NRL Demonstrates First Autonomous Real-Time Hyperspectral Target Detection System Flown Aboard a Predator UAV


10/31/2000 - 60-00r
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Researchers at the Naval Research Laboratory (NRL) have demonstrated the first real-time hyperspectral target detection system flown aboard a Predator Unmanned Air Vehicle (UAV). The recently completed demonstration is called WAR HORSE (Wide Area Reconnaissance Hyperspectral Overhead Real-Time Surveillance Experiment). During the demonstration, data from a nadir-looking visible hyperspectral sensor were analyzed by an on-board real-time processor. When a target was detected, a high-resolution image was collected from a bore-sighted panchromatic visible sensor. A three-band false-color waterfall display of the hyperspectral data with overlaid target cues, along with the corresponding high resolution image chips, were transmitted to a ground station in real time via a digital RS-170 data link.

The NRL-developed hyperspectral sensor records reflected light in many narrow contiguous bands in the visible/near infrared wavelength region of the electromagnetic spectrum. The push-broom sensor consists of a grating spectrometer and a 1024x1024 custom charge-coupled device (CCD) camera. The sensor operates at a frame rate of 40 Hz and provides 1024 cross-track spatial pixels and 64 wavelength bands (450-900 nm). The NRL panchromatic imaging sensor also operates in the visible wavelength region. It consists of a CCD line scanner and a large format lens ( 300 mm). This sensor operates at a frame rate of 240 Hz and provides high-resolution imagery via 6000 cross-track spatial pixels. The systems signal processor and interface computer was developed by Space Computer Corporation and NRL and consists of two DSPs (digital signal processors) and a Pentium II processor. The DSPs perform all calibration and run a subspace RX anomaly detection algorithm in real time. A custom board encodes digital data to a video compatible analog output with forward error correction. By means of a RS-170 video data-link, data are transmitted to the ground in real time. A high-frame-rate video frame grabber and custom demodulation software decode the transmitted data, which consists of a false-color waterfall display, target cue information and corresponding high-resolution image chips.

The WAR HORSE system was flown on a Navy Predator UAV (from the Naval Postgraduate School) over the Camp Roberts training facility in California. Typical flight parameters for the Predator were an altitude of 10,000 feet and an airspeed of 70 knots. This provided a hyperspectral imager ground sampling dimension of approximately one meter and a high-resolution line scanner ground sampling dimension of approximately six inches.

This exercise was performed as part of the Optical Sciences Division's DARKHORSE Project, which is part of NRL's ONR-funded 6.2 Base Program. Over the past six years, this project has been developing algorithms to detect and classify ground targets in clutter and camouflage, based on their spectral, spatial and temporal signatures. In addition, this project first demonstrated autonomous, real-time detection and cueing from an airplane, first with a hyperspectral visible sensor then a hyperspectral long wave infrared sensor. The technology demonstrated has evident applicability to UAV and Uninhabited Combat Air Vehicle (UCAV) programs, as well as the HISTAR system on the EP-3 and the SHARP system on the F-18.



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The U.S. Naval Research Laboratory is the Navy's full-spectrum corporate laboratory, conducting a broadly based multidisciplinary program of scientific research and advanced technological development. The Laboratory, with a total complement of nearly 2,800 personnel, is located in southwest Washington, D.C., with other major sites at the Stennis Space Center, Miss., and Monterey, Calif. NRL has served the Navy and the nation for over 90 years and continues to meet the complex technological challenges of today's world. For more information, visit the NRL homepage or join the conversation on Twitter, Facebook, and YouTube.

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