Airborne Multichannel Snow Thickness Sensing and Imaging Radar Based on FlexRIO

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"LabVIEW supports many instruments such as the FlexRIO, which can meet our requirement of developing the novel system. "

- Yan Li , Center for Remote Sensing of Ice Sheets (CReSIS)

The Challenge:
Developing new technologies and computer models to measure and predict the response of sea level change to the mass balance of ice sheets in Greenland, Antarctica and Arctic Ocean because sea ice in polar areas could significantly affect the climate of the world through global ocean circulation.

The Solution:
Using the NI 5761 high-speed digitizer and advanced NI PXIe-7975R FlexRIO FPGA module to create a data acquisition system that can record data from multiple channels with low data volume and perform real-time processing, and synchronizing the data from multiple channels through the trigger line in the backplane of the PXI-1085 chassis.

Author(s):
Yan Li - Center for Remote Sensing of Ice Sheets (CReSIS)
Zongbo Wang - Center for Remote Sensing of Ice Sheets (CReSIS)
Jie-Bang Yan - Center for Remote Sensing of Ice Sheets (CReSIS)
Carl Leuschen - Center for Remote Sensing of Ice Sheets (CReSIS)

Global Climate Change and the Rising Sea Level

Sea ice in the polar areas could significantly affect the climate of the world through global ocean circulation. Sea ice is always covered by snow with several meters of thickness, which modulates ice and atmosphere heat exchange. Knowing the thickness of the snow can help us better understand the changing of sea ice thickness.

Center for Remote Sensing of Ice Sheets

The Center for Remote Sensing of Ice Sheets (CReSIS) is a science and technology center established by the National Science Foundation (NSF) in 2005 with the mission of developing new technologies and computer models to measure and predict the response of sea level change to the mass balance of ice sheets in Greenland, Antarctica and Arctic Ocean.

CReSIS empowers students and faculties with opportunities to pursue exciting research in a variety of disciplines, collaborate with world-class scientists and engineers in the United States and abroad, and make meaningful contributions to the ongoing, urgent work of addressing the impact of climate change. With more than 10 years of expertise in designing, building and deploying radar systems, CReSIS can provide a wide range of high-quality radar systems including multichannel coherent radar depth sounder, accumulation radar, and snow radar, which have all been regularly deployed in Greenland, Antarctica and Arctic Ocean to survey ice sheet thickness and surface accumulation.

The Snow Radar System

Radar is an object-detection system that uses electromagnetic waves to determine the range, altitude, direction, or speed of objects. It can also be used to detect terrain and more. The radar antenna transmits electromagnetic pulses that are bounced off by any object in the path. The object reflects a part of the wave’s energy which is captured by the radar receiver after a certain time delay. The distance between the radar and the target can be determined by the delay. In the snow radar system, the reflection happens on both the air-snow and snow-ice interface, which can be used to determine the snow thickness. The signal-to-noise ratio (SNR) of the received signal, the critical metric of the radar system, is proportional to the signal amplitude and the pulse length. However, the higher amplitude may be beyond the dynamic range of the power amplifier, which diminishes system performance.

Using the frequency-modulated continuous waveform (FMCW) in our snow radar system can reduce the transmission power and achieve a higher compression gain. Instead of transmitting a pulsed wave, a continuous wave is transmitted. Due to the propagation delay between the transmitted signal and backscattered signal from the target, there is a frequency difference between the two signals, which is named “beat frequency”. We can easily prove that the range of the radar system is proportional to the beat frequency while the resolution is inversely proportional to the signal bandwidth.

In our snow radar system (Figure 1) which is in a standard rack, the system bandwidth is chosen to be 2–18 GHz to achieve finer target resolution. At the transmitter section, our direct digital synthesizer (DDS) with 2.5 GHz clock generates the baseband signal. The baseband signal is passed to the frequency multiplier to generate the RF signal, which is then amplified by the RF power amplifier. Finally, the signal is sent to the antenna for transmission. At the receiver side, the received signal is de-ramped with the reference signal after being amplified and filtered. The resulting beat frequency or intermediate frequency (IF) signal is fed into the analogy input of the NI 5761 high-speed digitizer.

Figure 1. The snow radar system

Snow Radar Digital Receiver

As mentioned, the range of the snow radar system is encoded by the beat frequency. In order to capture the received signal and determine the beat frequency, we chose the NI 5761 adapter module as our digitizer, which delivers 14 quantization bits and a 250 MS/s sample rate. Meanwhile, to maintain a lower data rate, we selected the single sample CLIP mode, which delivers a 125 MS/s sample rate. Consider that the nominal survey altitude is around 500 m; the IF signals is expected to fall within the fourth Nyquist zone (187.5 MHz to 250 MHz) of the ADC.

Moreover, to improve the SNR and clutter rejection capability through digital beamforming, an 8 x 1 receive antenna array is adopted in the snow radar system. Signals received from each antenna port have thus to be digitized individually and the data from different ADC boards are needed to be synchronized. Figure 2 shows that one of the boards is configured as the master board, which is responsible for sending the synchronization signal through the trigger line in the backplane of the chassis. Others are regarded as the slave, which receive the synchronization signal from the master. The NI PXIe-6674T timing module distributes the 125 MHz sample clock coming from the DDS to all the boards to guarantee that the clock signals simultaneously arrive at the different boards.

To decrease the data rate, we needed to perform the digital down converter (DDC) and decimation in the system. It is well known that the low pass filter is necessary to combat the aliasing induced by DDC and decimation. We can use LabVIEW to conveniently configure the finite impulse filter. By adopting DDC and decimation in our system, the data rate can be reduced by 16 times.

Finally, we can store the real-time data processed by the FPGA in the redundant array of independent disks (RAID) through the PCIe bus using the NI PXIe-8384 PCI Express control board. We can achieve a data rate of up to 300 MB/s, which guarantees accurate ice sheet thickness information.

Figure 2. The snow radar digital receiver

We installed the snow radar on the DHC-6 (Twin Otter) aircraft to measure sea ice of Arctic Ocean. In March 2015, the team from Naval Research Laboratory and CReSIS conducted more than 10 airborne radar surveys from the DHC-6 over Arctic Ocean in approximately a one month period in Barrow, AK. Figure 3 shows a sample radar echogram of the data collected. This image illustrates how internal layers, which vary in thickness down to a few centimeters, over fairly small distances, can be resolved with the snow radar system. We processed the preliminary image with traditional FMCW processing techniques, including the following steps: fast-time mean removal, coherent integration, coherent noise removal, fast Fourier transform, and incoherent averaging.

Figure 3. The sample echogram

Why Did CReSIS Choose LabVIEW?

First, it is convenient to use LabVIEW graphical development software to develop complicated tasks, such as the digital receiver, as opposed to the traditional development software like the Xilinx ISE. Moreover, we can access most of the hardware and functionality necessary for our system, which significantly reduces the development time. Another advantage of LabVIEW is that we can develop our own VIs using the software such as ISE, which provides development flexibility. Also, LabVIEW supports many instruments such as the FlexRIO, which can meet our requirement of developing the novel system.

Finally, it is easy and fast to produce good looking, intuitive GUIs, which help us focus on algorithm development rather than time consuming GUI development. In addition, the easily understood GUI means the radar operators can use and modify the system developed with LabVIEW.

Conclusion

So far, the snow radar has flown more than 48 sea ice flights. The snow radar digital receiver successfully recorded the data during these missions, which provided direct snow thickness measurements over large areas of Arctic and Antarctic sea ice for the first time.

This valuable data helps identify the effect of the global climate change on the ice sheet of the Arctic and Antarctic. The results also provide meaningful suggestions to the policy-maker to guide climate policies to protect our earth.


Acknowledgement

This work was supported by the Naval Research Laboratory under contract N00173-13-C-2032. Funding for the Arctic flight data was provided by the Naval Research Laboratory under Program Element 61153N. The early versions of the snow radar and chirp generator were developed with partial support from the National Science Foundation under Grant ANT-0424589 and by the National Aeronautics and Space Administration under Grant NNX10AT68G.

Author Information:
Yan Li
Center for Remote Sensing of Ice Sheets (CReSIS)
2335 Irving Hill Road
Lawrence, KS 66044
United States
Tel: (785) 727-9573
yanli_eecs@ku.edu

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