In this work, we present the framework surrounding the development of a mmW radar image-based algorithm for wire recognition and classification for rotorcraft operation in degraded visual environments. While a mmW sensor image lacks the optical resolution and perspective of an IR or LIDAR sensor, it currently presents the only true see-through mitigation under the heaviest of degraded vision conditions. Additionally, the mmW sensor produces a high-resolution, radar map that has proven to be exceedingly interpretable, especially to a familiar operator. Seizing on these clear advantages, the mmW radar image-based algorithm is trained and evaluated against independent mmW imagery data collected from a live flight test in a relevant environment. The foundation of our approach is based on image processing and machine learning techniques utilizing radar-based signal properties and sensor and platform information for added robustness. We discuss some of the requirements and practical challenges of a standalone algorithm development, and lastly, present some preliminary examples using existing development tools and discuss the path for continued advancement and evaluation.
Advanced spread spectrum linear frequency modulated (LFM) waveforms are being developed for advanced capability synthetic aperture radar (SAR) and ground moving target indication (GMTI) applications. We have demonstrated by analysis and simulation the feasibility of these new type waveforms and are now in the process of implementing them in hardware. The basic approach is to combine a traditional LFM radar waveform with a direct sequence spread spectrum (DSSS) waveform, and then on receive to de-spread the return and capture the resultant LFM return for traditional matched filter processing and enhanced SAR and GMTI. We show the analysis, simulation and some preliminary hardware results.
A small and lightweight dual-channel radar has been developed for SAR data collections. Using
standard Displaced Phase Center Antenna (DPCA) radar digital signal processing, SAR GMTI images have
been obtained. The prototype radar weighs 5-lbs and has demonstrated the extraction of ground moving
targets (GMTs) embedded in high-resolution SAR imagery data. Heretofore this type of capability has been
reserved for much larger systems such as the JSTARS. Previously, small lightweight SARs featured only a
single channel and only displayed SAR imagery. Now, with the advent of this new capability, SAR GMTI
performance is now possible for small UAV class radars.
KEYWORDS: Radar, Signal to noise ratio, Unmanned aerial vehicles, Extremely high frequency, Stars, Synthetic aperture radar, Image processing, Field programmable gate arrays, Signal processing, Antennas
Goleta has been developing low-cost and lite-weight MMW SAR / MTI radars for small UAS applications. Initial
models of two different radars have been built, the LUAVR and the LCLPR. The current LUAVR (Lite-weight UAV
radar) configuration weighs in at 18-lbs and the first LCLPR version (Low-Cost Low-Power Radar) weighs in at a little
under 2-lbs. Initial testing was done from the roof of a van simulating a low flying UAV. Currently the LUAVR is
flying in an ultra-lite as part of a UAS demonstration system. The system is comprised of both airborne and ground
segments with a data link connecting the two. SAR and MTI Imagery have been generated.
An ISAR image formation approach has been developed that incorporates advanced imaging and exploitation techniques for non-cooperative moving target feature extraction and ATR. A unique signal based motion compensation algorithm has been developed that works for both SAR and ISAR. Advanced Time-frequency (T-F) processing has been incorporated, which includes both slow time-Doppler frequency and fast time-RF.
A Focal Plane Array (FPA) Radar is being developed to image objects and personnel in enclosed areas. An FPA radar continuously receives energy from every angular resolution cell in the Field-of-View. Thus, an optimal signal processor must process, in real time, a large number of simultaneous channels, 216 in the current configuration. A DSP-based processor has been developed to achieve this goal.