[5]  Kristopher Ellis,et al.  Experimental Evaluation of PICAS: An Electro-Optical Array for Non-Cooperative Collision Sensing on Unmanned Aircraft Systems , 2017 .

[6]  Rafael Apaza,et al.  Urban Air Mobility Airspace Integration Concepts and Considerations , 2018, 2018 Aviation Technology, Integration, and Operations Conference.

[7]  Jason J. Ford,et al.  Characterization of Sky‐region Morphological‐temporal Airborne Collision Detection , 2013, J. Field Robotics.

[8]  F. Jung,et al.  Products , 1968, ADHESION ADHESIVES&SEALANTS.

[9]  Sanjiv Singh,et al.  Prototype Sense-and-Avoid System for UAVs , 2009 .

[10]  William C. Barott,et al.  Passive multispectral sensor architecture for radar-EOIR sensor fusion for low SWAP UAS sense and avoid , 2014, 2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014.

[11]  Jason J. Ford,et al.  Learning to Detect Aircraft for Long-Range Vision-Based Sense-and-Avoid Systems , 2018, IEEE Robotics and Automation Letters.

[12]  William Adams,et al.  Analysis of alerting performance for detect and avoid of unmanned aircraft systems , 2016, 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS).

[13]  P. Lawson,et al.  Federal Communications Commission , 2004, Bell Labs Technical Journal.

[14]  Jason J. Ford,et al.  A vision-based sense-and-avoid system tested on a ScanEagle UAV , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[15]  Timothy Molloy,et al.  Quickest Detection of Intermittent Signals With Application to Vision-Based Aircraft Detection , 2018, IEEE Transactions on Control Systems Technology.