IEEE Aerospace and Electronic Systems Magazine - June 2020 - 20

Feature Article:

DOI. No. 10.1109/MAES.2019.2960904

Assessing Agile Spectrum Management for Cognitive
Radar on Measured Data
V. Carotenuto, A. Aubry, A. De Maio, N. Pasquino, University of Naples
Federico II
A. Farina, Technical Consultant, Rome, Italy

INTRODUCTION
Radio frequency (RF) electromagnetic spectrum is a limited natural resource necessary for an ever-growing number of services and systems. It is used in several
applications, such as mobile communications, radio and
television broadcasting, as well as remote sensing.
Together with oil and water, nowadays the RF spectrum
represents one of the most important, significant, crucial,
and critical commodities due to the huge impact of radio
services on the society. Both high-quality/high-rate wireless services (4G and 5G) as well as accurate and reliable
remote sensing capabilities, such as Air Traffic Control,
geophysical monitoring of Earth, defense and security
applications, call for increased amounts of bandwidth [1],
[2], [3], [4]. Besides, basic electromagnetic considerations, such as good foliage penetration [5], low-path-loss
attenuation, reduced size of the devices push some systems to coexist in the same frequency band [6] (for
instance VHF and UHF). As a result, the RF spectrum
congestion problem has been attracting the interest of
many scientists and engineers during the last few years
and is currently becoming one among the hot topics in
both regulation and research fields [7], [8], [9], [10].
Clearly, the mentioned RF spectrum congestion
problem calls for new and innovative methodologies
that allow a smart and efficient spectrum utilization

Authors' current address: V. Carotenuto, A. Aubry,
A. De Maio, and N. Pasquino, Department of Electrical
Engineering and Information Technology, University
of Naples Federico II, Naples 80125, Italy (e-mails:
vincenzo.carotenuto@unina.it; augusto.aubry@unina.it;
ademaio@unina.it; nicola.pasquino@unina.it). A. Farina,
Technical Consultant, Rome, Italy (e-mail: alfonso.
farina@outlook.it).
Manuscript received February 22, 2019, revised July 26,
2019, and ready for publication November 27, 2019.
Review handled by S. Br€
ueggenwirth.
0885-8985/19/$26.00 ß 2019 IEEE
20

accounting for the time-varying behavior of the emitters
in the operational scenario. In this respect, the cognitive
radar paradigm [11], [12], [13], [14], [15], [16], [17],
[18], enabling spectral awareness and the subsequent
ability to anticipate the actions of radiators, appears as a
very promising solution to counter emerging threats in
today's dynamic and ever-changing environment, especially in urban centers where spectrum is severely congested [1]. Indeed, relying on real-time situational
awareness as well as new computing architectures, highspeed and off-the-shelf processors, arbitrary digital
waveform generators, solid-state transmitters, active
phased arrays, etc., it is possible to dynamically and cognitively select the probing waveforms in response to the
changing conditions so as to enhance the radar performance while controlling its impact on the other surrounding RF systems.
Not surprisingly, many radar waveform design algorithms have been proposed in the open literature to guarantee some degrees of spectral compatibility between active
surveillance systems and surrounding licensed emitters [19],
[20], [21], [22], [23]. Their theoretical effectiveness has
been widely assessed in nominal conditions, namely without
accounting for model mismatches induced by hardware
imperfections such as quantization errors, carrier offsets,
phase noise, high-power amplifier nonlinearity effects,
just to list a few. Such distortions may significantly alter
the shape of the transmitted waveform resulting in both radar
performance loss as well as spectral compatibility
impairments [24], [25], [26], [27]. Finally, in [28] Ravenscroft et al. prove experimentally the effectiveness of
spectrally notched frequency-modulated waveforms to
ensure the spectral compatibility with overlaid communication systems in the presence of an amplifier operating
in saturation.
In this article, we provide a proof of concept for the
fast-prototyping of the next cognitive radar generation. To
fully exploit cognitive radar potentiality, we suppose
amplifiers operating in the linear region so as to capitalize

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