Visualization of Real-Time Radar Data by Integration of X-Band Software

Visualization of Real-Time Radar Data by Integration of X-Band Software

G. Javidi (Virginia State University, Petersburg, VA, USA), E. Sheybani (Virginia State University, Petersburg, VA, USA) and D. Mason (Virginia State University, Petersburg, VA, USA)
DOI: 10.4018/IJITN.2014100108


To have a fully functional FMCW X-band radar for the SMARTLabs ACHIEVE trailer, it is necessary to produce code to retrieve data from an FPGA board linked to the radar, calculate Fourier transforms and display the power spectrum in near-real time using a computer code based on freely available scientific development tools. In order for the communication between the FPGA board and the computer to be reliable and accurate, developing a specific format through the use of C was an initial step. This was followed by the development of a method to visualize data efficiently. In this case, Python, along with its matplotlib, SciPy, and NumPy modules, were used. Both programs were then integrated together within a graphical user interface.
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The use of radar is important in the detection of both stationary objects, such as buildings, and moving objects, such as clouds and aircraft. Radar has varying levels of frequency with the measurements ranging anywhere from megahertz to gigahertz (Skolnik 1962). An X-Band radar frequency can range anywhere between 8 and 12.5 GHz (Skolnik 1962). The radar that is focused on throughout the course of this project was an X-Band, frequency modulated continuous wave (FMCW) radar with frequency of 10 GHz. FMCW radar utilizes frequency modulation of a continuous signal to acquire range information. One prior example of such a radar includes the PILOT radar which was “used by warships for navigation where the ability to perform accurate navigation in poor weather is essential for the accomplishment of the ships' tactical mission” (Stove 1992). Another example is the use of scanning X-band radar, paired with FMCW K-band radar, in an experiment by Joel Van Baelan, Frederic Tridon, and Yves Pointen to retrieve accurate rainfall rate estimates (Baelen 2009).

Benefits of this type of radar are that it is not just safe and inexpensive, but also serves as a means in filling in gaps of higher powered pulse-doppler radars when used in conjunction with them (Gabriel 2011). This proves especially important for SMARTLabs (Surface-based Mobile Atmospheric Research and Testbed Laboratories), which consists of the three mobile laboratories. The mobile trailers are SMART (Surface-sensing Measurements for Atmospheric Radiative Transfer), COMMIT (Chemical, Optical & Microphysical Measurements of In-situ Troposphere), and ACHEIVE (Aerosol-Cloud-Humidity Interaction Exploring & Validating Enterprise). The SMART trailer utilizes active and passive sensors to collect data on the atmosphere, gaining more knowledge about surface energy. The focus of the COMMIT trailer is to collect and measure information on the microphysics of aerosols, such as particle size, in addition to gathering information about optical properties of aerosols, such as absorption and scattering. For the focus of this paper, the ACHEIVE trailer will be referenced. The purpose of the ACHEIVE trailer is to further the understanding of aerosol-cloud interactions by being able to probe cloud properties for SMARTLabs as a whole. Within the trailer, there are three different radars that work together: the W-band (94 GHz), the K-band(24 GHz), and the X-band. To successfully use this X-Band radar, pictured in Figure 1, it was important to first effectively retrieve data in a proper format and then visualize the data in an efficient and reliable manner. As of the date of this publication, there is not an efficient way of visualizing the data captured from this range of radar frequencies. This creates a serious issue in all the applications that use FMCW.

Figure 1.

The X-band radar methods


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