M.Sc Student | Cohen David |
---|---|
Subject | High Resolution FDMA MIMO Radar with Sub-Nyquist Sampling |
Department | Department of Electrical Engineering | Supervisor | Professor Yonina Eldar |
Full Thesis text | ![]() |
Multiple input multiple output (MIMO) radars are
composed of several transmitters
and
receivers, where each transmit element radiates a different waveform.
Collocated
MIMO
radar systems exploit the waveform diversity, based on mutual orthogonality
of
the transmitted signals. MIMO radar exhibits several advantages with respect
to
traditional radar array systems in terms of flexibility and performance.
However,
MIMO
radar poses new challenges for both hardware design and digital processing.
In
particular, MIMO suffers from range-azimuth resolution trade-off between large
bandwidth
desired for high range resolution and small bandwidth for high azimuth
resolution.
In addition, achieving high azimuth resolution requires a large number of
transmit
and receive antennas. Moreover, the digital processing is performed on samples of
the received signal, from each transmitter to each receiver, at its Nyquist
rate, which can be prohibitively large when high resolution is needed.
In
this work, we adopt a frequency division multiple access (FDMA) approach to
avoid
the range-azimuth resolution conflict and show that, using FDMA the narrowband assumption
may be relaxed to the individual bandwidth. We can then combine large overall
total bandwidth for the sake of high range resolution and narrow individual bandwidth
to allow for a larger aperture and high azimuth resolution. In addition, we process
all channels jointly to overcome the FDMA range resolution limitation to a
single bandwidth, and address range-azimuth coupling using a random array
configuration.
Finally, overcoming the rate bottleneck, sub-Nyquist
sampling methods have been
proposed
that break the link between radar signal bandwidth and sampling rate. In
this
work, we extend these methods to MIMO configurations and propose a sub-Nyquist MIMO
radar (SUMMeR) system that performs both time and spatial compression. We present
a range-azimuth-Doppler recovery algorithm from sub-Nyquist samples obtained from
a reduced number of transmitters and receivers, that exploits the sparsity of
the recovered targets’ parameters. This allows us to achieve reduction in the
number of deployed antennas and the number of samples per receiver, without
degrading the time and spatial resolutions.
Our
enhanced range-azimuth resolution capabilities illustrated in simulations and
numerical
experiments show that our FDMA approach based on joint processing outperforms
the classic code division multiple access (CDMA). In addition, simulations illustrate
the detection performance of SUMMeR for different compression levels and
show
that both time and spatial resolution are preserved, with respect to classic
Nyquist MIMO configurations. We also examine the impact of design parameters,
such as antennas’ locations and carrier frequencies, on the detection
performance, and provide guidelines for their choice. Finally, we present a
hardware prototype of the SUMMeR system with capabilities of cognitive
transmission. The prototype allows both spatial and time compressive sampling.